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hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/inception-resnet-v2.mdx | # Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception architecture).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('inception_resnet_v2', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `inception_resnet_v2`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('inception_resnet_v2', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{szegedy2016inceptionv4,
title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning},
author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex Alemi},
year={2016},
eprint={1602.07261},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: Inception ResNet v2
Paper:
Title: Inception-v4, Inception-ResNet and the Impact of Residual Connections on
Learning
URL: https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact
Models:
- Name: inception_resnet_v2
In Collection: Inception ResNet v2
Metadata:
FLOPs: 16959133120
Parameters: 55850000
File Size: 223774238
Architecture:
- Average Pooling
- Dropout
- Inception-ResNet-v2 Reduction-B
- Inception-ResNet-v2-A
- Inception-ResNet-v2-B
- Inception-ResNet-v2-C
- Reduction-A
- Softmax
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 20x NVIDIA Kepler GPUs
ID: inception_resnet_v2
LR: 0.045
Dropout: 0.2
Crop Pct: '0.897'
Momentum: 0.9
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_resnet_v2.py#L343
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/inception_resnet_v2-940b1cd6.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 0.95%
Top 5 Accuracy: 17.29%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/inception-v3.mdx | # Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('inception_v3', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `inception_v3`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('inception_v3', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/SzegedyVISW15,
author = {Christian Szegedy and
Vincent Vanhoucke and
Sergey Ioffe and
Jonathon Shlens and
Zbigniew Wojna},
title = {Rethinking the Inception Architecture for Computer Vision},
journal = {CoRR},
volume = {abs/1512.00567},
year = {2015},
url = {http://arxiv.org/abs/1512.00567},
archivePrefix = {arXiv},
eprint = {1512.00567},
timestamp = {Mon, 13 Aug 2018 16:49:07 +0200},
biburl = {https://dblp.org/rec/journals/corr/SzegedyVISW15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: Inception v3
Paper:
Title: Rethinking the Inception Architecture for Computer Vision
URL: https://paperswithcode.com/paper/rethinking-the-inception-architecture-for
Models:
- Name: inception_v3
In Collection: Inception v3
Metadata:
FLOPs: 7352418880
Parameters: 23830000
File Size: 108857766
Architecture:
- 1x1 Convolution
- Auxiliary Classifier
- Average Pooling
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inception-v3 Module
- Max Pooling
- ReLU
- Softmax
Tasks:
- Image Classification
Training Techniques:
- Gradient Clipping
- Label Smoothing
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 50x NVIDIA Kepler GPUs
ID: inception_v3
LR: 0.045
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v3.py#L442
Weights: https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.46%
Top 5 Accuracy: 93.48%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/inception-v4.mdx | # Inception v4
**Inception-v4** is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than [Inception-v3](https://paperswithcode.com/method/inception-v3).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('inception_v4', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `inception_v4`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('inception_v4', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{szegedy2016inceptionv4,
title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning},
author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex Alemi},
year={2016},
eprint={1602.07261},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: Inception v4
Paper:
Title: Inception-v4, Inception-ResNet and the Impact of Residual Connections on
Learning
URL: https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact
Models:
- Name: inception_v4
In Collection: Inception v4
Metadata:
FLOPs: 15806527936
Parameters: 42680000
File Size: 171082495
Architecture:
- Average Pooling
- Dropout
- Inception-A
- Inception-B
- Inception-C
- Reduction-A
- Reduction-B
- Softmax
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 20x NVIDIA Kepler GPUs
ID: inception_v4
LR: 0.045
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v4.py#L313
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/inceptionv4-8e4777a0.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 1.01%
Top 5 Accuracy: 16.85%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/legacy-se-resnet.mdx | # (Legacy) SE-ResNet
**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('legacy_seresnet101', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `legacy_seresnet101`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('legacy_seresnet101', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{hu2019squeezeandexcitation,
title={Squeeze-and-Excitation Networks},
author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
year={2019},
eprint={1709.01507},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: Legacy SE ResNet
Paper:
Title: Squeeze-and-Excitation Networks
URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
Models:
- Name: legacy_seresnet101
In Collection: Legacy SE ResNet
Metadata:
FLOPs: 9762614000
Parameters: 49330000
File Size: 197822624
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_seresnet101
LR: 0.6
Epochs: 100
Layers: 101
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L426
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet101-7e38fcc6.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.38%
Top 5 Accuracy: 94.26%
- Name: legacy_seresnet152
In Collection: Legacy SE ResNet
Metadata:
FLOPs: 14553578160
Parameters: 66819999
File Size: 268033864
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_seresnet152
LR: 0.6
Epochs: 100
Layers: 152
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L433
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet152-d17c99b7.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.67%
Top 5 Accuracy: 94.38%
- Name: legacy_seresnet18
In Collection: Legacy SE ResNet
Metadata:
FLOPs: 2328876024
Parameters: 11780000
File Size: 47175663
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_seresnet18
LR: 0.6
Epochs: 100
Layers: 18
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L405
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet18-4bb0ce65.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 71.74%
Top 5 Accuracy: 90.34%
- Name: legacy_seresnet34
In Collection: Legacy SE ResNet
Metadata:
FLOPs: 4706201004
Parameters: 21960000
File Size: 87958697
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_seresnet34
LR: 0.6
Epochs: 100
Layers: 34
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L412
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet34-a4004e63.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 74.79%
Top 5 Accuracy: 92.13%
- Name: legacy_seresnet50
In Collection: Legacy SE ResNet
Metadata:
FLOPs: 4974351024
Parameters: 28090000
File Size: 112611220
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_seresnet50
LR: 0.6
Epochs: 100
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Interpolation: bilinear
Minibatch Size: 1024
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L419
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet50-ce0d4300.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.64%
Top 5 Accuracy: 93.74%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/legacy-se-resnext.mdx | # (Legacy) SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('legacy_seresnext101_32x4d', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `legacy_seresnext101_32x4d`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('legacy_seresnext101_32x4d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{hu2019squeezeandexcitation,
title={Squeeze-and-Excitation Networks},
author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
year={2019},
eprint={1709.01507},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: Legacy SE ResNeXt
Paper:
Title: Squeeze-and-Excitation Networks
URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
Models:
- Name: legacy_seresnext101_32x4d
In Collection: Legacy SE ResNeXt
Metadata:
FLOPs: 10287698672
Parameters: 48960000
File Size: 196466866
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_seresnext101_32x4d
LR: 0.6
Epochs: 100
Layers: 101
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L462
Weights: http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.23%
Top 5 Accuracy: 95.02%
- Name: legacy_seresnext26_32x4d
In Collection: Legacy SE ResNeXt
Metadata:
FLOPs: 3187342304
Parameters: 16790000
File Size: 67346327
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_seresnext26_32x4d
LR: 0.6
Epochs: 100
Layers: 26
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L448
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.11%
Top 5 Accuracy: 93.31%
- Name: legacy_seresnext50_32x4d
In Collection: Legacy SE ResNeXt
Metadata:
FLOPs: 5459954352
Parameters: 27560000
File Size: 110559176
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_seresnext50_32x4d
LR: 0.6
Epochs: 100
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L455
Weights: http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.08%
Top 5 Accuracy: 94.43%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/legacy-senet.mdx | # (Legacy) SENet
A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
The weights from this model were ported from Gluon.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('legacy_senet154', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `legacy_senet154`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('legacy_senet154', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{hu2019squeezeandexcitation,
title={Squeeze-and-Excitation Networks},
author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
year={2019},
eprint={1709.01507},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: Legacy SENet
Paper:
Title: Squeeze-and-Excitation Networks
URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
Models:
- Name: legacy_senet154
In Collection: Legacy SENet
Metadata:
FLOPs: 26659556016
Parameters: 115090000
File Size: 461488402
Architecture:
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_senet154
LR: 0.6
Epochs: 100
Layers: 154
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L440
Weights: http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.33%
Top 5 Accuracy: 95.51%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/mixnet.mdx | # MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('mixnet_l', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `mixnet_l`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('mixnet_l', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{tan2019mixconv,
title={MixConv: Mixed Depthwise Convolutional Kernels},
author={Mingxing Tan and Quoc V. Le},
year={2019},
eprint={1907.09595},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: MixNet
Paper:
Title: 'MixConv: Mixed Depthwise Convolutional Kernels'
URL: https://paperswithcode.com/paper/mixnet-mixed-depthwise-convolutional-kernels
Models:
- Name: mixnet_l
In Collection: MixNet
Metadata:
FLOPs: 738671316
Parameters: 7330000
File Size: 29608232
Architecture:
- Batch Normalization
- Dense Connections
- Dropout
- Global Average Pooling
- Grouped Convolution
- MixConv
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- MNAS
Training Data:
- ImageNet
ID: mixnet_l
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1669
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.98%
Top 5 Accuracy: 94.18%
- Name: mixnet_m
In Collection: MixNet
Metadata:
FLOPs: 454543374
Parameters: 5010000
File Size: 20298347
Architecture:
- Batch Normalization
- Dense Connections
- Dropout
- Global Average Pooling
- Grouped Convolution
- MixConv
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- MNAS
Training Data:
- ImageNet
ID: mixnet_m
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1660
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.27%
Top 5 Accuracy: 93.42%
- Name: mixnet_s
In Collection: MixNet
Metadata:
FLOPs: 321264910
Parameters: 4130000
File Size: 16727982
Architecture:
- Batch Normalization
- Dense Connections
- Dropout
- Global Average Pooling
- Grouped Convolution
- MixConv
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- MNAS
Training Data:
- ImageNet
ID: mixnet_s
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1651
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.99%
Top 5 Accuracy: 92.79%
- Name: mixnet_xl
In Collection: MixNet
Metadata:
FLOPs: 1195880424
Parameters: 11900000
File Size: 48001170
Architecture:
- Batch Normalization
- Dense Connections
- Dropout
- Global Average Pooling
- Grouped Convolution
- MixConv
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- MNAS
Training Data:
- ImageNet
ID: mixnet_xl
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1678
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.47%
Top 5 Accuracy: 94.93%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/mnasnet.mdx | # MnasNet
**MnasNet** is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. The main building block is an [inverted residual block](https://paperswithcode.com/method/inverted-residual-block) (from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2)).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('mnasnet_100', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `mnasnet_100`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('mnasnet_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{tan2019mnasnet,
title={MnasNet: Platform-Aware Neural Architecture Search for Mobile},
author={Mingxing Tan and Bo Chen and Ruoming Pang and Vijay Vasudevan and Mark Sandler and Andrew Howard and Quoc V. Le},
year={2019},
eprint={1807.11626},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: MNASNet
Paper:
Title: 'MnasNet: Platform-Aware Neural Architecture Search for Mobile'
URL: https://paperswithcode.com/paper/mnasnet-platform-aware-neural-architecture
Models:
- Name: mnasnet_100
In Collection: MNASNet
Metadata:
FLOPs: 416415488
Parameters: 4380000
File Size: 17731774
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Inverted Residual Block
- Max Pooling
- ReLU
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
ID: mnasnet_100
Layers: 100
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4000
Image Size: '224'
Interpolation: bicubic
RMSProp Decay: 0.9
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L894
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 74.67%
Top 5 Accuracy: 92.1%
- Name: semnasnet_100
In Collection: MNASNet
Metadata:
FLOPs: 414570766
Parameters: 3890000
File Size: 15731489
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Inverted Residual Block
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: semnasnet_100
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L928
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.45%
Top 5 Accuracy: 92.61%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/mobilenet-v2.mdx | # MobileNet v2
**MobileNetV2** is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an [inverted residual structure](https://paperswithcode.com/method/inverted-residual-block) where the residual connections are between the bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. As a whole, the architecture of MobileNetV2 contains the initial fully convolution layer with 32 filters, followed by 19 residual bottleneck layers.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('mobilenetv2_100', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `mobilenetv2_100`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('mobilenetv2_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/abs-1801-04381,
author = {Mark Sandler and
Andrew G. Howard and
Menglong Zhu and
Andrey Zhmoginov and
Liang{-}Chieh Chen},
title = {Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification,
Detection and Segmentation},
journal = {CoRR},
volume = {abs/1801.04381},
year = {2018},
url = {http://arxiv.org/abs/1801.04381},
archivePrefix = {arXiv},
eprint = {1801.04381},
timestamp = {Tue, 12 Jan 2021 15:30:06 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1801-04381.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: MobileNet V2
Paper:
Title: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks'
URL: https://paperswithcode.com/paper/mobilenetv2-inverted-residuals-and-linear
Models:
- Name: mobilenetv2_100
In Collection: MobileNet V2
Metadata:
FLOPs: 401920448
Parameters: 3500000
File Size: 14202571
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Depthwise Separable Convolution
- Dropout
- Inverted Residual Block
- Max Pooling
- ReLU6
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 16x GPUs
ID: mobilenetv2_100
LR: 0.045
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1536
Image Size: '224'
Weight Decay: 4.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L955
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_100_ra-b33bc2c4.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 72.95%
Top 5 Accuracy: 91.0%
- Name: mobilenetv2_110d
In Collection: MobileNet V2
Metadata:
FLOPs: 573958832
Parameters: 4520000
File Size: 18316431
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Depthwise Separable Convolution
- Dropout
- Inverted Residual Block
- Max Pooling
- ReLU6
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 16x GPUs
ID: mobilenetv2_110d
LR: 0.045
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1536
Image Size: '224'
Weight Decay: 4.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L969
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_110d_ra-77090ade.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.05%
Top 5 Accuracy: 92.19%
- Name: mobilenetv2_120d
In Collection: MobileNet V2
Metadata:
FLOPs: 888510048
Parameters: 5830000
File Size: 23651121
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Depthwise Separable Convolution
- Dropout
- Inverted Residual Block
- Max Pooling
- ReLU6
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 16x GPUs
ID: mobilenetv2_120d
LR: 0.045
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1536
Image Size: '224'
Weight Decay: 4.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L977
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_120d_ra-5987e2ed.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.28%
Top 5 Accuracy: 93.51%
- Name: mobilenetv2_140
In Collection: MobileNet V2
Metadata:
FLOPs: 770196784
Parameters: 6110000
File Size: 24673555
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Depthwise Separable Convolution
- Dropout
- Inverted Residual Block
- Max Pooling
- ReLU6
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 16x GPUs
ID: mobilenetv2_140
LR: 0.045
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1536
Image Size: '224'
Weight Decay: 4.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L962
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_140_ra-21a4e913.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.51%
Top 5 Accuracy: 93.0%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/mobilenet-v3.mdx | # MobileNet v3
**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-block) modules in the [MBConv blocks](https://paperswithcode.com/method/inverted-residual-block).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('mobilenetv3_large_100', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `mobilenetv3_large_100`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('mobilenetv3_large_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/abs-1905-02244,
author = {Andrew Howard and
Mark Sandler and
Grace Chu and
Liang{-}Chieh Chen and
Bo Chen and
Mingxing Tan and
Weijun Wang and
Yukun Zhu and
Ruoming Pang and
Vijay Vasudevan and
Quoc V. Le and
Hartwig Adam},
title = {Searching for MobileNetV3},
journal = {CoRR},
volume = {abs/1905.02244},
year = {2019},
url = {http://arxiv.org/abs/1905.02244},
archivePrefix = {arXiv},
eprint = {1905.02244},
timestamp = {Tue, 12 Jan 2021 15:30:06 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: MobileNet V3
Paper:
Title: Searching for MobileNetV3
URL: https://paperswithcode.com/paper/searching-for-mobilenetv3
Models:
- Name: mobilenetv3_large_100
In Collection: MobileNet V3
Metadata:
FLOPs: 287193752
Parameters: 5480000
File Size: 22076443
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Hard Swish
- Inverted Residual Block
- ReLU
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x4 TPU Pod
ID: mobilenetv3_large_100
LR: 0.1
Dropout: 0.8
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4096
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L363
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.77%
Top 5 Accuracy: 92.54%
- Name: mobilenetv3_rw
In Collection: MobileNet V3
Metadata:
FLOPs: 287190638
Parameters: 5480000
File Size: 22064048
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Hard Swish
- Inverted Residual Block
- ReLU
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x4 TPU Pod
ID: mobilenetv3_rw
LR: 0.1
Dropout: 0.8
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4096
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L384
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.62%
Top 5 Accuracy: 92.71%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/nasnet.mdx | # NASNet
**NASNet** is a type of convolutional neural network discovered through neural architecture search. The building blocks consist of normal and reduction cells.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('nasnetalarge', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `nasnetalarge`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('nasnetalarge', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{zoph2018learning,
title={Learning Transferable Architectures for Scalable Image Recognition},
author={Barret Zoph and Vijay Vasudevan and Jonathon Shlens and Quoc V. Le},
year={2018},
eprint={1707.07012},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: NASNet
Paper:
Title: Learning Transferable Architectures for Scalable Image Recognition
URL: https://paperswithcode.com/paper/learning-transferable-architectures-for
Models:
- Name: nasnetalarge
In Collection: NASNet
Metadata:
FLOPs: 30242402862
Parameters: 88750000
File Size: 356056626
Architecture:
- Average Pooling
- Batch Normalization
- Convolution
- Depthwise Separable Convolution
- Dropout
- ReLU
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 50x Tesla K40 GPUs
ID: nasnetalarge
Dropout: 0.5
Crop Pct: '0.911'
Momentum: 0.9
Image Size: '331'
Interpolation: bicubic
Label Smoothing: 0.1
RMSProp $\epsilon$: 1.0
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/nasnet.py#L562
Weights: http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.63%
Top 5 Accuracy: 96.05%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/noisy-student.mdx | # Noisy Student (EfficientNet)
**Noisy Student Training** is a semi-supervised learning approach. It extends the idea of self-training
and distillation with the use of equal-or-larger student models and noise added to the student during learning. It has three main steps:
1. train a teacher model on labeled images
2. use the teacher to generate pseudo labels on unlabeled images
3. train a student model on the combination of labeled images and pseudo labeled images.
The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student.
Noisy Student Training seeks to improve on self-training and distillation in two ways. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Second, it adds noise to the student so the noised student is forced to learn harder from the pseudo labels. To noise the student, it uses input noise such as RandAugment data augmentation, and model noise such as dropout and stochastic depth during training.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('tf_efficientnet_b0_ns', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `tf_efficientnet_b0_ns`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('tf_efficientnet_b0_ns', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{xie2020selftraining,
title={Self-training with Noisy Student improves ImageNet classification},
author={Qizhe Xie and Minh-Thang Luong and Eduard Hovy and Quoc V. Le},
year={2020},
eprint={1911.04252},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
Type: model-index
Collections:
- Name: Noisy Student
Paper:
Title: Self-training with Noisy Student improves ImageNet classification
URL: https://paperswithcode.com/paper/self-training-with-noisy-student-improves
Models:
- Name: tf_efficientnet_b0_ns
In Collection: Noisy Student
Metadata:
FLOPs: 488688572
Parameters: 5290000
File Size: 21386709
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPU v3 Pod
ID: tf_efficientnet_b0_ns
LR: 0.128
Epochs: 700
Dropout: 0.5
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 2048
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Stochastic Depth Survival: 0.8
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1427
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ns-c0e6a31c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.66%
Top 5 Accuracy: 94.37%
- Name: tf_efficientnet_b1_ns
In Collection: Noisy Student
Metadata:
FLOPs: 883633200
Parameters: 7790000
File Size: 31516408
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPU v3 Pod
ID: tf_efficientnet_b1_ns
LR: 0.128
Epochs: 700
Dropout: 0.5
Crop Pct: '0.882'
Momentum: 0.9
Batch Size: 2048
Image Size: '240'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Stochastic Depth Survival: 0.8
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1437
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ns-99dd0c41.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.39%
Top 5 Accuracy: 95.74%
- Name: tf_efficientnet_b2_ns
In Collection: Noisy Student
Metadata:
FLOPs: 1234321170
Parameters: 9110000
File Size: 36801803
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPU v3 Pod
ID: tf_efficientnet_b2_ns
LR: 0.128
Epochs: 700
Dropout: 0.5
Crop Pct: '0.89'
Momentum: 0.9
Batch Size: 2048
Image Size: '260'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Stochastic Depth Survival: 0.8
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1447
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ns-00306e48.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.39%
Top 5 Accuracy: 96.24%
- Name: tf_efficientnet_b3_ns
In Collection: Noisy Student
Metadata:
FLOPs: 2275247568
Parameters: 12230000
File Size: 49385734
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPU v3 Pod
ID: tf_efficientnet_b3_ns
LR: 0.128
Epochs: 700
Dropout: 0.5
Crop Pct: '0.904'
Momentum: 0.9
Batch Size: 2048
Image Size: '300'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Stochastic Depth Survival: 0.8
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1457
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 84.04%
Top 5 Accuracy: 96.91%
- Name: tf_efficientnet_b4_ns
In Collection: Noisy Student
Metadata:
FLOPs: 5749638672
Parameters: 19340000
File Size: 77995057
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPU v3 Pod
ID: tf_efficientnet_b4_ns
LR: 0.128
Epochs: 700
Dropout: 0.5
Crop Pct: '0.922'
Momentum: 0.9
Batch Size: 2048
Image Size: '380'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Stochastic Depth Survival: 0.8
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1467
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ns-d6313a46.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 85.15%
Top 5 Accuracy: 97.47%
- Name: tf_efficientnet_b5_ns
In Collection: Noisy Student
Metadata:
FLOPs: 13176501888
Parameters: 30390000
File Size: 122404944
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPU v3 Pod
ID: tf_efficientnet_b5_ns
LR: 0.128
Epochs: 350
Dropout: 0.5
Crop Pct: '0.934'
Momentum: 0.9
Batch Size: 2048
Image Size: '456'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Stochastic Depth Survival: 0.8
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1477
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ns-6f26d0cf.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 86.08%
Top 5 Accuracy: 97.75%
- Name: tf_efficientnet_b6_ns
In Collection: Noisy Student
Metadata:
FLOPs: 24180518488
Parameters: 43040000
File Size: 173239537
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPU v3 Pod
ID: tf_efficientnet_b6_ns
LR: 0.128
Epochs: 350
Dropout: 0.5
Crop Pct: '0.942'
Momentum: 0.9
Batch Size: 2048
Image Size: '528'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Stochastic Depth Survival: 0.8
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1487
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ns-51548356.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 86.45%
Top 5 Accuracy: 97.88%
- Name: tf_efficientnet_b7_ns
In Collection: Noisy Student
Metadata:
FLOPs: 48205304880
Parameters: 66349999
File Size: 266853140
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPU v3 Pod
ID: tf_efficientnet_b7_ns
LR: 0.128
Epochs: 350
Dropout: 0.5
Crop Pct: '0.949'
Momentum: 0.9
Batch Size: 2048
Image Size: '600'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Stochastic Depth Survival: 0.8
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1498
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 86.83%
Top 5 Accuracy: 98.08%
- Name: tf_efficientnet_l2_ns
In Collection: Noisy Student
Metadata:
FLOPs: 611646113804
Parameters: 480310000
File Size: 1925950424
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPU v3 Pod
Training Time: 6 days
ID: tf_efficientnet_l2_ns
LR: 0.128
Epochs: 350
Dropout: 0.5
Crop Pct: '0.96'
Momentum: 0.9
Batch Size: 2048
Image Size: '800'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Stochastic Depth Survival: 0.8
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1520
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 88.35%
Top 5 Accuracy: 98.66%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/pnasnet.mdx | # PNASNet
**Progressive Neural Architecture Search**, or **PNAS**, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to complex ones, pruning out unpromising structures as we go.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('pnasnet5large', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `pnasnet5large`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('pnasnet5large', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{liu2018progressive,
title={Progressive Neural Architecture Search},
author={Chenxi Liu and Barret Zoph and Maxim Neumann and Jonathon Shlens and Wei Hua and Li-Jia Li and Li Fei-Fei and Alan Yuille and Jonathan Huang and Kevin Murphy},
year={2018},
eprint={1712.00559},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: PNASNet
Paper:
Title: Progressive Neural Architecture Search
URL: https://paperswithcode.com/paper/progressive-neural-architecture-search
Models:
- Name: pnasnet5large
In Collection: PNASNet
Metadata:
FLOPs: 31458865950
Parameters: 86060000
File Size: 345153926
Architecture:
- Average Pooling
- Batch Normalization
- Convolution
- Depthwise Separable Convolution
- Dropout
- ReLU
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 100x NVIDIA P100 GPUs
ID: pnasnet5large
LR: 0.015
Dropout: 0.5
Crop Pct: '0.911'
Momentum: 0.9
Batch Size: 1600
Image Size: '331'
Interpolation: bicubic
Label Smoothing: 0.1
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/pnasnet.py#L343
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/pnasnet5large-bf079911.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 0.98%
Top 5 Accuracy: 18.58%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/regnetx.mdx | # RegNetX
**RegNetX** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
$$ u\_{j} = w\_{0} + w\_{a}\cdot{j} $$
For **RegNetX** we have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \leq d \leq 28$, and $w\_{m} \geq 2$ (the width multiplier).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('regnetx_002', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `regnetx_002`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('regnetx_002', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{radosavovic2020designing,
title={Designing Network Design Spaces},
author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
year={2020},
eprint={2003.13678},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: RegNetX
Paper:
Title: Designing Network Design Spaces
URL: https://paperswithcode.com/paper/designing-network-design-spaces
Models:
- Name: regnetx_002
In Collection: RegNetX
Metadata:
FLOPs: 255276032
Parameters: 2680000
File Size: 10862199
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_002
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L337
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 68.75%
Top 5 Accuracy: 88.56%
- Name: regnetx_004
In Collection: RegNetX
Metadata:
FLOPs: 510619136
Parameters: 5160000
File Size: 20841309
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_004
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L343
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 72.39%
Top 5 Accuracy: 90.82%
- Name: regnetx_006
In Collection: RegNetX
Metadata:
FLOPs: 771659136
Parameters: 6200000
File Size: 24965172
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_006
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L349
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 73.84%
Top 5 Accuracy: 91.68%
- Name: regnetx_008
In Collection: RegNetX
Metadata:
FLOPs: 1027038208
Parameters: 7260000
File Size: 29235944
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_008
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L355
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.05%
Top 5 Accuracy: 92.34%
- Name: regnetx_016
In Collection: RegNetX
Metadata:
FLOPs: 2059337856
Parameters: 9190000
File Size: 36988158
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_016
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L361
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.95%
Top 5 Accuracy: 93.43%
- Name: regnetx_032
In Collection: RegNetX
Metadata:
FLOPs: 4082555904
Parameters: 15300000
File Size: 61509573
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_032
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L367
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.15%
Top 5 Accuracy: 94.09%
- Name: regnetx_040
In Collection: RegNetX
Metadata:
FLOPs: 5095167744
Parameters: 22120000
File Size: 88844824
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_040
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L373
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.48%
Top 5 Accuracy: 94.25%
- Name: regnetx_064
In Collection: RegNetX
Metadata:
FLOPs: 8303405824
Parameters: 26210000
File Size: 105184854
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_064
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L379
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.06%
Top 5 Accuracy: 94.47%
- Name: regnetx_080
In Collection: RegNetX
Metadata:
FLOPs: 10276726784
Parameters: 39570000
File Size: 158720042
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_080
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L385
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.21%
Top 5 Accuracy: 94.55%
- Name: regnetx_120
In Collection: RegNetX
Metadata:
FLOPs: 15536378368
Parameters: 46110000
File Size: 184866342
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_120
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L391
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.61%
Top 5 Accuracy: 94.73%
- Name: regnetx_160
In Collection: RegNetX
Metadata:
FLOPs: 20491740672
Parameters: 54280000
File Size: 217623862
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_160
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L397
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.84%
Top 5 Accuracy: 94.82%
- Name: regnetx_320
In Collection: RegNetX
Metadata:
FLOPs: 40798958592
Parameters: 107810000
File Size: 431962133
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnetx_320
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L403
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.25%
Top 5 Accuracy: 95.03%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/regnety.mdx | # RegNetY
**RegNetY** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\_{0} > 0$, and slope $w\_{a} > 0$, and generates a different block width $u\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):
$$ u\_{j} = w\_{0} + w\_{a}\cdot{j} $$
For **RegNetX** authors have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \leq d \leq 28$, and $w\_{m} \geq 2$ (the width multiplier).
For **RegNetY** authors make one change, which is to include [Squeeze-and-Excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('regnety_002', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `regnety_002`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('regnety_002', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{radosavovic2020designing,
title={Designing Network Design Spaces},
author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
year={2020},
eprint={2003.13678},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: RegNetY
Paper:
Title: Designing Network Design Spaces
URL: https://paperswithcode.com/paper/designing-network-design-spaces
Models:
- Name: regnety_002
In Collection: RegNetY
Metadata:
FLOPs: 255754236
Parameters: 3160000
File Size: 12782926
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_002
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L409
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 70.28%
Top 5 Accuracy: 89.55%
- Name: regnety_004
In Collection: RegNetY
Metadata:
FLOPs: 515664568
Parameters: 4340000
File Size: 17542753
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_004
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L415
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 74.02%
Top 5 Accuracy: 91.76%
- Name: regnety_006
In Collection: RegNetY
Metadata:
FLOPs: 771746928
Parameters: 6060000
File Size: 24394127
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_006
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L421
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.27%
Top 5 Accuracy: 92.53%
- Name: regnety_008
In Collection: RegNetY
Metadata:
FLOPs: 1023448952
Parameters: 6260000
File Size: 25223268
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_008
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L427
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.32%
Top 5 Accuracy: 93.07%
- Name: regnety_016
In Collection: RegNetY
Metadata:
FLOPs: 2070895094
Parameters: 11200000
File Size: 45115589
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_016
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L433
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.87%
Top 5 Accuracy: 93.73%
- Name: regnety_032
In Collection: RegNetY
Metadata:
FLOPs: 4081118714
Parameters: 19440000
File Size: 78084523
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_032
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L439
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.01%
Top 5 Accuracy: 95.91%
- Name: regnety_040
In Collection: RegNetY
Metadata:
FLOPs: 5105933432
Parameters: 20650000
File Size: 82913909
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_040
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L445
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.23%
Top 5 Accuracy: 94.64%
- Name: regnety_064
In Collection: RegNetY
Metadata:
FLOPs: 8167730444
Parameters: 30580000
File Size: 122751416
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_064
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L451
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.73%
Top 5 Accuracy: 94.76%
- Name: regnety_080
In Collection: RegNetY
Metadata:
FLOPs: 10233621420
Parameters: 39180000
File Size: 157124671
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_080
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L457
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.87%
Top 5 Accuracy: 94.83%
- Name: regnety_120
In Collection: RegNetY
Metadata:
FLOPs: 15542094856
Parameters: 51820000
File Size: 207743949
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_120
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L463
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.38%
Top 5 Accuracy: 95.12%
- Name: regnety_160
In Collection: RegNetY
Metadata:
FLOPs: 20450196852
Parameters: 83590000
File Size: 334916722
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_160
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L469
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.28%
Top 5 Accuracy: 94.97%
- Name: regnety_320
In Collection: RegNetY
Metadata:
FLOPs: 41492618394
Parameters: 145050000
File Size: 580891965
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- ReLU
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA V100 GPUs
ID: regnety_320
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 5.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/regnet.py#L475
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.8%
Top 5 Accuracy: 95.25%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/res2net.mdx | # Res2Net
**Res2Net** is an image model that employs a variation on bottleneck residual blocks, [Res2Net Blocks](https://paperswithcode.com/method/res2net-block). The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('res2net101_26w_4s', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `res2net101_26w_4s`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('res2net101_26w_4s', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{Gao_2021,
title={Res2Net: A New Multi-Scale Backbone Architecture},
volume={43},
ISSN={1939-3539},
url={http://dx.doi.org/10.1109/TPAMI.2019.2938758},
DOI={10.1109/tpami.2019.2938758},
number={2},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
year={2021},
month={Feb},
pages={652–662}
}
```
<!--
Type: model-index
Collections:
- Name: Res2Net
Paper:
Title: 'Res2Net: A New Multi-scale Backbone Architecture'
URL: https://paperswithcode.com/paper/res2net-a-new-multi-scale-backbone
Models:
- Name: res2net101_26w_4s
In Collection: Res2Net
Metadata:
FLOPs: 10415881200
Parameters: 45210000
File Size: 181456059
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net101_26w_4s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L152
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net101_26w_4s-02a759a1.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.19%
Top 5 Accuracy: 94.43%
- Name: res2net50_14w_8s
In Collection: Res2Net
Metadata:
FLOPs: 5403546768
Parameters: 25060000
File Size: 100638543
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_14w_8s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L196
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_14w_8s-6527dddc.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.14%
Top 5 Accuracy: 93.86%
- Name: res2net50_26w_4s
In Collection: Res2Net
Metadata:
FLOPs: 5499974064
Parameters: 25700000
File Size: 103110087
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_4s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L141
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_4s-06e79181.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.99%
Top 5 Accuracy: 93.85%
- Name: res2net50_26w_6s
In Collection: Res2Net
Metadata:
FLOPs: 8130156528
Parameters: 37050000
File Size: 148603239
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_6s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L163
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_6s-19041792.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.57%
Top 5 Accuracy: 94.12%
- Name: res2net50_26w_8s
In Collection: Res2Net
Metadata:
FLOPs: 10760338992
Parameters: 48400000
File Size: 194085165
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_8s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L174
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_8s-2c7c9f12.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.19%
Top 5 Accuracy: 94.37%
- Name: res2net50_48w_2s
In Collection: Res2Net
Metadata:
FLOPs: 5375291520
Parameters: 25290000
File Size: 101421406
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_48w_2s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L185
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_48w_2s-afed724a.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.53%
Top 5 Accuracy: 93.56%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/res2next.mdx | # Res2NeXt
**Res2NeXt** is an image model that employs a variation on [ResNeXt](https://paperswithcode.com/method/resnext) bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('res2next50', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `res2next50`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('res2next50', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{Gao_2021,
title={Res2Net: A New Multi-Scale Backbone Architecture},
volume={43},
ISSN={1939-3539},
url={http://dx.doi.org/10.1109/TPAMI.2019.2938758},
DOI={10.1109/tpami.2019.2938758},
number={2},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
year={2021},
month={Feb},
pages={652–662}
}
```
<!--
Type: model-index
Collections:
- Name: Res2NeXt
Paper:
Title: 'Res2Net: A New Multi-scale Backbone Architecture'
URL: https://paperswithcode.com/paper/res2net-a-new-multi-scale-backbone
Models:
- Name: res2next50
In Collection: Res2NeXt
Metadata:
FLOPs: 5396798208
Parameters: 24670000
File Size: 99019592
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2NeXt Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2next50
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L207
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next50_4s-6ef7e7bf.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.24%
Top 5 Accuracy: 93.91%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/resnest.mdx | # ResNeSt
A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: $V = \text{Concat}${$V^{1},V^{2},\cdots{V}^{K}$}. As in standard residual blocks, the final output $Y$ of otheur Split-Attention block is produced using a shortcut connection: $Y=V+X$, if the input and output feature-map share the same shape. For blocks with a stride, an appropriate transformation $\mathcal{T}$ is applied to the shortcut connection to align the output shapes: $Y=V+\mathcal{T}(X)$. For example, $\mathcal{T}$ can be strided convolution or combined convolution-with-pooling.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('resnest101e', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `resnest101e`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('resnest101e', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{zhang2020resnest,
title={ResNeSt: Split-Attention Networks},
author={Hang Zhang and Chongruo Wu and Zhongyue Zhang and Yi Zhu and Haibin Lin and Zhi Zhang and Yue Sun and Tong He and Jonas Mueller and R. Manmatha and Mu Li and Alexander Smola},
year={2020},
eprint={2004.08955},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: ResNeSt
Paper:
Title: 'ResNeSt: Split-Attention Networks'
URL: https://paperswithcode.com/paper/resnest-split-attention-networks
Models:
- Name: resnest101e
In Collection: ResNeSt
Metadata:
FLOPs: 17423183648
Parameters: 48280000
File Size: 193782911
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest101e
LR: 0.1
Epochs: 270
Layers: 101
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4096
Image Size: '256'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L182
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest101-22405ba7.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.88%
Top 5 Accuracy: 96.31%
- Name: resnest14d
In Collection: ResNeSt
Metadata:
FLOPs: 3548594464
Parameters: 10610000
File Size: 42562639
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest14d
LR: 0.1
Epochs: 270
Layers: 14
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 8192
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L148
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest14-9c8fe254.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.51%
Top 5 Accuracy: 92.52%
- Name: resnest200e
In Collection: ResNeSt
Metadata:
FLOPs: 45954387872
Parameters: 70200000
File Size: 193782911
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest200e
LR: 0.1
Epochs: 270
Layers: 200
Dropout: 0.2
Crop Pct: '0.909'
Momentum: 0.9
Batch Size: 2048
Image Size: '320'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L194
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest101-22405ba7.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.85%
Top 5 Accuracy: 96.89%
- Name: resnest269e
In Collection: ResNeSt
Metadata:
FLOPs: 100830307104
Parameters: 110930000
File Size: 445402691
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest269e
LR: 0.1
Epochs: 270
Layers: 269
Dropout: 0.2
Crop Pct: '0.928'
Momentum: 0.9
Batch Size: 2048
Image Size: '416'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L206
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest269-0cc87c48.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 84.53%
Top 5 Accuracy: 96.99%
- Name: resnest26d
In Collection: ResNeSt
Metadata:
FLOPs: 4678918720
Parameters: 17070000
File Size: 68470242
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest26d
LR: 0.1
Epochs: 270
Layers: 26
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 8192
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L159
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest26-50eb607c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.48%
Top 5 Accuracy: 94.3%
- Name: resnest50d
In Collection: ResNeSt
Metadata:
FLOPs: 6937106336
Parameters: 27480000
File Size: 110273258
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest50d
LR: 0.1
Epochs: 270
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 8192
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L170
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50-528c19ca.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.96%
Top 5 Accuracy: 95.38%
- Name: resnest50d_1s4x24d
In Collection: ResNeSt
Metadata:
FLOPs: 5686764544
Parameters: 25680000
File Size: 103045531
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest50d_1s4x24d
LR: 0.1
Epochs: 270
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 8192
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L229
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_1s4x24d-d4a4f76f.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.0%
Top 5 Accuracy: 95.33%
- Name: resnest50d_4s2x40d
In Collection: ResNeSt
Metadata:
FLOPs: 5657064720
Parameters: 30420000
File Size: 122133282
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest50d_4s2x40d
LR: 0.1
Epochs: 270
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 8192
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L218
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_4s2x40d-41d14ed0.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.11%
Top 5 Accuracy: 95.55%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/resnet-d.mdx | # ResNet-D
**ResNet-D** is a modification on the [ResNet](https://paperswithcode.com/method/resnet) architecture that utilises an [average pooling](https://paperswithcode.com/method/average-pooling) tweak for downsampling. The motivation is that in the unmodified ResNet, the [1×1 convolution](https://paperswithcode.com/method/1x1-convolution) for the downsampling block ignores 3/4 of input feature maps, so this is modified so no information will be ignored
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('resnet101d', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `resnet101d`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('resnet101d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{he2018bag,
title={Bag of Tricks for Image Classification with Convolutional Neural Networks},
author={Tong He and Zhi Zhang and Hang Zhang and Zhongyue Zhang and Junyuan Xie and Mu Li},
year={2018},
eprint={1812.01187},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: ResNet-D
Paper:
Title: Bag of Tricks for Image Classification with Convolutional Neural Networks
URL: https://paperswithcode.com/paper/bag-of-tricks-for-image-classification-with
Models:
- Name: resnet101d
In Collection: ResNet-D
Metadata:
FLOPs: 13805639680
Parameters: 44570000
File Size: 178791263
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet101d
Crop Pct: '0.94'
Image Size: '256'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L716
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.31%
Top 5 Accuracy: 96.06%
- Name: resnet152d
In Collection: ResNet-D
Metadata:
FLOPs: 20155275264
Parameters: 60210000
File Size: 241596837
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet152d
Crop Pct: '0.94'
Image Size: '256'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L724
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.13%
Top 5 Accuracy: 96.35%
- Name: resnet18d
In Collection: ResNet-D
Metadata:
FLOPs: 2645205760
Parameters: 11710000
File Size: 46893231
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet18d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L649
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet18d_ra2-48a79e06.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 72.27%
Top 5 Accuracy: 90.69%
- Name: resnet200d
In Collection: ResNet-D
Metadata:
FLOPs: 26034378752
Parameters: 64690000
File Size: 259662933
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet200d
Crop Pct: '0.94'
Image Size: '256'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L749
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.24%
Top 5 Accuracy: 96.49%
- Name: resnet26d
In Collection: ResNet-D
Metadata:
FLOPs: 3335276032
Parameters: 16010000
File Size: 64209122
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet26d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L683
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26d-69e92c46.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.69%
Top 5 Accuracy: 93.15%
- Name: resnet34d
In Collection: ResNet-D
Metadata:
FLOPs: 5026601728
Parameters: 21820000
File Size: 87369807
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet34d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L666
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34d_ra2-f8dcfcaf.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.11%
Top 5 Accuracy: 93.38%
- Name: resnet50d
In Collection: ResNet-D
Metadata:
FLOPs: 5591002624
Parameters: 25580000
File Size: 102567109
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet50d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L699
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50d_ra2-464e36ba.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.55%
Top 5 Accuracy: 95.16%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/resnet.mdx | # ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('resnet18', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `resnet18`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('resnet18', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/HeZRS15,
author = {Kaiming He and
Xiangyu Zhang and
Shaoqing Ren and
Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {CoRR},
volume = {abs/1512.03385},
year = {2015},
url = {http://arxiv.org/abs/1512.03385},
archivePrefix = {arXiv},
eprint = {1512.03385},
timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: ResNet
Paper:
Title: Deep Residual Learning for Image Recognition
URL: https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition
Models:
- Name: resnet18
In Collection: ResNet
Metadata:
FLOPs: 2337073152
Parameters: 11690000
File Size: 46827520
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet18
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L641
Weights: https://download.pytorch.org/models/resnet18-5c106cde.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 69.74%
Top 5 Accuracy: 89.09%
- Name: resnet26
In Collection: ResNet
Metadata:
FLOPs: 3026804736
Parameters: 16000000
File Size: 64129972
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet26
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L675
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26-9aa10e23.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.29%
Top 5 Accuracy: 92.57%
- Name: resnet34
In Collection: ResNet
Metadata:
FLOPs: 4718469120
Parameters: 21800000
File Size: 87290831
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet34
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L658
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.11%
Top 5 Accuracy: 92.28%
- Name: resnet50
In Collection: ResNet
Metadata:
FLOPs: 5282531328
Parameters: 25560000
File Size: 102488165
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnet50
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L691
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50_ram-a26f946b.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.04%
Top 5 Accuracy: 94.39%
- Name: resnetblur50
In Collection: ResNet
Metadata:
FLOPs: 6621606912
Parameters: 25560000
File Size: 102488165
Architecture:
- 1x1 Convolution
- Batch Normalization
- Blur Pooling
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnetblur50
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L1160
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnetblur50-84f4748f.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.29%
Top 5 Accuracy: 94.64%
- Name: tv_resnet101
In Collection: ResNet
Metadata:
FLOPs: 10068547584
Parameters: 44550000
File Size: 178728960
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: tv_resnet101
LR: 0.1
Epochs: 90
Crop Pct: '0.875'
LR Gamma: 0.1
Momentum: 0.9
Batch Size: 32
Image Size: '224'
LR Step Size: 30
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L761
Weights: https://download.pytorch.org/models/resnet101-5d3b4d8f.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.37%
Top 5 Accuracy: 93.56%
- Name: tv_resnet152
In Collection: ResNet
Metadata:
FLOPs: 14857660416
Parameters: 60190000
File Size: 241530880
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: tv_resnet152
LR: 0.1
Epochs: 90
Crop Pct: '0.875'
LR Gamma: 0.1
Momentum: 0.9
Batch Size: 32
Image Size: '224'
LR Step Size: 30
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L769
Weights: https://download.pytorch.org/models/resnet152-b121ed2d.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.32%
Top 5 Accuracy: 94.05%
- Name: tv_resnet34
In Collection: ResNet
Metadata:
FLOPs: 4718469120
Parameters: 21800000
File Size: 87306240
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: tv_resnet34
LR: 0.1
Epochs: 90
Crop Pct: '0.875'
LR Gamma: 0.1
Momentum: 0.9
Batch Size: 32
Image Size: '224'
LR Step Size: 30
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L745
Weights: https://download.pytorch.org/models/resnet34-333f7ec4.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 73.3%
Top 5 Accuracy: 91.42%
- Name: tv_resnet50
In Collection: ResNet
Metadata:
FLOPs: 5282531328
Parameters: 25560000
File Size: 102502400
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: tv_resnet50
LR: 0.1
Epochs: 90
Crop Pct: '0.875'
LR Gamma: 0.1
Momentum: 0.9
Batch Size: 32
Image Size: '224'
LR Step Size: 30
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L753
Weights: https://download.pytorch.org/models/resnet50-19c8e357.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.16%
Top 5 Accuracy: 92.88%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/resnext.mdx | # ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('resnext101_32x8d', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `resnext101_32x8d`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('resnext101_32x8d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/XieGDTH16,
author = {Saining Xie and
Ross B. Girshick and
Piotr Doll{\'{a}}r and
Zhuowen Tu and
Kaiming He},
title = {Aggregated Residual Transformations for Deep Neural Networks},
journal = {CoRR},
volume = {abs/1611.05431},
year = {2016},
url = {http://arxiv.org/abs/1611.05431},
archivePrefix = {arXiv},
eprint = {1611.05431},
timestamp = {Mon, 13 Aug 2018 16:45:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/XieGDTH16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: ResNeXt
Paper:
Title: Aggregated Residual Transformations for Deep Neural Networks
URL: https://paperswithcode.com/paper/aggregated-residual-transformations-for-deep
Models:
- Name: resnext101_32x8d
In Collection: ResNeXt
Metadata:
FLOPs: 21180417024
Parameters: 88790000
File Size: 356082095
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnext101_32x8d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L877
Weights: https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.3%
Top 5 Accuracy: 94.53%
- Name: resnext50_32x4d
In Collection: ResNeXt
Metadata:
FLOPs: 5472648192
Parameters: 25030000
File Size: 100435887
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnext50_32x4d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L851
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50_32x4d_ra-d733960d.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.79%
Top 5 Accuracy: 94.61%
- Name: resnext50d_32x4d
In Collection: ResNeXt
Metadata:
FLOPs: 5781119488
Parameters: 25050000
File Size: 100515304
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: resnext50d_32x4d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L869
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50d_32x4d-103e99f8.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.67%
Top 5 Accuracy: 94.87%
- Name: tv_resnext50_32x4d
In Collection: ResNeXt
Metadata:
FLOPs: 5472648192
Parameters: 25030000
File Size: 100441675
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: tv_resnext50_32x4d
LR: 0.1
Epochs: 90
Crop Pct: '0.875'
LR Gamma: 0.1
Momentum: 0.9
Batch Size: 32
Image Size: '224'
LR Step Size: 30
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L842
Weights: https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.61%
Top 5 Accuracy: 93.68%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/rexnet.mdx | # RexNet
**Rank Expansion Networks** (ReXNets) follow a set of new design principles for designing bottlenecks in image classification models. Authors refine each layer by 1) expanding the input channel size of the convolution layer and 2) replacing the [ReLU6s](https://www.paperswithcode.com/method/relu6).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('rexnet_100', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `rexnet_100`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('rexnet_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{han2020rexnet,
title={ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network},
author={Dongyoon Han and Sangdoo Yun and Byeongho Heo and YoungJoon Yoo},
year={2020},
eprint={2007.00992},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: RexNet
Paper:
Title: 'ReXNet: Diminishing Representational Bottleneck on Convolutional Neural
Network'
URL: https://paperswithcode.com/paper/rexnet-diminishing-representational
Models:
- Name: rexnet_100
In Collection: RexNet
Metadata:
FLOPs: 509989377
Parameters: 4800000
File Size: 19417552
Architecture:
- Batch Normalization
- Convolution
- Dropout
- ReLU6
- Residual Connection
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- Linear Warmup With Cosine Annealing
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x NVIDIA V100 GPUs
ID: rexnet_100
LR: 0.5
Epochs: 400
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
Label Smoothing: 0.1
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L212
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_100-1b4dddf4.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.86%
Top 5 Accuracy: 93.88%
- Name: rexnet_130
In Collection: RexNet
Metadata:
FLOPs: 848364461
Parameters: 7560000
File Size: 30508197
Architecture:
- Batch Normalization
- Convolution
- Dropout
- ReLU6
- Residual Connection
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- Linear Warmup With Cosine Annealing
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x NVIDIA V100 GPUs
ID: rexnet_130
LR: 0.5
Epochs: 400
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
Label Smoothing: 0.1
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L218
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_130-590d768e.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.49%
Top 5 Accuracy: 94.67%
- Name: rexnet_150
In Collection: RexNet
Metadata:
FLOPs: 1122374469
Parameters: 9730000
File Size: 39227315
Architecture:
- Batch Normalization
- Convolution
- Dropout
- ReLU6
- Residual Connection
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- Linear Warmup With Cosine Annealing
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x NVIDIA V100 GPUs
ID: rexnet_150
LR: 0.5
Epochs: 400
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
Label Smoothing: 0.1
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L224
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_150-bd1a6aa8.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.31%
Top 5 Accuracy: 95.16%
- Name: rexnet_200
In Collection: RexNet
Metadata:
FLOPs: 1960224938
Parameters: 16370000
File Size: 65862221
Architecture:
- Batch Normalization
- Convolution
- Dropout
- ReLU6
- Residual Connection
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- Linear Warmup With Cosine Annealing
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x NVIDIA V100 GPUs
ID: rexnet_200
LR: 0.5
Epochs: 400
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
Label Smoothing: 0.1
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L230
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_200-8c0b7f2d.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.63%
Top 5 Accuracy: 95.67%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/se-resnet.mdx | # SE-ResNet
**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('seresnet152d', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `seresnet152d`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('seresnet152d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{hu2019squeezeandexcitation,
title={Squeeze-and-Excitation Networks},
author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
year={2019},
eprint={1709.01507},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: SE ResNet
Paper:
Title: Squeeze-and-Excitation Networks
URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
Models:
- Name: seresnet152d
In Collection: SE ResNet
Metadata:
FLOPs: 20161904304
Parameters: 66840000
File Size: 268144497
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: seresnet152d
LR: 0.6
Epochs: 100
Layers: 152
Dropout: 0.2
Crop Pct: '0.94'
Momentum: 0.9
Batch Size: 1024
Image Size: '256'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1206
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.74%
Top 5 Accuracy: 96.77%
- Name: seresnet50
In Collection: SE ResNet
Metadata:
FLOPs: 5285062320
Parameters: 28090000
File Size: 112621903
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: seresnet50
LR: 0.6
Epochs: 100
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1180
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet50_ra_224-8efdb4bb.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.26%
Top 5 Accuracy: 95.07%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/selecsls.mdx | # SelecSLS
**SelecSLS** uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('selecsls42b', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `selecsls42b`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('selecsls42b', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{Mehta_2020,
title={XNect},
volume={39},
ISSN={1557-7368},
url={http://dx.doi.org/10.1145/3386569.3392410},
DOI={10.1145/3386569.3392410},
number={4},
journal={ACM Transactions on Graphics},
publisher={Association for Computing Machinery (ACM)},
author={Mehta, Dushyant and Sotnychenko, Oleksandr and Mueller, Franziska and Xu, Weipeng and Elgharib, Mohamed and Fua, Pascal and Seidel, Hans-Peter and Rhodin, Helge and Pons-Moll, Gerard and Theobalt, Christian},
year={2020},
month={Jul}
}
```
<!--
Type: model-index
Collections:
- Name: SelecSLS
Paper:
Title: 'XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera'
URL: https://paperswithcode.com/paper/xnect-real-time-multi-person-3d-human-pose
Models:
- Name: selecsls42b
In Collection: SelecSLS
Metadata:
FLOPs: 3824022528
Parameters: 32460000
File Size: 129948954
Architecture:
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Global Average Pooling
- ReLU
- SelecSLS Block
Tasks:
- Image Classification
Training Techniques:
- Cosine Annealing
- Random Erasing
Training Data:
- ImageNet
ID: selecsls42b
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L335
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls42b-8af30141.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.18%
Top 5 Accuracy: 93.39%
- Name: selecsls60
In Collection: SelecSLS
Metadata:
FLOPs: 4610472600
Parameters: 30670000
File Size: 122839714
Architecture:
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Global Average Pooling
- ReLU
- SelecSLS Block
Tasks:
- Image Classification
Training Techniques:
- Cosine Annealing
- Random Erasing
Training Data:
- ImageNet
ID: selecsls60
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L342
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60-bbf87526.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.99%
Top 5 Accuracy: 93.83%
- Name: selecsls60b
In Collection: SelecSLS
Metadata:
FLOPs: 4657653144
Parameters: 32770000
File Size: 131252898
Architecture:
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Global Average Pooling
- ReLU
- SelecSLS Block
Tasks:
- Image Classification
Training Techniques:
- Cosine Annealing
- Random Erasing
Training Data:
- ImageNet
ID: selecsls60b
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L349
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60b-94e619b5.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.41%
Top 5 Accuracy: 94.18%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/seresnext.mdx | # SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNext](https://www.paperswithcode.com/method/resneXt) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('seresnext26d_32x4d', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `seresnext26d_32x4d`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('seresnext26d_32x4d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{hu2019squeezeandexcitation,
title={Squeeze-and-Excitation Networks},
author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
year={2019},
eprint={1709.01507},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: SEResNeXt
Paper:
Title: Squeeze-and-Excitation Networks
URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
Models:
- Name: seresnext26d_32x4d
In Collection: SEResNeXt
Metadata:
FLOPs: 3507053024
Parameters: 16810000
File Size: 67425193
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: seresnext26d_32x4d
LR: 0.6
Epochs: 100
Layers: 26
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1234
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26d_32x4d-80fa48a3.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.59%
Top 5 Accuracy: 93.61%
- Name: seresnext26t_32x4d
In Collection: SEResNeXt
Metadata:
FLOPs: 3466436448
Parameters: 16820000
File Size: 67414838
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: seresnext26t_32x4d
LR: 0.6
Epochs: 100
Layers: 26
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1246
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26tn_32x4d-569cb627.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.99%
Top 5 Accuracy: 93.73%
- Name: seresnext50_32x4d
In Collection: SEResNeXt
Metadata:
FLOPs: 5475179184
Parameters: 27560000
File Size: 110569859
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: seresnext50_32x4d
LR: 0.6
Epochs: 100
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1267
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext50_32x4d_racm-a304a460.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.27%
Top 5 Accuracy: 95.62%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/skresnet.mdx | # SK-ResNet
**SK ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNet are replaced by the proposed [SK convolutions](https://paperswithcode.com/method/selective-kernel-convolution), enabling the network to choose appropriate receptive field sizes in an adaptive manner.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('skresnet18', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `skresnet18`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('skresnet18', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{li2019selective,
title={Selective Kernel Networks},
author={Xiang Li and Wenhai Wang and Xiaolin Hu and Jian Yang},
year={2019},
eprint={1903.06586},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: SKResNet
Paper:
Title: Selective Kernel Networks
URL: https://paperswithcode.com/paper/selective-kernel-networks
Models:
- Name: skresnet18
In Collection: SKResNet
Metadata:
FLOPs: 2333467136
Parameters: 11960000
File Size: 47923238
Architecture:
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- Residual Connection
- Selective Kernel
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x GPUs
ID: skresnet18
LR: 0.1
Epochs: 100
Layers: 18
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 4.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/sknet.py#L148
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet18_ra-4eec2804.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 73.03%
Top 5 Accuracy: 91.17%
- Name: skresnet34
In Collection: SKResNet
Metadata:
FLOPs: 4711849952
Parameters: 22280000
File Size: 89299314
Architecture:
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- Residual Connection
- Selective Kernel
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x GPUs
ID: skresnet34
LR: 0.1
Epochs: 100
Layers: 34
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 4.0e-05
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/sknet.py#L165
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet34_ra-bdc0ccde.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.93%
Top 5 Accuracy: 93.32%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/skresnext.mdx | # SK-ResNeXt
**SK ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs a [Selective Kernel](https://paperswithcode.com/method/selective-kernel) unit. In general, all the large kernel convolutions in the original bottleneck blocks in ResNext are replaced by the proposed [SK convolutions](https://paperswithcode.com/method/selective-kernel-convolution), enabling the network to choose appropriate receptive field sizes in an adaptive manner.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('skresnext50_32x4d', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `skresnext50_32x4d`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('skresnext50_32x4d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{li2019selective,
title={Selective Kernel Networks},
author={Xiang Li and Wenhai Wang and Xiaolin Hu and Jian Yang},
year={2019},
eprint={1903.06586},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: SKResNeXt
Paper:
Title: Selective Kernel Networks
URL: https://paperswithcode.com/paper/selective-kernel-networks
Models:
- Name: skresnext50_32x4d
In Collection: SKResNeXt
Metadata:
FLOPs: 5739845824
Parameters: 27480000
File Size: 110340975
Architecture:
- Convolution
- Dense Connections
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- Residual Connection
- Selective Kernel
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
Training Resources: 8x GPUs
ID: skresnext50_32x4d
LR: 0.1
Epochs: 100
Layers: 50
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/sknet.py#L210
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnext50_ra-f40e40bf.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.15%
Top 5 Accuracy: 94.64%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/spnasnet.mdx | # SPNASNet
**Single-Path NAS** is a novel differentiable NAS method for designing hardware-efficient ConvNets in less than 4 hours.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('spnasnet_100', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `spnasnet_100`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('spnasnet_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{stamoulis2019singlepath,
title={Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours},
author={Dimitrios Stamoulis and Ruizhou Ding and Di Wang and Dimitrios Lymberopoulos and Bodhi Priyantha and Jie Liu and Diana Marculescu},
year={2019},
eprint={1904.02877},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
Type: model-index
Collections:
- Name: SPNASNet
Paper:
Title: 'Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4
Hours'
URL: https://paperswithcode.com/paper/single-path-nas-designing-hardware-efficient
Models:
- Name: spnasnet_100
In Collection: SPNASNet
Metadata:
FLOPs: 442385600
Parameters: 4420000
File Size: 17902337
Architecture:
- Average Pooling
- Batch Normalization
- Convolution
- Depthwise Separable Convolution
- Dropout
- ReLU
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: spnasnet_100
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L995
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 74.08%
Top 5 Accuracy: 91.82%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/ssl-resnet.mdx | # SSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks.
The model in this collection utilises semi-supervised learning to improve the performance of the model. The approach brings important gains to standard architectures for image, video and fine-grained classification.
Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('ssl_resnet18', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `ssl_resnet18`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('ssl_resnet18', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/abs-1905-00546,
author = {I. Zeki Yalniz and
Herv{\'{e}} J{\'{e}}gou and
Kan Chen and
Manohar Paluri and
Dhruv Mahajan},
title = {Billion-scale semi-supervised learning for image classification},
journal = {CoRR},
volume = {abs/1905.00546},
year = {2019},
url = {http://arxiv.org/abs/1905.00546},
archivePrefix = {arXiv},
eprint = {1905.00546},
timestamp = {Mon, 28 Sep 2020 08:19:37 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-00546.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: SSL ResNet
Paper:
Title: Billion-scale semi-supervised learning for image classification
URL: https://paperswithcode.com/paper/billion-scale-semi-supervised-learning-for
Models:
- Name: ssl_resnet18
In Collection: SSL ResNet
Metadata:
FLOPs: 2337073152
Parameters: 11690000
File Size: 46811375
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
- YFCC-100M
Training Resources: 64x GPUs
ID: ssl_resnet18
LR: 0.0015
Epochs: 30
Layers: 18
Crop Pct: '0.875'
Batch Size: 1536
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L894
Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 72.62%
Top 5 Accuracy: 91.42%
- Name: ssl_resnet50
In Collection: SSL ResNet
Metadata:
FLOPs: 5282531328
Parameters: 25560000
File Size: 102480594
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
- YFCC-100M
Training Resources: 64x GPUs
ID: ssl_resnet50
LR: 0.0015
Epochs: 30
Layers: 50
Crop Pct: '0.875'
Batch Size: 1536
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L904
Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.24%
Top 5 Accuracy: 94.83%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/swsl-resnet.mdx | # SWSL ResNet
**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks.
The models in this collection utilise semi-weakly supervised learning to improve the performance of the model. The approach brings important gains to standard architectures for image, video and fine-grained classification.
Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('swsl_resnet18', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `swsl_resnet18`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('swsl_resnet18', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/abs-1905-00546,
author = {I. Zeki Yalniz and
Herv{\'{e}} J{\'{e}}gou and
Kan Chen and
Manohar Paluri and
Dhruv Mahajan},
title = {Billion-scale semi-supervised learning for image classification},
journal = {CoRR},
volume = {abs/1905.00546},
year = {2019},
url = {http://arxiv.org/abs/1905.00546},
archivePrefix = {arXiv},
eprint = {1905.00546},
timestamp = {Mon, 28 Sep 2020 08:19:37 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-00546.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: SWSL ResNet
Paper:
Title: Billion-scale semi-supervised learning for image classification
URL: https://paperswithcode.com/paper/billion-scale-semi-supervised-learning-for
Models:
- Name: swsl_resnet18
In Collection: SWSL ResNet
Metadata:
FLOPs: 2337073152
Parameters: 11690000
File Size: 46811375
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- IG-1B-Targeted
- ImageNet
Training Resources: 64x GPUs
ID: swsl_resnet18
LR: 0.0015
Epochs: 30
Layers: 18
Crop Pct: '0.875'
Batch Size: 1536
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L954
Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 73.28%
Top 5 Accuracy: 91.76%
- Name: swsl_resnet50
In Collection: SWSL ResNet
Metadata:
FLOPs: 5282531328
Parameters: 25560000
File Size: 102480594
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- IG-1B-Targeted
- ImageNet
Training Resources: 64x GPUs
ID: swsl_resnet50
LR: 0.0015
Epochs: 30
Layers: 50
Crop Pct: '0.875'
Batch Size: 1536
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L965
Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.14%
Top 5 Accuracy: 95.97%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/swsl-resnext.mdx | # SWSL ResNeXt
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width.
The models in this collection utilise semi-weakly supervised learning to improve the performance of the model. The approach brings important gains to standard architectures for image, video and fine-grained classification.
Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('swsl_resnext101_32x16d', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `swsl_resnext101_32x16d`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('swsl_resnext101_32x16d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/abs-1905-00546,
author = {I. Zeki Yalniz and
Herv{\'{e}} J{\'{e}}gou and
Kan Chen and
Manohar Paluri and
Dhruv Mahajan},
title = {Billion-scale semi-supervised learning for image classification},
journal = {CoRR},
volume = {abs/1905.00546},
year = {2019},
url = {http://arxiv.org/abs/1905.00546},
archivePrefix = {arXiv},
eprint = {1905.00546},
timestamp = {Mon, 28 Sep 2020 08:19:37 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-00546.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: SWSL ResNext
Paper:
Title: Billion-scale semi-supervised learning for image classification
URL: https://paperswithcode.com/paper/billion-scale-semi-supervised-learning-for
Models:
- Name: swsl_resnext101_32x16d
In Collection: SWSL ResNext
Metadata:
FLOPs: 46623691776
Parameters: 194030000
File Size: 777518664
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- IG-1B-Targeted
- ImageNet
Training Resources: 64x GPUs
ID: swsl_resnext101_32x16d
LR: 0.0015
Epochs: 30
Layers: 101
Crop Pct: '0.875'
Batch Size: 1536
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L1009
Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.34%
Top 5 Accuracy: 96.84%
- Name: swsl_resnext101_32x4d
In Collection: SWSL ResNext
Metadata:
FLOPs: 10298145792
Parameters: 44180000
File Size: 177341913
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- IG-1B-Targeted
- ImageNet
Training Resources: 64x GPUs
ID: swsl_resnext101_32x4d
LR: 0.0015
Epochs: 30
Layers: 101
Crop Pct: '0.875'
Batch Size: 1536
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L987
Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.22%
Top 5 Accuracy: 96.77%
- Name: swsl_resnext101_32x8d
In Collection: SWSL ResNext
Metadata:
FLOPs: 21180417024
Parameters: 88790000
File Size: 356056638
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- IG-1B-Targeted
- ImageNet
Training Resources: 64x GPUs
ID: swsl_resnext101_32x8d
LR: 0.0015
Epochs: 30
Layers: 101
Crop Pct: '0.875'
Batch Size: 1536
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L998
Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 84.27%
Top 5 Accuracy: 97.17%
- Name: swsl_resnext50_32x4d
In Collection: SWSL ResNext
Metadata:
FLOPs: 5472648192
Parameters: 25030000
File Size: 100428550
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- IG-1B-Targeted
- ImageNet
Training Resources: 64x GPUs
ID: swsl_resnext50_32x4d
LR: 0.0015
Epochs: 30
Layers: 50
Crop Pct: '0.875'
Batch Size: 1536
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L976
Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.17%
Top 5 Accuracy: 96.23%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-efficientnet-condconv.mdx | # (Tensorflow) EfficientNet CondConv
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use $2^N$ times more computational resources, then we can simply increase the network depth by $\alpha ^ N$, width by $\beta ^ N$, and image size by $\gamma ^ N$, where $\alpha, \beta, \gamma$ are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient $\phi$ to uniformly scales network width, depth, and resolution in a principled way.
The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.
The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2), in addition to squeeze-and-excitation blocks.
This collection of models amends EfficientNet by adding [CondConv](https://paperswithcode.com/method/condconv) convolutions.
The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('tf_efficientnet_cc_b0_4e', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `tf_efficientnet_cc_b0_4e`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('tf_efficientnet_cc_b0_4e', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/abs-1904-04971,
author = {Brandon Yang and
Gabriel Bender and
Quoc V. Le and
Jiquan Ngiam},
title = {Soft Conditional Computation},
journal = {CoRR},
volume = {abs/1904.04971},
year = {2019},
url = {http://arxiv.org/abs/1904.04971},
archivePrefix = {arXiv},
eprint = {1904.04971},
timestamp = {Thu, 25 Apr 2019 13:55:01 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1904-04971.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: TF EfficientNet CondConv
Paper:
Title: 'CondConv: Conditionally Parameterized Convolutions for Efficient Inference'
URL: https://paperswithcode.com/paper/soft-conditional-computation
Models:
- Name: tf_efficientnet_cc_b0_4e
In Collection: TF EfficientNet CondConv
Metadata:
FLOPs: 224153788
Parameters: 13310000
File Size: 53490940
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- CondConv
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_cc_b0_4e
LR: 0.256
Epochs: 350
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 2048
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1561
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_4e-4362b6b2.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.32%
Top 5 Accuracy: 93.32%
- Name: tf_efficientnet_cc_b0_8e
In Collection: TF EfficientNet CondConv
Metadata:
FLOPs: 224158524
Parameters: 24010000
File Size: 96287616
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- CondConv
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_cc_b0_8e
LR: 0.256
Epochs: 350
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 2048
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1572
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_8e-66184a25.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.91%
Top 5 Accuracy: 93.65%
- Name: tf_efficientnet_cc_b1_8e
In Collection: TF EfficientNet CondConv
Metadata:
FLOPs: 370427824
Parameters: 39720000
File Size: 159206198
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- CondConv
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_cc_b1_8e
LR: 0.256
Epochs: 350
Crop Pct: '0.882'
Momentum: 0.9
Batch Size: 2048
Image Size: '240'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1584
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b1_8e-f7c79ae1.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.33%
Top 5 Accuracy: 94.37%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-efficientnet-lite.mdx | # (Tensorflow) EfficientNet Lite
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use $2^N$ times more computational resources, then we can simply increase the network depth by $\alpha ^ N$, width by $\beta ^ N$, and image size by $\gamma ^ N$, where $\alpha, \beta, \gamma$ are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient $\phi$ to uniformly scales network width, depth, and resolution in a principled way.
The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.
The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2).
EfficientNet-Lite makes EfficientNet more suitable for mobile devices by introducing [ReLU6](https://paperswithcode.com/method/relu6) activation functions and removing [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation).
The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('tf_efficientnet_lite0', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `tf_efficientnet_lite0`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('tf_efficientnet_lite0', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{tan2020efficientnet,
title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
author={Mingxing Tan and Quoc V. Le},
year={2020},
eprint={1905.11946},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
Type: model-index
Collections:
- Name: TF EfficientNet Lite
Paper:
Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
URL: https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for
Models:
- Name: tf_efficientnet_lite0
In Collection: TF EfficientNet Lite
Metadata:
FLOPs: 488052032
Parameters: 4650000
File Size: 18820223
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- RELU6
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_lite0
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1596
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 74.83%
Top 5 Accuracy: 92.17%
- Name: tf_efficientnet_lite1
In Collection: TF EfficientNet Lite
Metadata:
FLOPs: 773639520
Parameters: 5420000
File Size: 21939331
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- RELU6
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_lite1
Crop Pct: '0.882'
Image Size: '240'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1607
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite1-bde8b488.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.67%
Top 5 Accuracy: 93.24%
- Name: tf_efficientnet_lite2
In Collection: TF EfficientNet Lite
Metadata:
FLOPs: 1068494432
Parameters: 6090000
File Size: 24658687
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- RELU6
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_lite2
Crop Pct: '0.89'
Image Size: '260'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1618
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite2-dcccb7df.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.48%
Top 5 Accuracy: 93.75%
- Name: tf_efficientnet_lite3
In Collection: TF EfficientNet Lite
Metadata:
FLOPs: 2011534304
Parameters: 8199999
File Size: 33161413
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- RELU6
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_lite3
Crop Pct: '0.904'
Image Size: '300'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1629
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite3-b733e338.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.83%
Top 5 Accuracy: 94.91%
- Name: tf_efficientnet_lite4
In Collection: TF EfficientNet Lite
Metadata:
FLOPs: 5164802912
Parameters: 13010000
File Size: 52558819
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- RELU6
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_lite4
Crop Pct: '0.92'
Image Size: '380'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1640
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite4-741542c3.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.54%
Top 5 Accuracy: 95.66%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-efficientnet.mdx | # (Tensorflow) EfficientNet
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use $2^N$ times more computational resources, then we can simply increase the network depth by $\alpha ^ N$, width by $\beta ^ N$, and image size by $\gamma ^ N$, where $\alpha, \beta, \gamma$ are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient $\phi$ to uniformly scales network width, depth, and resolution in a principled way.
The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.
The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2), in addition to [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block).
The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('tf_efficientnet_b0', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `tf_efficientnet_b0`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('tf_efficientnet_b0', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{tan2020efficientnet,
title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
author={Mingxing Tan and Quoc V. Le},
year={2020},
eprint={1905.11946},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
Type: model-index
Collections:
- Name: TF EfficientNet
Paper:
Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
URL: https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for
Models:
- Name: tf_efficientnet_b0
In Collection: TF EfficientNet
Metadata:
FLOPs: 488688572
Parameters: 5290000
File Size: 21383997
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
Training Resources: TPUv3 Cloud TPU
ID: tf_efficientnet_b0
LR: 0.256
Epochs: 350
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 2048
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1241
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.85%
Top 5 Accuracy: 93.23%
- Name: tf_efficientnet_b1
In Collection: TF EfficientNet
Metadata:
FLOPs: 883633200
Parameters: 7790000
File Size: 31512534
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b1
LR: 0.256
Epochs: 350
Crop Pct: '0.882'
Momentum: 0.9
Batch Size: 2048
Image Size: '240'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1251
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_aa-ea7a6ee0.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.84%
Top 5 Accuracy: 94.2%
- Name: tf_efficientnet_b2
In Collection: TF EfficientNet
Metadata:
FLOPs: 1234321170
Parameters: 9110000
File Size: 36797929
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b2
LR: 0.256
Epochs: 350
Crop Pct: '0.89'
Momentum: 0.9
Batch Size: 2048
Image Size: '260'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1261
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_aa-60c94f97.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.07%
Top 5 Accuracy: 94.9%
- Name: tf_efficientnet_b3
In Collection: TF EfficientNet
Metadata:
FLOPs: 2275247568
Parameters: 12230000
File Size: 49381362
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b3
LR: 0.256
Epochs: 350
Crop Pct: '0.904'
Momentum: 0.9
Batch Size: 2048
Image Size: '300'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1271
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_aa-84b4657e.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.65%
Top 5 Accuracy: 95.72%
- Name: tf_efficientnet_b4
In Collection: TF EfficientNet
Metadata:
FLOPs: 5749638672
Parameters: 19340000
File Size: 77989689
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
Training Resources: TPUv3 Cloud TPU
ID: tf_efficientnet_b4
LR: 0.256
Epochs: 350
Crop Pct: '0.922'
Momentum: 0.9
Batch Size: 2048
Image Size: '380'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1281
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_aa-818f208c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.03%
Top 5 Accuracy: 96.3%
- Name: tf_efficientnet_b5
In Collection: TF EfficientNet
Metadata:
FLOPs: 13176501888
Parameters: 30390000
File Size: 122403150
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b5
LR: 0.256
Epochs: 350
Crop Pct: '0.934'
Momentum: 0.9
Batch Size: 2048
Image Size: '456'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1291
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ra-9a3e5369.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.81%
Top 5 Accuracy: 96.75%
- Name: tf_efficientnet_b6
In Collection: TF EfficientNet
Metadata:
FLOPs: 24180518488
Parameters: 43040000
File Size: 173232007
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b6
LR: 0.256
Epochs: 350
Crop Pct: '0.942'
Momentum: 0.9
Batch Size: 2048
Image Size: '528'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1301
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_aa-80ba17e4.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 84.11%
Top 5 Accuracy: 96.89%
- Name: tf_efficientnet_b7
In Collection: TF EfficientNet
Metadata:
FLOPs: 48205304880
Parameters: 66349999
File Size: 266850607
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b7
LR: 0.256
Epochs: 350
Crop Pct: '0.949'
Momentum: 0.9
Batch Size: 2048
Image Size: '600'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1312
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ra-6c08e654.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 84.93%
Top 5 Accuracy: 97.2%
- Name: tf_efficientnet_b8
In Collection: TF EfficientNet
Metadata:
FLOPs: 80962956270
Parameters: 87410000
File Size: 351379853
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Label Smoothing
- RMSProp
- Stochastic Depth
- Weight Decay
Training Data:
- ImageNet
ID: tf_efficientnet_b8
LR: 0.256
Epochs: 350
Crop Pct: '0.954'
Momentum: 0.9
Batch Size: 2048
Image Size: '672'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1323
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ra-572d5dd9.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 85.35%
Top 5 Accuracy: 97.39%
- Name: tf_efficientnet_el
In Collection: TF EfficientNet
Metadata:
FLOPs: 9356616096
Parameters: 10590000
File Size: 42800271
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_el
Crop Pct: '0.904'
Image Size: '300'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1551
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.45%
Top 5 Accuracy: 95.17%
- Name: tf_efficientnet_em
In Collection: TF EfficientNet
Metadata:
FLOPs: 3636607040
Parameters: 6900000
File Size: 27933644
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_em
Crop Pct: '0.882'
Image Size: '240'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1541
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_em-e78cfe58.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.71%
Top 5 Accuracy: 94.33%
- Name: tf_efficientnet_es
In Collection: TF EfficientNet
Metadata:
FLOPs: 2057577472
Parameters: 5440000
File Size: 22008479
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: tf_efficientnet_es
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1531
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.28%
Top 5 Accuracy: 93.6%
- Name: tf_efficientnet_l2_ns_475
In Collection: TF EfficientNet
Metadata:
FLOPs: 217795669644
Parameters: 480310000
File Size: 1925950424
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- FixRes
- Label Smoothing
- Noisy Student
- RMSProp
- RandAugment
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: TPUv3 Cloud TPU
ID: tf_efficientnet_l2_ns_475
LR: 0.128
Epochs: 350
Dropout: 0.5
Crop Pct: '0.936'
Momentum: 0.9
Batch Size: 2048
Image Size: '475'
Weight Decay: 1.0e-05
Interpolation: bicubic
RMSProp Decay: 0.9
Label Smoothing: 0.1
BatchNorm Momentum: 0.99
Stochastic Depth Survival: 0.8
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1509
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns_475-bebbd00a.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 88.24%
Top 5 Accuracy: 98.55%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-inception-v3.mdx | # (Tensorflow) Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('tf_inception_v3', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `tf_inception_v3`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('tf_inception_v3', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/SzegedyVISW15,
author = {Christian Szegedy and
Vincent Vanhoucke and
Sergey Ioffe and
Jonathon Shlens and
Zbigniew Wojna},
title = {Rethinking the Inception Architecture for Computer Vision},
journal = {CoRR},
volume = {abs/1512.00567},
year = {2015},
url = {http://arxiv.org/abs/1512.00567},
archivePrefix = {arXiv},
eprint = {1512.00567},
timestamp = {Mon, 13 Aug 2018 16:49:07 +0200},
biburl = {https://dblp.org/rec/journals/corr/SzegedyVISW15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: TF Inception v3
Paper:
Title: Rethinking the Inception Architecture for Computer Vision
URL: https://paperswithcode.com/paper/rethinking-the-inception-architecture-for
Models:
- Name: tf_inception_v3
In Collection: TF Inception v3
Metadata:
FLOPs: 7352418880
Parameters: 23830000
File Size: 95549439
Architecture:
- 1x1 Convolution
- Auxiliary Classifier
- Average Pooling
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inception-v3 Module
- Max Pooling
- ReLU
- Softmax
Tasks:
- Image Classification
Training Techniques:
- Gradient Clipping
- Label Smoothing
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 50x NVIDIA Kepler GPUs
ID: tf_inception_v3
LR: 0.045
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v3.py#L449
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.87%
Top 5 Accuracy: 93.65%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-mixnet.mdx | # (Tensorflow) MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('tf_mixnet_l', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `tf_mixnet_l`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('tf_mixnet_l', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{tan2019mixconv,
title={MixConv: Mixed Depthwise Convolutional Kernels},
author={Mingxing Tan and Quoc V. Le},
year={2019},
eprint={1907.09595},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: TF MixNet
Paper:
Title: 'MixConv: Mixed Depthwise Convolutional Kernels'
URL: https://paperswithcode.com/paper/mixnet-mixed-depthwise-convolutional-kernels
Models:
- Name: tf_mixnet_l
In Collection: TF MixNet
Metadata:
FLOPs: 688674516
Parameters: 7330000
File Size: 29620756
Architecture:
- Batch Normalization
- Dense Connections
- Dropout
- Global Average Pooling
- Grouped Convolution
- MixConv
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- MNAS
Training Data:
- ImageNet
ID: tf_mixnet_l
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1720
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.78%
Top 5 Accuracy: 94.0%
- Name: tf_mixnet_m
In Collection: TF MixNet
Metadata:
FLOPs: 416633502
Parameters: 5010000
File Size: 20310871
Architecture:
- Batch Normalization
- Dense Connections
- Dropout
- Global Average Pooling
- Grouped Convolution
- MixConv
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- MNAS
Training Data:
- ImageNet
ID: tf_mixnet_m
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1709
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.96%
Top 5 Accuracy: 93.16%
- Name: tf_mixnet_s
In Collection: TF MixNet
Metadata:
FLOPs: 302587678
Parameters: 4130000
File Size: 16738218
Architecture:
- Batch Normalization
- Dense Connections
- Dropout
- Global Average Pooling
- Grouped Convolution
- MixConv
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- MNAS
Training Data:
- ImageNet
ID: tf_mixnet_s
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1698
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.68%
Top 5 Accuracy: 92.64%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tf-mobilenet-v3.mdx | # (Tensorflow) MobileNet v3
**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-block) modules in the [MBConv blocks](https://paperswithcode.com/method/inverted-residual-block).
The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('tf_mobilenetv3_large_075', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `tf_mobilenetv3_large_075`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('tf_mobilenetv3_large_075', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/abs-1905-02244,
author = {Andrew Howard and
Mark Sandler and
Grace Chu and
Liang{-}Chieh Chen and
Bo Chen and
Mingxing Tan and
Weijun Wang and
Yukun Zhu and
Ruoming Pang and
Vijay Vasudevan and
Quoc V. Le and
Hartwig Adam},
title = {Searching for MobileNetV3},
journal = {CoRR},
volume = {abs/1905.02244},
year = {2019},
url = {http://arxiv.org/abs/1905.02244},
archivePrefix = {arXiv},
eprint = {1905.02244},
timestamp = {Tue, 12 Jan 2021 15:30:06 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: TF MobileNet V3
Paper:
Title: Searching for MobileNetV3
URL: https://paperswithcode.com/paper/searching-for-mobilenetv3
Models:
- Name: tf_mobilenetv3_large_075
In Collection: TF MobileNet V3
Metadata:
FLOPs: 194323712
Parameters: 3990000
File Size: 16097377
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Hard Swish
- Inverted Residual Block
- ReLU
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x4 TPU Pod
ID: tf_mobilenetv3_large_075
LR: 0.1
Dropout: 0.8
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4096
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L394
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 73.45%
Top 5 Accuracy: 91.34%
- Name: tf_mobilenetv3_large_100
In Collection: TF MobileNet V3
Metadata:
FLOPs: 274535288
Parameters: 5480000
File Size: 22076649
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Hard Swish
- Inverted Residual Block
- ReLU
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x4 TPU Pod
ID: tf_mobilenetv3_large_100
LR: 0.1
Dropout: 0.8
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4096
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L403
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.51%
Top 5 Accuracy: 92.61%
- Name: tf_mobilenetv3_large_minimal_100
In Collection: TF MobileNet V3
Metadata:
FLOPs: 267216928
Parameters: 3920000
File Size: 15836368
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Hard Swish
- Inverted Residual Block
- ReLU
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x4 TPU Pod
ID: tf_mobilenetv3_large_minimal_100
LR: 0.1
Dropout: 0.8
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4096
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L412
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 72.24%
Top 5 Accuracy: 90.64%
- Name: tf_mobilenetv3_small_075
In Collection: TF MobileNet V3
Metadata:
FLOPs: 48457664
Parameters: 2040000
File Size: 8242701
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Hard Swish
- Inverted Residual Block
- ReLU
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 16x GPUs
ID: tf_mobilenetv3_small_075
LR: 0.045
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4096
Image Size: '224'
Weight Decay: 4.0e-05
Interpolation: bilinear
RMSProp Decay: 0.9
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L421
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 65.72%
Top 5 Accuracy: 86.13%
- Name: tf_mobilenetv3_small_100
In Collection: TF MobileNet V3
Metadata:
FLOPs: 65450600
Parameters: 2540000
File Size: 10256398
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Hard Swish
- Inverted Residual Block
- ReLU
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 16x GPUs
ID: tf_mobilenetv3_small_100
LR: 0.045
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4096
Image Size: '224'
Weight Decay: 4.0e-05
Interpolation: bilinear
RMSProp Decay: 0.9
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L430
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 67.92%
Top 5 Accuracy: 87.68%
- Name: tf_mobilenetv3_small_minimal_100
In Collection: TF MobileNet V3
Metadata:
FLOPs: 60827936
Parameters: 2040000
File Size: 8258083
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Hard Swish
- Inverted Residual Block
- ReLU
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
Training Resources: 16x GPUs
ID: tf_mobilenetv3_small_minimal_100
LR: 0.045
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4096
Image Size: '224'
Weight Decay: 4.0e-05
Interpolation: bilinear
RMSProp Decay: 0.9
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L439
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 62.91%
Top 5 Accuracy: 84.24%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/tresnet.mdx | # TResNet
A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency. They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-downsampling), In-Place Activated BatchNorm, Blocks selection and [squeeze-and-excitation layers](https://paperswithcode.com/method/squeeze-and-excitation-block).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('tresnet_l', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `tresnet_l`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('tresnet_l', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{ridnik2020tresnet,
title={TResNet: High Performance GPU-Dedicated Architecture},
author={Tal Ridnik and Hussam Lawen and Asaf Noy and Emanuel Ben Baruch and Gilad Sharir and Itamar Friedman},
year={2020},
eprint={2003.13630},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: TResNet
Paper:
Title: 'TResNet: High Performance GPU-Dedicated Architecture'
URL: https://paperswithcode.com/paper/tresnet-high-performance-gpu-dedicated
Models:
- Name: tresnet_l
In Collection: TResNet
Metadata:
FLOPs: 10873416792
Parameters: 53456696
File Size: 224440219
Architecture:
- 1x1 Convolution
- Anti-Alias Downsampling
- Convolution
- Global Average Pooling
- InPlace-ABN
- Leaky ReLU
- ReLU
- Residual Connection
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Cutout
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA 100 GPUs
ID: tresnet_l
LR: 0.01
Epochs: 300
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L267
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_81_5-235b486c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.49%
Top 5 Accuracy: 95.62%
- Name: tresnet_l_448
In Collection: TResNet
Metadata:
FLOPs: 43488238584
Parameters: 53456696
File Size: 224440219
Architecture:
- 1x1 Convolution
- Anti-Alias Downsampling
- Convolution
- Global Average Pooling
- InPlace-ABN
- Leaky ReLU
- ReLU
- Residual Connection
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Cutout
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA 100 GPUs
ID: tresnet_l_448
LR: 0.01
Epochs: 300
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '448'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L285
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.26%
Top 5 Accuracy: 95.98%
- Name: tresnet_m
In Collection: TResNet
Metadata:
FLOPs: 5733048064
Parameters: 41282200
File Size: 125861314
Architecture:
- 1x1 Convolution
- Anti-Alias Downsampling
- Convolution
- Global Average Pooling
- InPlace-ABN
- Leaky ReLU
- ReLU
- Residual Connection
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Cutout
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA 100 GPUs
Training Time: < 24 hours
ID: tresnet_m
LR: 0.01
Epochs: 300
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L261
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_80_8-dbc13962.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.8%
Top 5 Accuracy: 94.86%
- Name: tresnet_m_448
In Collection: TResNet
Metadata:
FLOPs: 22929743104
Parameters: 29278464
File Size: 125861314
Architecture:
- 1x1 Convolution
- Anti-Alias Downsampling
- Convolution
- Global Average Pooling
- InPlace-ABN
- Leaky ReLU
- ReLU
- Residual Connection
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Cutout
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA 100 GPUs
ID: tresnet_m_448
LR: 0.01
Epochs: 300
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '448'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L279
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_m_448-bc359d10.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.72%
Top 5 Accuracy: 95.57%
- Name: tresnet_xl
In Collection: TResNet
Metadata:
FLOPs: 15162534034
Parameters: 75646610
File Size: 314378965
Architecture:
- 1x1 Convolution
- Anti-Alias Downsampling
- Convolution
- Global Average Pooling
- InPlace-ABN
- Leaky ReLU
- ReLU
- Residual Connection
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Cutout
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA 100 GPUs
ID: tresnet_xl
LR: 0.01
Epochs: 300
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L273
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_xl_82_0-a2d51b00.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.05%
Top 5 Accuracy: 95.93%
- Name: tresnet_xl_448
In Collection: TResNet
Metadata:
FLOPs: 60641712730
Parameters: 75646610
File Size: 224440219
Architecture:
- 1x1 Convolution
- Anti-Alias Downsampling
- Convolution
- Global Average Pooling
- InPlace-ABN
- Leaky ReLU
- ReLU
- Residual Connection
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- Cutout
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA 100 GPUs
ID: tresnet_xl_448
LR: 0.01
Epochs: 300
Crop Pct: '0.875'
Momentum: 0.9
Image Size: '448'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/tresnet.py#L291
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_448-940d0cd1.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.06%
Top 5 Accuracy: 96.19%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/wide-resnet.mdx | # Wide ResNet
**Wide Residual Networks** are a variant on [ResNets](https://paperswithcode.com/method/resnet) where we decrease depth and increase the width of residual networks. This is achieved through the use of [wide residual blocks](https://paperswithcode.com/method/wide-residual-block).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('wide_resnet101_2', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `wide_resnet101_2`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('wide_resnet101_2', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/ZagoruykoK16,
author = {Sergey Zagoruyko and
Nikos Komodakis},
title = {Wide Residual Networks},
journal = {CoRR},
volume = {abs/1605.07146},
year = {2016},
url = {http://arxiv.org/abs/1605.07146},
archivePrefix = {arXiv},
eprint = {1605.07146},
timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},
biburl = {https://dblp.org/rec/journals/corr/ZagoruykoK16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: Wide ResNet
Paper:
Title: Wide Residual Networks
URL: https://paperswithcode.com/paper/wide-residual-networks
Models:
- Name: wide_resnet101_2
In Collection: Wide ResNet
Metadata:
FLOPs: 29304929280
Parameters: 126890000
File Size: 254695146
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Wide Residual Block
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: wide_resnet101_2
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/resnet.py#L802
Weights: https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.85%
Top 5 Accuracy: 94.28%
- Name: wide_resnet50_2
In Collection: Wide ResNet
Metadata:
FLOPs: 14688058368
Parameters: 68880000
File Size: 275853271
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Wide Residual Block
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: wide_resnet50_2
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/resnet.py#L790
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/wide_resnet50_racm-8234f177.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.45%
Top 5 Accuracy: 95.52%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/models/xception.mdx | # Xception
**Xception** is a convolutional neural network architecture that relies solely on [depthwise separable convolution layers](https://paperswithcode.com/method/depthwise-separable-convolution).
The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('xception', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `xception`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('xception', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/ZagoruykoK16,
@misc{chollet2017xception,
title={Xception: Deep Learning with Depthwise Separable Convolutions},
author={François Chollet},
year={2017},
eprint={1610.02357},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: Xception
Paper:
Title: 'Xception: Deep Learning with Depthwise Separable Convolutions'
URL: https://paperswithcode.com/paper/xception-deep-learning-with-depthwise
Models:
- Name: xception
In Collection: Xception
Metadata:
FLOPs: 10600506792
Parameters: 22860000
File Size: 91675053
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: xception
Crop Pct: '0.897'
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception.py#L229
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/xception-43020ad28.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.05%
Top 5 Accuracy: 94.4%
- Name: xception41
In Collection: Xception
Metadata:
FLOPs: 11681983232
Parameters: 26970000
File Size: 108422028
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: xception41
Crop Pct: '0.903'
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception_aligned.py#L181
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_41-e6439c97.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.54%
Top 5 Accuracy: 94.28%
- Name: xception65
In Collection: Xception
Metadata:
FLOPs: 17585702144
Parameters: 39920000
File Size: 160536780
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: xception65
Crop Pct: '0.903'
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception_aligned.py#L200
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_65-c9ae96e8.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.55%
Top 5 Accuracy: 94.66%
- Name: xception71
In Collection: Xception
Metadata:
FLOPs: 22817346560
Parameters: 42340000
File Size: 170295556
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Depthwise Separable Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: xception71
Crop Pct: '0.903'
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/xception_aligned.py#L219
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_xception_71-8eec7df1.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.88%
Top 5 Accuracy: 94.93%
--> | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/reference/data.mdx | # Data
[[autodoc]] timm.data.create_dataset
[[autodoc]] timm.data.create_loader
[[autodoc]] timm.data.create_transform
[[autodoc]] timm.data.resolve_data_config | 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/reference/models.mdx | # Models
[[autodoc]] timm.create_model
[[autodoc]] timm.list_models
| 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/reference/optimizers.mdx | # Optimization
This page contains the API reference documentation for learning rate optimizers included in `timm`.
## Optimizers
### Factory functions
[[autodoc]] timm.optim.optim_factory.create_optimizer
[[autodoc]] timm.optim.optim_factory.create_optimizer_v2
### Optimizer Classes
[[autodoc]] timm.optim.adabelief.AdaBelief
[[autodoc]] timm.optim.adafactor.Adafactor
[[autodoc]] timm.optim.adahessian.Adahessian
[[autodoc]] timm.optim.adamp.AdamP
[[autodoc]] timm.optim.adamw.AdamW
[[autodoc]] timm.optim.lamb.Lamb
[[autodoc]] timm.optim.lars.Lars
[[autodoc]] timm.optim.lookahead.Lookahead
[[autodoc]] timm.optim.madgrad.MADGRAD
[[autodoc]] timm.optim.nadam.Nadam
[[autodoc]] timm.optim.nvnovograd.NvNovoGrad
[[autodoc]] timm.optim.radam.RAdam
[[autodoc]] timm.optim.rmsprop_tf.RMSpropTF
[[autodoc]] timm.optim.sgdp.SGDP
| 0 |
hf_public_repos/pytorch-image-models/hfdocs/source | hf_public_repos/pytorch-image-models/hfdocs/source/reference/schedulers.mdx | # Learning Rate Schedulers
This page contains the API reference documentation for learning rate schedulers included in `timm`.
## Schedulers
### Factory functions
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler
[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler_v2
### Scheduler Classes
[[autodoc]] timm.scheduler.cosine_lr.CosineLRScheduler
[[autodoc]] timm.scheduler.multistep_lr.MultiStepLRScheduler
[[autodoc]] timm.scheduler.plateau_lr.PlateauLRScheduler
[[autodoc]] timm.scheduler.poly_lr.PolyLRScheduler
[[autodoc]] timm.scheduler.step_lr.StepLRScheduler
[[autodoc]] timm.scheduler.tanh_lr.TanhLRScheduler
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/README.md | # Validation and Benchmark Results
This folder contains validation and benchmark results for the models in this collection. Validation scores are currently only run for models with pretrained weights and ImageNet-1k heads, benchmark numbers are run for all.
## Datasets
There are currently results for the ImageNet validation set and 5 additional test / label sets.
The test set results include rank and top-1/top-5 differences from clean validation. For the "Real Labels", ImageNetV2, and Sketch test sets, the differences were calculated against the full 1000 class ImageNet-1k validation set. For both the Adversarial and Rendition sets, the differences were calculated against 'clean' runs on the ImageNet-1k validation set with the same 200 classes used in each test set respectively.
### ImageNet Validation - [`results-imagenet.csv`](results-imagenet.csv)
The standard 50,000 image ImageNet-1k validation set. Model selection during training utilizes this validation set, so it is not a true test set. Question: Does anyone have the official ImageNet-1k test set classification labels now that challenges are done?
* Source: http://image-net.org/challenges/LSVRC/2012/index
* Paper: "ImageNet Large Scale Visual Recognition Challenge" - https://arxiv.org/abs/1409.0575
### ImageNet-"Real Labels" - [`results-imagenet-real.csv`](results-imagenet-real.csv)
The usual ImageNet-1k validation set with a fresh new set of labels intended to improve on mistakes in the original annotation process.
* Source: https://github.com/google-research/reassessed-imagenet
* Paper: "Are we done with ImageNet?" - https://arxiv.org/abs/2006.07159
### ImageNetV2 Matched Frequency - [`results-imagenetv2-matched-frequency.csv`](results-imagenetv2-matched-frequency.csv)
An ImageNet test set of 10,000 images sampled from new images roughly 10 years after the original. Care was taken to replicate the original ImageNet curation/sampling process.
* Source: https://github.com/modestyachts/ImageNetV2
* Paper: "Do ImageNet Classifiers Generalize to ImageNet?" - https://arxiv.org/abs/1902.10811
### ImageNet-Sketch - [`results-sketch.csv`](results-sketch.csv)
50,000 non photographic (or photos of such) images (sketches, doodles, mostly monochromatic) covering all 1000 ImageNet classes.
* Source: https://github.com/HaohanWang/ImageNet-Sketch
* Paper: "Learning Robust Global Representations by Penalizing Local Predictive Power" - https://arxiv.org/abs/1905.13549
### ImageNet-Adversarial - [`results-imagenet-a.csv`](results-imagenet-a.csv)
A collection of 7500 images covering 200 of the 1000 ImageNet classes. Images are naturally occurring adversarial examples that confuse typical ImageNet classifiers. This is a challenging dataset, your typical ResNet-50 will score 0% top-1.
For clean validation with same 200 classes, see [`results-imagenet-a-clean.csv`](results-imagenet-a-clean.csv)
* Source: https://github.com/hendrycks/natural-adv-examples
* Paper: "Natural Adversarial Examples" - https://arxiv.org/abs/1907.07174
### ImageNet-Rendition - [`results-imagenet-r.csv`](results-imagenet-r.csv)
Renditions of 200 ImageNet classes resulting in 30,000 images for testing robustness.
For clean validation with same 200 classes, see [`results-imagenet-r-clean.csv`](results-imagenet-r-clean.csv)
* Source: https://github.com/hendrycks/imagenet-r
* Paper: "The Many Faces of Robustness" - https://arxiv.org/abs/2006.16241
### TODO
* Explore adding a reduced version of ImageNet-C (Corruptions) and ImageNet-P (Perturbations) from https://github.com/hendrycks/robustness. The originals are huge and image size specific.
## Benchmark
CSV files with a `model_benchmark` prefix include benchmark numbers for models on various accelerators with different precision. Currently only run on RTX 3090 w/ AMP for inference, I intend to add more in the future.
## Metadata
CSV files with `model_metadata` prefix contain extra information about the source training, currently the pretraining dataset and technique (ie distillation, SSL, WSL, etc). Eventually I'd like to have metadata about augmentation, regularization, etc. but that will be a challenge to source consistently.
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt111-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,param_count
tinynet_e,47972.76,21.335,1024,106,2.04
mobilenetv3_small_050,42473.43,24.099,1024,224,1.59
lcnet_035,39739.31,25.756,1024,224,1.64
lcnet_050,35211.0,29.071,1024,224,1.88
mobilenetv3_small_075,31410.3,32.589,1024,224,2.04
mobilenetv3_small_100,28111.39,36.416,1024,224,2.54
tf_mobilenetv3_small_minimal_100,27538.82,37.173,1024,224,2.04
tinynet_d,26670.17,38.384,1024,152,2.34
tf_mobilenetv3_small_075,26522.93,38.597,1024,224,2.04
tf_mobilenetv3_small_100,24036.65,42.591,1024,224,2.54
lcnet_075,22451.72,45.598,1024,224,2.36
levit_128s,19963.52,51.282,1024,224,7.78
mnasnet_small,19706.27,51.952,1024,224,2.03
lcnet_100,18132.59,56.461,1024,224,2.95
mobilenetv2_035,17586.23,58.217,1024,224,1.68
ghostnet_050,16726.5,61.209,1024,224,2.59
regnetx_002,16238.56,63.048,1024,224,2.68
regnety_002,15227.23,67.235,1024,224,3.16
mnasnet_050,15022.24,68.154,1024,224,2.22
tinynet_c,14089.9,72.665,1024,184,2.46
mobilenetv2_050,14032.51,72.961,1024,224,1.97
levit_128,13679.77,74.845,1024,224,9.21
semnasnet_050,13508.98,75.79,1024,224,2.08
vit_small_patch32_224,12109.88,84.548,1024,224,22.88
mixer_s32_224,11702.15,87.494,1024,224,19.1
levit_192,11695.39,87.545,1024,224,10.95
lcnet_150,11564.86,88.533,1024,224,4.5
mobilenetv3_large_075,11407.33,89.755,1024,224,3.99
gernet_s,10837.81,94.473,1024,224,8.17
vit_tiny_r_s16_p8_224,10598.14,96.609,1024,224,6.34
mobilenetv3_rw,10164.28,100.733,1024,224,5.48
tf_mobilenetv3_large_075,10125.76,101.117,1024,224,3.99
regnetx_004,10069.8,101.678,1024,224,5.16
mobilenetv3_large_100,10017.28,102.212,1024,224,5.48
mobilenetv3_large_100_miil,10014.68,102.238,1024,224,5.48
ese_vovnet19b_slim_dw,9944.69,102.957,1024,224,1.9
hardcorenas_a,9792.24,104.561,1024,224,5.26
mnasnet_075,9774.09,104.755,1024,224,3.17
tf_mobilenetv3_large_minimal_100,9771.38,104.784,1024,224,3.92
ghostnet_100,9041.17,113.248,1024,224,5.18
hardcorenas_b,9021.74,113.492,1024,224,5.18
tinynet_b,8976.69,114.061,1024,188,3.73
swsl_resnet18,8971.65,114.125,1024,224,11.69
tf_mobilenetv3_large_100,8954.66,114.343,1024,224,5.48
gluon_resnet18_v1b,8949.74,114.406,1024,224,11.69
ssl_resnet18,8947.2,114.439,1024,224,11.69
resnet18,8927.25,114.693,1024,224,11.69
hardcorenas_c,8864.36,115.506,1024,224,5.52
mobilenetv2_075,8764.66,116.82,1024,224,2.64
mnasnet_100,8646.99,118.411,1024,224,4.38
mnasnet_b1,8646.34,118.421,1024,224,4.38
semnasnet_075,8603.57,119.009,1024,224,2.91
levit_256,8528.42,120.058,1024,224,18.89
regnety_004,8497.03,120.501,1024,224,4.34
seresnet18,8461.0,121.015,1024,224,11.78
hardcorenas_d,8306.25,123.269,1024,224,7.5
legacy_seresnet18,8213.74,124.658,1024,224,11.78
regnetx_006,8055.06,127.114,1024,224,6.2
mobilenetv2_100,7900.5,129.6,1024,224,3.5
spnasnet_100,7827.4,130.811,1024,224,4.42
semnasnet_100,7701.96,132.941,1024,224,3.89
mnasnet_a1,7678.8,133.342,1024,224,3.89
resnet18d,7478.23,136.919,1024,224,11.71
levit_256d,7357.48,139.166,1024,224,26.21
ghostnet_130,7270.82,140.824,1024,224,7.36
regnety_006,7263.32,140.971,1024,224,6.06
hardcorenas_f,7222.67,141.764,1024,224,8.2
hardcorenas_e,7174.56,142.715,1024,224,8.07
efficientnet_lite0,7057.13,145.09,1024,224,4.65
ese_vovnet19b_slim,6975.46,146.789,1024,224,3.17
tinynet_a,6918.13,148.004,1024,192,6.19
fbnetc_100,6847.55,149.531,1024,224,5.57
tf_efficientnetv2_b0,6842.85,149.633,1024,224,7.14
xcit_nano_12_p16_224_dist,6769.74,151.25,1024,224,3.05
xcit_nano_12_p16_224,6760.61,151.454,1024,224,3.05
regnetx_008,6358.56,161.031,1024,224,7.26
deit_tiny_patch16_224,6350.86,161.227,1024,224,5.72
vit_tiny_patch16_224,6346.8,161.33,1024,224,5.72
tf_efficientnet_lite0,6324.64,161.895,1024,224,4.65
deit_tiny_distilled_patch16_224,6241.01,164.064,1024,224,5.91
efficientnet_b0,6183.5,165.59,1024,224,5.29
efficientnet_b1_pruned,6044.57,169.396,1024,240,6.33
rexnet_100,6031.43,169.765,1024,224,4.8
mnasnet_140,6024.95,169.948,1024,224,7.12
rexnetr_100,5992.24,170.876,1024,224,4.88
dla46_c,5989.01,170.968,1024,224,1.3
pit_ti_distilled_224,5978.67,171.264,1024,224,5.1
mobilenetv2_110d,5966.33,171.618,1024,224,4.52
pit_ti_224,5950.27,172.082,1024,224,4.85
regnety_008,5940.31,172.37,1024,224,6.26
resnetblur18,5929.76,172.677,1024,224,11.69
tf_efficientnet_b0,5620.98,182.161,1024,224,5.29
tf_efficientnet_b0_ns,5610.83,182.491,1024,224,5.29
tf_efficientnet_b0_ap,5603.36,182.734,1024,224,5.29
skresnet18,5578.53,183.549,1024,224,11.96
regnetz_005,5482.6,186.761,1024,224,7.12
semnasnet_140,5325.69,192.263,1024,224,6.11
mobilenetv2_140,5271.81,194.229,1024,224,6.11
resnet34,5239.3,195.434,1024,224,21.8
tv_resnet34,5236.24,195.548,1024,224,21.8
gluon_resnet34_v1b,5233.7,195.644,1024,224,21.8
ese_vovnet19b_dw,5190.19,197.284,1024,224,6.54
levit_384,5177.17,197.779,1024,224,39.13
mobilevit_xxs,5154.01,198.668,1024,256,1.27
visformer_tiny,5148.77,198.871,1024,224,10.32
hrnet_w18_small,5137.51,199.307,1024,224,13.19
nf_regnet_b0,5134.01,199.442,1024,256,8.76
seresnet34,4940.74,207.245,1024,224,21.96
mixnet_s,4921.18,208.067,1024,224,4.13
gernet_m,4881.9,209.742,1024,224,21.14
efficientnet_lite1,4817.11,212.565,1024,240,5.42
legacy_seresnet34,4790.34,213.752,1024,224,21.96
selecsls42,4787.26,213.889,1024,224,30.35
selecsls42b,4772.45,214.553,1024,224,32.46
dla46x_c,4717.05,217.071,1024,224,1.07
resnet34d,4707.81,217.499,1024,224,21.82
vit_base_patch32_224,4653.29,220.048,1024,224,88.22
vit_base_patch32_224_sam,4636.98,220.822,1024,224,88.22
pit_xs_224,4628.58,221.222,1024,224,10.62
tf_mixnet_s,4615.88,221.831,1024,224,4.13
fbnetv3_b,4595.16,222.83,1024,256,8.6
rexnetr_130,4587.31,223.212,1024,224,7.61
pit_xs_distilled_224,4586.36,223.258,1024,224,11.0
resmlp_12_distilled_224,4524.79,226.297,1024,224,15.35
resmlp_12_224,4522.51,226.411,1024,224,15.35
tf_efficientnetv2_b1,4515.62,226.756,1024,240,8.14
dla60x_c,4459.55,229.607,1024,224,1.32
tf_efficientnet_lite1,4427.03,231.295,1024,240,5.42
mixer_b32_224,4423.78,231.465,1024,224,60.29
rexnet_130,4423.43,231.482,1024,224,7.56
xcit_tiny_12_p16_224_dist,4363.62,234.654,1024,224,6.72
xcit_tiny_12_p16_224,4352.75,235.24,1024,224,6.72
resnet26,4300.48,238.1,1024,224,16.0
mobilenetv2_120d,4276.4,239.441,1024,224,5.83
efficientnet_es_pruned,4244.8,241.225,1024,224,5.44
efficientnet_es,4243.52,241.298,1024,224,5.44
repvgg_b0,4216.88,242.821,1024,224,15.82
selecsls60,4146.99,246.913,1024,224,30.67
selecsls60b,4134.81,247.64,1024,224,32.77
tf_efficientnet_es,4097.34,249.906,1024,224,5.44
fbnetv3_d,4060.14,252.196,1024,256,10.31
efficientnet_b2_pruned,4051.65,252.724,1024,260,8.31
efficientnet_b0_g16_evos,3982.5,257.113,1024,224,8.11
rexnetr_150,3947.19,259.414,1024,224,9.78
crossvit_tiny_240,3922.12,261.07,1024,240,7.01
mixer_s16_224,3902.13,262.409,1024,224,18.53
resnet26d,3896.6,262.781,1024,224,16.01
dla34,3859.49,265.307,1024,224,15.74
ecaresnet50d_pruned,3854.93,265.621,1024,224,19.94
rexnet_150,3827.99,267.492,1024,224,9.73
vit_small_patch32_384,3806.52,269.0,1024,384,22.92
nf_resnet26,3804.26,269.16,1024,224,16.0
gmixer_12_224,3797.94,269.608,1024,224,12.7
efficientnet_lite2,3793.99,269.889,1024,260,6.09
gmlp_ti16_224,3724.27,274.941,1024,224,5.87
crossvit_9_240,3711.74,275.869,1024,240,8.55
regnetx_016,3640.75,281.247,1024,224,9.19
efficientnet_cc_b0_4e,3606.59,283.912,1024,224,13.31
efficientnet_cc_b0_8e,3600.63,284.383,1024,224,24.01
crossvit_9_dagger_240,3560.19,287.614,1024,240,8.78
tf_efficientnet_b1_ap,3560.01,287.626,1024,240,7.79
tf_efficientnet_b1,3559.25,287.687,1024,240,7.79
tf_efficientnet_b1_ns,3553.74,288.134,1024,240,7.79
tf_efficientnet_lite2,3505.87,292.07,1024,260,6.09
efficientnet_b1,3481.01,294.155,1024,256,7.79
poolformer_s12,3480.88,294.166,1024,224,11.92
vit_tiny_r_s16_p8_384,3451.73,148.319,512,384,6.36
tf_efficientnetv2_b2,3443.76,297.337,1024,260,10.1
tf_efficientnet_cc_b0_8e,3407.59,300.493,1024,224,24.01
tf_efficientnet_cc_b0_4e,3402.61,300.934,1024,224,13.31
mixnet_m,3369.57,303.884,1024,224,5.01
regnety_016,3343.57,306.248,1024,224,11.2
nf_seresnet26,3326.57,307.813,1024,224,17.4
nf_ecaresnet26,3308.22,309.519,1024,224,16.0
repvgg_a2,3284.74,311.731,1024,224,28.21
gernet_l,3260.13,314.086,1024,256,31.08
tf_mixnet_m,3258.23,314.269,1024,224,5.01
resnest14d,3225.43,317.465,1024,224,10.61
efficientnet_b3_pruned,3214.49,318.545,1024,300,9.86
convnext_nano_hnf,3199.89,319.999,1024,224,15.59
skresnet34,3189.47,321.044,1024,224,22.28
convit_tiny,3117.16,328.49,1024,224,5.71
resnext26ts,3098.65,330.453,1024,256,10.3
legacy_seresnext26_32x4d,3086.27,331.78,1024,224,16.79
nf_regnet_b1,3049.58,335.771,1024,288,10.22
resnet26t,3040.5,336.774,1024,256,16.01
seresnext26ts,3026.32,338.35,1024,256,10.39
eca_resnext26ts,3023.05,338.719,1024,256,10.3
gcresnext26ts,2976.22,344.049,1024,256,10.48
ecaresnet101d_pruned,2954.94,346.526,1024,224,24.88
nf_regnet_b2,2933.65,349.041,1024,272,14.31
mobilevit_xs,2913.13,175.744,512,256,2.32
seresnext26tn_32x4d,2898.81,353.236,1024,224,16.81
seresnext26t_32x4d,2897.48,353.398,1024,224,16.81
ecaresnext26t_32x4d,2893.22,353.918,1024,224,15.41
ecaresnext50t_32x4d,2891.83,354.088,1024,224,15.41
seresnext26d_32x4d,2884.91,354.937,1024,224,16.81
ecaresnetlight,2878.45,355.733,1024,224,30.16
pit_s_224,2872.59,356.46,1024,224,23.46
deit_small_patch16_224,2853.43,358.855,1024,224,22.05
pit_s_distilled_224,2851.86,359.052,1024,224,24.04
vit_small_patch16_224,2845.41,359.865,1024,224,22.05
tf_efficientnet_b2_ap,2814.51,363.814,1024,260,9.11
tf_efficientnet_b2_ns,2814.31,363.839,1024,260,9.11
coat_lite_tiny,2814.08,363.872,1024,224,5.72
tf_efficientnet_b2,2813.96,363.886,1024,260,9.11
rexnetr_200,2808.62,182.283,512,224,16.52
deit_small_distilled_patch16_224,2801.73,365.478,1024,224,22.44
tresnet_m,2787.92,367.287,1024,224,31.39
resnetv2_50,2780.22,368.303,1024,224,25.55
eca_botnext26ts_256,2766.45,370.137,1024,256,10.59
rexnet_200,2763.52,185.259,512,224,16.37
vit_base2_patch32_256,2752.1,372.066,1024,256,119.46
botnet26t_256,2750.58,372.273,1024,256,12.49
halonet26t,2727.12,375.475,1024,256,12.48
eca_halonext26ts,2721.42,376.262,1024,256,10.76
swsl_resnet50,2693.51,380.159,1024,224,25.56
tv_resnet50,2687.71,380.98,1024,224,25.56
efficientnet_b0_gn,2686.21,381.194,1024,224,5.29
ssl_resnet50,2684.8,381.394,1024,224,25.56
gluon_resnet50_v1b,2682.34,381.744,1024,224,25.56
resnet50,2681.21,381.904,1024,224,25.56
vit_small_resnet26d_224,2675.44,382.728,1024,224,63.61
efficientnet_b2a,2654.03,385.816,1024,288,9.11
efficientnet_b2,2649.9,386.418,1024,288,9.11
coat_lite_mini,2646.6,386.899,1024,224,11.01
hrnet_w18_small_v2,2638.47,388.089,1024,224,15.6
resnetv2_50t,2621.75,390.565,1024,224,25.57
vovnet39a,2620.98,390.68,1024,224,22.6
resnetv2_50d,2613.09,391.86,1024,224,25.57
bat_resnext26ts,2594.52,394.665,1024,256,10.73
resnet32ts,2591.72,395.092,1024,256,17.96
efficientnet_em,2587.13,395.792,1024,240,6.9
cspresnet50,2561.78,399.709,1024,256,21.62
mixnet_l,2560.63,199.939,512,224,7.33
resnet33ts,2550.89,401.417,1024,256,19.68
dpn68b,2548.03,401.866,1024,224,12.61
gluon_resnet50_v1c,2543.04,402.654,1024,224,25.58
ese_vovnet39b,2535.01,403.931,1024,224,24.57
eca_vovnet39b,2533.56,404.162,1024,224,22.6
cspresnext50,2530.86,404.592,1024,224,20.57
legacy_seresnet50,2527.3,405.162,1024,224,28.09
vgg11_bn,2524.72,202.784,512,224,132.87
tf_efficientnet_em,2523.81,405.723,1024,240,6.9
resnet50t,2521.66,406.069,1024,224,25.57
resnet50d,2517.37,406.76,1024,224,25.58
gluon_resnet50_v1d,2514.4,407.243,1024,224,25.58
dpn68,2513.01,407.467,1024,224,12.61
selecsls84,2490.46,411.155,1024,224,50.95
seresnet33ts,2489.53,411.308,1024,256,19.78
eca_resnet33ts,2483.09,412.378,1024,256,19.68
lambda_resnet26t,2479.99,412.893,1024,256,10.96
tf_mixnet_l,2478.99,206.524,512,224,7.33
twins_svt_small,2475.31,413.674,1024,224,24.06
gcresnet33ts,2439.7,419.711,1024,256,19.88
cspresnet50w,2418.46,423.398,1024,256,28.12
seresnet50,2407.37,425.348,1024,224,28.09
cspresnet50d,2400.89,426.492,1024,256,21.64
dla60,2376.63,430.848,1024,224,22.04
densenet121,2346.76,436.333,1024,224,7.98
resnest26d,2346.08,436.46,1024,224,17.07
tv_densenet121,2345.79,436.514,1024,224,7.98
xcit_tiny_24_p16_224_dist,2334.54,438.616,1024,224,12.12
xcit_nano_12_p16_384_dist,2332.68,438.968,1024,384,3.05
xcit_tiny_24_p16_224,2328.43,439.766,1024,224,12.12
haloregnetz_b,2320.38,441.294,1024,224,11.68
resmlp_24_224,2308.85,443.498,1024,224,30.02
resmlp_24_distilled_224,2308.13,443.636,1024,224,30.02
resnetaa50d,2295.34,446.109,1024,224,25.58
seresnet50t,2282.97,448.526,1024,224,28.1
efficientnet_cc_b1_8e,2282.2,448.676,1024,240,39.72
convnext_tiny,2276.32,449.828,1024,224,28.59
res2net50_48w_2s,2265.43,451.999,1024,224,25.29
resnetblur50,2265.02,452.08,1024,224,25.56
efficientnet_lite3,2261.44,226.393,512,300,8.2
ecaresnet50d,2260.92,452.9,1024,224,25.58
efficientnet_b0_g8_gn,2259.05,453.276,1024,224,6.56
densenet121d,2243.46,456.425,1024,224,8.0
resnetrs50,2241.6,456.804,1024,224,35.69
mobilevit_s,2240.37,228.522,512,256,5.58
regnetx_032,2201.52,465.118,1024,224,15.3
visformer_small,2194.82,466.539,1024,224,40.22
gluon_resnet50_v1s,2193.42,466.838,1024,224,25.68
tf_efficientnet_cc_b1_8e,2188.26,467.941,1024,240,39.72
vit_base_resnet26d_224,2188.18,467.954,1024,224,101.4
resnetblur50d,2150.42,476.173,1024,224,25.58
gluon_inception_v3,2150.17,476.225,1024,299,23.83
adv_inception_v3,2148.48,476.602,1024,299,23.83
vovnet57a,2148.25,476.651,1024,224,36.64
tf_inception_v3,2147.17,476.894,1024,299,23.83
inception_v3,2146.78,476.978,1024,299,23.83
densenetblur121d,2133.31,479.992,1024,224,8.0
cspresnext50_iabn,2129.31,480.895,1024,256,20.57
semobilevit_s,2105.83,243.122,512,256,5.74
seresnetaa50d,2088.02,490.403,1024,224,28.11
cspdarknet53_iabn,2087.26,490.582,1024,256,27.64
swsl_resnext50_32x4d,2080.34,492.214,1024,224,25.03
convnext_tiny_hnf,2075.56,493.349,1024,224,28.59
ese_vovnet57b,2074.63,493.57,1024,224,38.61
tf_efficientnet_lite3,2074.49,246.795,512,300,8.2
resnext50_32x4d,2073.97,493.725,1024,224,25.03
ssl_resnext50_32x4d,2073.23,493.902,1024,224,25.03
gluon_resnext50_32x4d,2072.3,494.125,1024,224,25.03
tv_resnext50_32x4d,2055.26,498.221,1024,224,25.03
res2net50_26w_4s,2037.79,502.491,1024,224,25.7
twins_pcpvt_small,2036.4,502.835,1024,224,24.11
xcit_small_12_p16_224_dist,2021.26,506.599,1024,224,26.25
tf_efficientnetv2_b3,2020.91,506.688,1024,300,14.36
xcit_small_12_p16_224,2020.4,506.814,1024,224,26.25
nf_seresnet50,2015.9,507.948,1024,224,28.09
skresnet50,2014.26,508.362,1024,224,25.8
nf_ecaresnet50,2005.54,510.572,1024,224,25.56
regnetx_040,2003.23,511.16,1024,224,22.12
efficientnetv2_rw_t,1999.8,512.038,1024,288,13.65
sehalonet33ts,1991.52,257.078,512,256,13.69
fbnetv3_g,1991.44,514.187,1024,288,16.62
dla60x,1986.19,515.547,1024,224,17.35
gcresnet50t,1982.14,516.599,1024,256,25.9
resnext50d_32x4d,1976.72,518.018,1024,224,25.05
lambda_resnet26rpt_256,1950.98,262.42,512,256,10.99
gmixer_24_224,1937.61,528.474,1024,224,24.72
gc_efficientnetv2_rw_t,1928.42,530.993,1024,288,13.68
res2net50_14w_8s,1920.35,533.22,1024,224,25.06
skresnet50d,1919.46,533.468,1024,224,25.82
densenet169,1916.46,534.305,1024,224,14.15
dla60_res2net,1907.38,536.848,1024,224,20.85
gcresnext50ts,1902.33,538.276,1024,256,15.67
seresnext50_32x4d,1902.13,538.331,1024,224,27.56
gluon_seresnext50_32x4d,1901.68,538.457,1024,224,27.56
res2next50,1896.89,539.818,1024,224,24.67
legacy_seresnext50_32x4d,1896.31,539.984,1024,224,27.56
repvgg_b1g4,1884.44,543.384,1024,224,39.97
resnest50d_1s4x24d,1879.81,544.722,1024,224,25.68
crossvit_small_240,1855.05,551.992,1024,240,26.86
nf_regnet_b3,1852.1,552.873,1024,320,18.59
darknet53,1847.62,277.102,512,256,41.61
mixnet_xl,1839.34,278.346,512,224,11.9
dla60_res2next,1829.2,559.793,1024,224,17.03
swin_tiny_patch4_window7_224,1820.74,562.394,1024,224,28.29
cspdarknet53,1804.24,283.762,512,256,27.64
poolformer_s24,1803.36,567.817,1024,224,21.39
vit_small_r26_s32_224,1799.77,568.949,1024,224,36.43
xcit_nano_12_p8_224_dist,1796.05,570.128,1024,224,3.05
xcit_nano_12_p8_224,1795.49,570.304,1024,224,3.05
regnetz_b16,1791.93,571.438,1024,288,9.72
ecaresnet26t,1786.82,573.073,1024,320,16.01
convnext_tiny_hnfd,1744.82,586.868,1024,224,28.63
gmlp_s16_224,1741.6,587.951,1024,224,19.42
sebotnet33ts_256,1718.54,223.433,384,256,13.7
crossvit_15_240,1711.44,598.314,1024,240,27.53
resnetv2_101,1693.03,604.82,1024,224,44.54
vit_base_resnet50d_224,1670.98,612.798,1024,224,110.97
swin_s3_tiny_224,1660.14,616.801,1024,224,28.33
repvgg_b1,1657.85,617.654,1024,224,57.42
tv_resnet101,1656.03,618.332,1024,224,44.55
gluon_resnet101_v1b,1653.8,619.167,1024,224,44.55
resnet101,1652.45,619.673,1024,224,44.55
tf_efficientnet_b3_ap,1649.82,310.323,512,300,12.23
tf_efficientnet_b3,1649.61,310.363,512,300,12.23
tf_efficientnet_b3_ns,1649.37,310.407,512,300,12.23
crossvit_15_dagger_240,1648.34,621.218,1024,240,28.21
lambda_resnet50ts,1639.74,624.475,1024,256,21.54
resnetv2_101d,1628.19,628.906,1024,224,44.56
efficientnet_b3,1614.3,317.152,512,320,12.23
efficientnet_b3a,1613.57,317.296,512,320,12.23
gluon_resnet101_v1c,1597.11,641.145,1024,224,44.57
resnest50d,1586.43,645.46,1024,224,27.48
gluon_resnet101_v1d,1584.97,646.054,1024,224,44.57
wide_resnet50_2,1583.06,646.835,1024,224,68.88
cait_xxs24_224,1580.68,647.808,1024,224,11.96
dla102,1573.96,650.576,1024,224,33.27
resnetv2_50x1_bit_distilled,1561.81,655.635,1024,224,25.55
res2net50_26w_6s,1556.3,657.955,1024,224,37.05
vit_large_patch32_224,1551.79,659.869,1024,224,306.54
resmlp_36_224,1549.07,661.03,1024,224,44.69
regnetx_080,1548.92,661.091,1024,224,39.57
resmlp_36_distilled_224,1548.49,661.277,1024,224,44.69
halonet50ts,1542.39,663.891,1024,256,22.73
legacy_seresnet101,1528.82,669.783,1024,224,49.33
ese_vovnet39b_evos,1523.51,672.121,1024,224,24.58
coat_lite_small,1509.49,678.361,1024,224,19.84
vgg13_bn,1504.09,340.392,512,224,133.05
xcit_tiny_12_p16_384_dist,1501.95,681.763,1024,384,6.72
resnetaa101d,1496.92,684.059,1024,224,44.57
swin_v2_cr_tiny_224,1495.36,684.765,1024,224,28.33
densenet201,1488.38,687.985,1024,224,20.01
seresnet101,1484.39,689.83,1024,224,49.33
vit_tiny_patch16_384,1479.06,692.319,1024,384,5.79
lamhalobotnet50ts_256,1474.4,694.504,1024,256,22.57
swin_v2_cr_tiny_ns_224,1471.62,695.817,1024,224,28.33
vit_base_patch32_384,1470.27,696.459,1024,384,88.3
vit_base_r26_s32_224,1467.48,697.779,1024,224,101.38
gluon_resnet101_v1s,1452.66,704.901,1024,224,44.67
regnetx_064,1450.48,352.975,512,224,26.21
mixer_b16_224,1448.87,706.746,1024,224,59.88
nf_resnet101,1448.19,707.076,1024,224,44.55
mixer_b16_224_miil,1446.14,708.08,1024,224,59.88
resnetv2_50d_frn,1444.18,709.041,1024,224,25.59
resnetblur101d,1433.31,714.414,1024,224,44.57
nf_resnet50,1425.83,718.163,1024,288,25.56
mixer_l32_224,1425.39,718.388,1024,224,206.94
ecaresnet101d,1423.43,719.375,1024,224,44.57
hrnet_w18,1416.64,722.817,1024,224,21.3
convnext_small,1412.23,725.081,1024,224,50.22
tresnet_l,1401.27,730.75,1024,224,55.99
twins_pcpvt_base,1398.04,732.441,1024,224,43.83
regnety_032,1384.84,739.421,1024,288,19.44
nest_tiny,1381.77,370.526,512,224,17.06
resnet50_gn,1377.54,743.338,1024,224,25.56
resnet51q,1366.14,749.545,1024,288,35.7
resnetv2_50d_evob,1365.49,749.897,1024,224,25.59
jx_nest_tiny,1357.18,377.241,512,224,17.06
botnet50ts_256,1354.82,377.899,512,256,22.74
xception,1347.6,379.923,512,299,22.86
dla102x,1333.01,768.169,1024,224,26.31
convit_small,1329.45,770.231,1024,224,27.78
halo2botnet50ts_256,1327.73,385.607,512,256,22.64
skresnext50_32x4d,1314.86,778.776,1024,224,27.48
swsl_resnext101_32x4d,1313.2,779.762,1024,224,44.18
ssl_resnext101_32x4d,1309.86,781.751,1024,224,44.18
gluon_resnext101_32x4d,1309.44,781.999,1024,224,44.18
resnext101_32x4d,1308.3,782.684,1024,224,44.18
repvgg_b2g4,1287.88,795.093,1024,224,61.76
res2net50_26w_8s,1277.42,801.601,1024,224,48.4
res2net101_26w_4s,1275.83,802.597,1024,224,45.21
resnest50d_4s2x40d,1267.83,807.666,1024,224,30.42
nf_seresnet101,1246.67,821.372,1024,224,49.33
twins_svt_base,1242.25,824.296,1024,224,56.07
nf_ecaresnet101,1240.12,825.715,1024,224,44.55
vgg16_bn,1239.17,413.169,512,224,138.37
resnet61q,1236.58,828.077,1024,288,36.85
eca_nfnet_l0,1234.82,829.257,1024,288,24.14
nfnet_l0,1234.39,829.546,1024,288,35.07
hrnet_w32,1225.86,835.318,1024,224,41.23
xception41p,1219.47,419.841,512,299,26.91
poolformer_s36,1217.85,840.81,1024,224,30.86
ese_vovnet99b_iabn,1214.4,843.2,1024,224,63.2
crossvit_18_240,1210.54,845.892,1024,240,43.27
regnetv_040,1210.02,423.122,512,288,20.64
regnety_040,1209.84,423.186,512,288,20.65
hrnet_w30,1208.97,846.988,1024,224,37.71
dpn92,1205.79,849.224,1024,224,37.67
gluon_seresnext101_32x4d,1198.19,854.61,1024,224,48.96
seresnext101_32x4d,1197.93,854.792,1024,224,48.96
legacy_seresnext101_32x4d,1196.55,855.783,1024,224,48.96
efficientnet_el,1181.26,433.424,512,300,10.59
efficientnet_el_pruned,1179.3,434.142,512,300,10.59
ese_vovnet99b,1178.02,869.24,1024,224,63.2
resnetv2_152,1172.64,873.226,1024,224,60.19
crossvit_18_dagger_240,1169.98,875.211,1024,240,44.27
tf_efficientnet_el,1156.16,442.832,512,300,10.59
tv_resnet152,1155.28,886.354,1024,224,60.19
resnet152,1153.46,887.752,1024,224,60.19
gluon_resnet152_v1b,1153.39,887.802,1024,224,60.19
xcit_tiny_12_p8_224_dist,1148.77,891.372,1024,224,6.71
xcit_tiny_12_p8_224,1148.55,891.542,1024,224,6.71
vit_small_resnet50d_s16_224,1140.9,897.523,1024,224,57.53
resnetv2_152d,1140.85,897.561,1024,224,60.2
mixnet_xxl,1136.05,338.002,384,224,23.96
ecaresnet50t,1133.49,903.391,1024,320,25.57
regnetz_c16,1132.12,452.237,512,320,13.46
repvgg_b2,1129.63,906.478,1024,224,89.02
gluon_resnet152_v1c,1126.87,908.694,1024,224,60.21
vit_base_patch16_224_miil,1124.8,910.37,1024,224,86.54
gluon_resnet152_v1d,1122.21,912.468,1024,224,60.21
volo_d1_224,1121.56,912.997,1024,224,26.63
swin_small_patch4_window7_224,1117.13,916.62,1024,224,49.61
regnety_040s_gn,1113.45,919.647,1024,224,20.65
inception_v4,1099.65,931.189,1024,299,42.68
vit_base_patch16_224_sam,1089.03,940.269,1024,224,86.57
xception41,1089.03,470.133,512,299,26.97
vit_base_patch16_224,1089.0,940.3,1024,224,86.57
deit_base_patch16_224,1088.05,941.117,1024,224,86.57
xcit_small_24_p16_224_dist,1079.16,948.867,1024,224,47.67
xcit_small_24_p16_224,1078.82,949.17,1024,224,47.67
densenet161,1076.84,950.914,1024,224,28.68
convmixer_1024_20_ks9_p14,1075.95,951.707,1024,224,24.38
nfnet_f0,1075.06,952.487,1024,256,71.49
deit_base_distilled_patch16_224,1073.74,953.659,1024,224,87.34
dla169,1071.22,955.906,1024,224,53.39
vgg19_bn,1060.19,482.919,512,224,143.68
cait_xxs36_224,1058.79,967.13,1024,224,17.3
tnt_s_patch16_224,1056.28,969.422,1024,224,23.76
legacy_seresnet152,1054.79,970.791,1024,224,66.82
gluon_resnet152_v1s,1052.41,972.991,1024,224,60.32
regnetx_120,1048.48,488.312,512,224,46.11
seresnet152,1033.07,991.203,1024,224,66.82
tresnet_xl,1032.49,991.76,1024,224,78.44
efficientnet_lite4,1029.33,373.047,384,380,13.01
beit_base_patch16_224,1003.86,1020.053,1024,224,86.53
regnety_120,1003.65,510.126,512,224,51.82
repvgg_b3g4,997.42,1026.637,1024,224,83.83
twins_pcpvt_large,995.78,1028.325,1024,224,60.99
convnext_base,984.44,1040.164,1024,224,88.59
convnext_base_in22ft1k,984.35,1040.256,1024,224,88.59
coat_tiny,976.27,1048.875,1024,224,5.5
tf_efficientnet_lite4,967.59,396.848,384,380,13.01
pit_b_224,954.91,536.164,512,224,73.76
dm_nfnet_f0,948.25,1079.874,1024,256,71.49
pit_b_distilled_224,947.46,540.381,512,224,74.79
vit_small_patch16_36x1_224,919.98,1113.057,1024,224,64.67
wide_resnet101_2,917.07,1116.581,1024,224,126.89
swin_v2_cr_small_224,915.96,1117.937,1024,224,49.7
dla102x2,910.31,562.434,512,224,41.28
resnetv2_50d_gn,909.47,1125.915,1024,288,25.57
efficientnetv2_s,905.47,1130.894,1024,384,21.46
vit_small_patch16_18x2_224,899.06,1138.948,1024,224,64.67
tf_efficientnetv2_s_in21ft1k,889.42,1151.294,1024,384,21.46
tf_efficientnetv2_s,889.32,1151.431,1024,384,21.46
xception65p,886.31,577.66,512,299,39.82
nest_small,881.75,580.652,512,224,38.35
twins_svt_large,880.33,1163.185,1024,224,99.27
repvgg_b3,878.37,1165.779,1024,224,123.09
resnetrs101,877.57,1166.842,1024,288,63.62
jx_nest_small,871.53,587.458,512,224,38.35
efficientnetv2_rw_s,866.51,1181.735,1024,384,23.94
dpn98,862.25,1187.578,1024,224,61.57
ens_adv_inception_resnet_v2,856.37,1195.737,1024,299,55.84
inception_resnet_v2,855.57,1196.844,1024,299,55.84
nf_regnet_b4,854.46,1198.403,1024,384,30.21
regnetz_b16_evos,853.4,599.942,512,288,9.74
regnetx_160,848.43,603.455,512,224,54.28
regnetz_d8,845.88,1210.552,1024,320,23.37
cait_s24_224,834.81,1226.608,1024,224,46.92
gluon_resnext101_64x4d,834.02,1227.777,1024,224,83.46
resnet200,828.19,1236.414,1024,224,64.67
regnetz_040,826.88,464.381,384,320,27.12
regnetz_040h,823.24,466.438,384,320,28.94
efficientnet_b4,820.41,468.046,384,384,19.34
swin_s3_small_224,817.53,626.263,512,224,49.74
hrnet_w40,816.49,1254.128,1024,224,57.56
poolformer_m36,815.39,1255.826,1024,224,56.17
swsl_resnext101_32x8d,805.27,1271.611,1024,224,88.79
regnety_064,803.23,637.411,512,288,30.58
ssl_resnext101_32x8d,802.86,1275.43,1024,224,88.79
xcit_tiny_24_p16_384_dist,802.73,1275.631,1024,384,12.12
ig_resnext101_32x8d,802.17,1276.521,1024,224,88.79
resnext101_32x8d,802.06,1276.704,1024,224,88.79
regnetv_064,800.43,639.64,512,288,30.58
resnetv2_50d_evos,798.97,640.813,512,288,25.59
gluon_xception65,797.15,642.277,512,299,39.92
resnet101d,796.13,1286.203,1024,320,44.57
xception65,795.21,643.84,512,299,39.92
resnest101e,791.92,646.513,512,256,48.28
swin_base_patch4_window7_224,791.65,1293.482,1024,224,87.77
gluon_seresnext101_64x4d,787.65,1300.055,1024,224,88.23
coat_mini,785.23,1304.064,1024,224,10.34
tf_efficientnet_b4_ap,782.92,490.459,384,380,19.34
tf_efficientnet_b4,782.13,490.953,384,380,19.34
tf_efficientnet_b4_ns,782.09,490.976,384,380,19.34
regnety_080,767.29,667.271,512,288,39.18
hrnet_w44,759.24,1348.7,1024,224,67.06
crossvit_base_240,756.03,677.208,512,240,105.03
xcit_medium_24_p16_224_dist,748.3,1368.42,1024,224,84.4
xcit_medium_24_p16_224,747.93,1369.089,1024,224,84.4
gmlp_b16_224,744.43,1375.538,1024,224,73.08
hrnet_w48,733.55,1395.939,1024,224,77.47
tresnet_m_448,727.54,1407.473,1024,448,31.39
vit_large_r50_s32_224,726.03,1410.402,1024,224,328.99
regnetz_d32,721.75,1418.755,1024,320,27.58
vit_small_patch16_384,684.33,748.167,512,384,22.2
tnt_b_patch16_224,677.0,1512.541,1024,224,65.41
xcit_small_12_p16_384_dist,675.76,1515.313,1024,384,26.25
convit_base,673.78,1519.763,1024,224,86.54
swin_s3_base_224,663.17,1544.087,1024,224,71.13
swin_v2_cr_base_224,651.94,1570.69,1024,224,87.88
densenet264d_iabn,647.21,1582.161,1024,224,72.74
efficientnet_b3_gn,646.96,395.683,256,320,11.73
dpn131,635.27,1611.889,1024,224,79.25
densenet264,628.46,1629.36,1024,224,72.69
nest_base,627.45,815.992,512,224,67.72
volo_d2_224,626.17,1635.329,1024,224,58.68
jx_nest_base,620.43,825.215,512,224,67.72
poolformer_m48,615.67,1663.217,1024,224,73.47
vit_small_r26_s32_384,611.26,628.196,384,384,36.47
xcit_nano_12_p8_384_dist,610.06,1678.498,1024,384,3.05
xcit_tiny_24_p8_224,603.0,1698.15,1024,224,12.11
xcit_tiny_24_p8_224_dist,602.22,1700.353,1024,224,12.11
xception71,600.16,853.086,512,299,42.34
hrnet_w64,599.02,1709.451,1024,224,128.06
vit_base_r50_s16_224,598.25,1711.642,1024,224,98.66
legacy_senet154,582.07,1759.212,1024,224,115.09
senet154,582.01,1759.392,1024,224,115.09
gluon_senet154,581.94,1759.609,1024,224,115.09
dpn107,578.97,1768.627,1024,224,86.92
eca_nfnet_l1,575.61,1778.965,1024,320,41.41
seresnet200d,562.84,1819.333,1024,256,71.86
resnet152d,561.29,1824.359,1024,320,60.21
ecaresnet200d,559.73,1829.437,1024,256,64.69
convnext_large_in22ft1k,546.71,936.5,512,224,197.77
convnext_large,546.09,937.561,512,224,197.77
regnetz_c16_evos,539.7,948.669,512,320,13.49
regnety_320,534.72,957.496,512,224,145.05
regnety_160,523.72,733.2,384,288,83.59
efficientnet_b3_g8_gn,520.23,492.079,256,320,14.25
xcit_small_12_p8_224,514.94,1988.547,1024,224,26.21
xcit_small_12_p8_224_dist,514.63,1989.761,1024,224,26.21
halonet_h1,507.89,504.03,256,256,8.1
resnext101_64x4d,505.79,1012.259,512,288,83.46
seresnet152d,503.07,2035.474,1024,320,66.84
resnetrs152,499.61,2049.599,1024,320,86.62
vit_large_patch32_384,494.68,2070.028,1024,384,306.63
regnetx_320,468.44,819.734,384,224,107.81
mixer_l16_224,464.51,2204.436,1024,224,208.2
seresnext101_32x8d,461.67,1109.006,512,288,93.57
swin_large_patch4_window7_224,454.97,1125.326,512,224,196.53
regnetz_e8,451.54,1133.877,512,320,57.7
efficientnetv2_m,442.19,2315.716,1024,416,54.14
seresnet269d,440.45,2324.895,1024,256,113.67
volo_d3_224,438.95,2332.847,1024,224,86.33
xcit_large_24_p16_224,432.87,2365.575,1024,224,189.1
xcit_large_24_p16_224_dist,432.7,2366.505,1024,224,189.1
efficientnet_b5,411.07,622.743,256,456,30.39
efficientnetv2_rw_m,406.88,1258.338,512,416,53.24
resnet200d,404.01,2534.588,1024,320,64.69
tf_efficientnet_b5,395.61,647.082,256,456,30.39
tf_efficientnet_b5_ap,395.52,647.242,256,456,30.39
tf_efficientnet_b5_ns,395.47,647.322,256,456,30.39
resnetv2_50x1_bitm,392.67,1303.884,512,448,25.55
xcit_tiny_12_p8_384_dist,390.32,2623.487,1024,384,6.71
swin_v2_cr_large_224,385.91,1326.718,512,224,196.68
swsl_resnext101_32x16d,375.78,1362.484,512,224,194.03
ig_resnext101_32x16d,373.86,1369.478,512,224,194.03
ssl_resnext101_32x16d,373.64,1370.28,512,224,194.03
regnetz_d8_evos,373.09,1372.306,512,320,23.46
swin_v2_cr_tiny_384,364.64,702.039,256,384,28.33
tresnet_l_448,361.5,2832.633,1024,448,55.99
convnext_xlarge_in22ft1k,360.86,1418.822,512,224,350.2
xcit_small_24_p16_384_dist,360.65,2839.301,1024,384,47.67
resnetrs200,358.31,2857.856,1024,320,93.21
nfnet_f1,357.09,2867.576,1024,320,132.63
vit_large_patch16_224,356.78,2870.107,1024,224,304.33
crossvit_15_dagger_408,354.9,721.306,256,408,28.5
vit_base_patch16_18x2_224,348.19,2940.87,1024,224,256.73
tf_efficientnetv2_m_in21ft1k,347.38,1473.892,512,480,54.14
tf_efficientnetv2_m,346.86,1476.074,512,480,54.14
dm_nfnet_f1,342.33,1495.606,512,320,132.63
convnext_base_384_in22ft1k,336.37,1141.59,384,384,88.59
beit_large_patch16_224,330.46,3098.668,1024,224,304.43
volo_d1_384,293.12,1746.724,512,384,26.78
convmixer_768_32,290.99,3518.992,1024,224,21.11
eca_nfnet_l2,282.42,1812.878,512,384,56.72
volo_d4_224,281.7,3635.003,1024,224,192.96
resnetv2_152x2_bit_teacher,280.73,1823.773,512,224,236.34
vit_base_patch16_384,280.43,1369.296,384,384,86.86
deit_base_patch16_384,280.4,1369.456,384,384,86.86
deit_base_distilled_patch16_384,276.56,1388.495,384,384,87.63
xcit_small_24_p8_224,269.73,3796.43,1024,224,47.63
tresnet_xl_448,269.71,1898.302,512,448,78.44
xcit_small_24_p8_224_dist,269.67,3797.157,1024,224,47.63
resnest200e,265.14,1931.055,512,320,70.2
cait_xxs24_384,264.07,3877.737,1024,384,12.03
vit_large_patch14_224,262.13,3906.369,1024,224,304.2
crossvit_18_dagger_408,260.17,983.968,256,408,44.61
xcit_medium_24_p16_384_dist,254.44,2012.271,512,384,84.4
nasnetalarge,254.21,1510.535,384,331,88.75
pnasnet5large,251.02,1529.719,384,331,86.06
resnetv2_101x1_bitm,246.55,2076.684,512,448,44.54
beit_base_patch16_384,241.2,1592.025,384,384,86.74
vit_large_r50_s32_384,240.58,1596.142,384,384,329.09
efficientnet_b6,239.36,534.745,128,528,43.04
ecaresnet269d,233.52,4385.094,1024,352,102.09
tf_efficientnet_b6_ns,231.37,553.209,128,528,43.04
tf_efficientnet_b6,231.23,553.545,128,528,43.04
tf_efficientnet_b6_ap,231.04,553.998,128,528,43.04
resnetrs270,227.52,4500.663,1024,352,129.86
swin_v2_cr_small_384,224.2,1141.845,256,384,49.7
swin_base_patch4_window12_384,211.77,906.647,192,384,87.9
resmlp_big_24_224,209.16,4895.869,1024,224,129.14
resmlp_big_24_distilled_224,208.94,4900.863,1024,224,129.14
resmlp_big_24_224_in22ft1k,208.92,4901.346,1024,224,129.14
xcit_tiny_24_p8_384_dist,204.37,5010.586,1024,384,12.11
nfnet_f2,200.76,5100.492,1024,352,193.78
tf_efficientnetv2_l,199.73,1922.595,384,480,118.52
efficientnetv2_l,198.4,2580.63,512,480,118.52
tf_efficientnetv2_l_in21ft1k,196.98,2599.257,512,480,118.52
dm_nfnet_f2,194.75,2628.942,512,352,193.78
xcit_medium_24_p8_224,189.81,2697.371,512,224,84.32
xcit_medium_24_p8_224_dist,189.81,2697.462,512,224,84.32
volo_d5_224,187.06,5474.175,1024,224,295.46
convnext_large_384_in22ft1k,186.8,1370.399,256,384,197.77
cait_xs24_384,184.72,2771.758,512,384,26.67
vit_base_patch8_224,183.25,1396.951,256,224,86.58
cait_xxs36_384,176.62,5797.903,1024,384,17.37
vit_base_r50_s16_384,174.09,2205.77,384,384,98.95
vit_base_resnet50_384,174.05,2206.21,384,384,98.95
xcit_small_12_p8_384_dist,173.11,2957.56,512,384,26.21
swin_v2_cr_huge_224,170.28,2255.094,384,224,657.83
convmixer_1536_20,167.4,6117.1,1024,224,51.63
volo_d2_384,164.47,2334.792,384,384,58.87
swin_v2_cr_base_384,160.01,1199.906,192,384,87.88
eca_nfnet_l3,159.42,3211.652,512,448,72.04
resnetrs350,151.88,3371.146,512,384,163.96
xcit_large_24_p16_384_dist,147.05,3481.712,512,384,189.1
ig_resnext101_32x32d,146.83,1743.474,256,224,468.53
cait_s24_384,142.26,3598.927,512,384,47.06
vit_huge_patch14_224,141.6,7231.567,1024,224,632.05
efficientnet_b7,139.66,687.357,96,600,66.35
tf_efficientnet_b7,135.86,706.608,96,600,66.35
tf_efficientnet_b7_ap,135.79,706.955,96,600,66.35
tf_efficientnet_b7_ns,135.74,707.2,96,600,66.35
efficientnetv2_xl,127.77,3005.388,384,512,208.12
tf_efficientnetv2_xl_in21ft1k,127.07,3021.991,384,512,208.12
swin_large_patch4_window12_384,125.28,1021.736,128,384,196.74
resnest269e,123.52,3108.719,384,416,110.93
convnext_xlarge_384_in22ft1k,123.3,1557.136,192,384,350.2
xcit_large_24_p8_224,110.05,4652.475,512,224,188.93
xcit_large_24_p8_224_dist,109.94,4656.907,512,224,188.93
nfnet_f3,109.46,4677.461,512,416,254.92
resnetrs420,108.78,4706.738,512,416,191.89
dm_nfnet_f3,98.93,5175.208,512,416,254.92
swin_v2_cr_large_384,98.1,1304.756,128,384,196.68
resnetv2_152x2_bit_teacher_384,97.22,2633.19,256,384,236.34
cait_s36_384,95.27,5374.225,512,384,68.37
vit_large_patch16_384,94.74,2702.22,256,384,304.72
resnetv2_50x3_bitm,94.62,1352.761,128,448,217.32
vit_giant_patch14_224,92.89,5511.692,512,224,1012.61
xcit_small_24_p8_384_dist,90.84,4227.257,384,384,47.63
ig_resnext101_32x48d,87.18,2202.253,192,224,828.41
efficientnet_b8,83.91,1144.078,96,672,87.41
beit_large_patch16_384,82.64,3097.749,256,384,305.0
tf_efficientnet_b8_ap,82.27,1166.925,96,672,87.41
tf_efficientnet_b8,82.25,1167.133,96,672,87.41
volo_d3_448,72.26,2657.14,192,448,86.63
resnetv2_152x2_bitm,71.6,2681.389,192,448,236.34
xcit_medium_24_p8_384_dist,64.15,3990.849,256,384,84.32
nfnet_f4,60.24,6374.659,384,512,316.07
dm_nfnet_f4,59.09,4332.344,256,512,316.07
resnetv2_101x3_bitm,58.2,2199.411,128,448,387.93
vit_gigantic_patch14_224,56.14,9120.818,512,224,1844.44
volo_d4_448,52.76,2425.859,128,448,193.41
swin_v2_cr_giant_224,49.04,2610.23,128,224,2598.76
tf_efficientnet_l2_ns_475,46.41,1378.993,64,475,480.31
nfnet_f5,44.14,5799.172,256,544,377.21
swin_v2_cr_huge_384,43.59,1468.203,64,384,657.94
dm_nfnet_f5,39.84,6426.216,256,544,377.21
xcit_large_24_p8_384_dist,36.78,5220.859,192,384,188.93
volo_d5_448,36.57,3500.029,128,448,295.91
nfnet_f6,34.59,7401.234,256,576,438.36
beit_large_patch16_512,33.3,2882.954,96,512,305.67
cait_m36_384,31.36,8162.98,256,384,271.22
dm_nfnet_f6,31.17,8214.193,256,576,438.36
nfnet_f7,26.91,9514.003,256,608,499.5
volo_d5_512,25.64,4991.614,128,512,296.09
resnetv2_152x4_bitm,18.58,3444.83,64,480,936.53
efficientnet_l2,16.55,1450.295,24,800,480.31
tf_efficientnet_l2_ns,16.36,1467.093,24,800,480.31
swin_v2_cr_giant_384,13.63,1760.856,24,384,2598.76
cait_m48_448,13.35,9584.614,128,448,356.46
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt112-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,49285.12,20.767,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,43905.96,23.312,1024,224,0.03,0.92,1.59
lcnet_035,40961.84,24.988,1024,224,0.03,1.04,1.64
lcnet_050,36451.18,28.081,1024,224,0.05,1.26,1.88
mobilenetv3_small_075,32291.57,31.7,1024,224,0.05,1.3,2.04
mobilenetv3_small_100,28935.54,35.379,1024,224,0.06,1.42,2.54
tf_mobilenetv3_small_minimal_100,27926.5,36.657,1024,224,0.06,1.41,2.04
tinynet_d,27303.88,37.493,1024,152,0.05,1.42,2.34
tf_mobilenetv3_small_075,26850.04,38.127,1024,224,0.05,1.3,2.04
tf_mobilenetv3_small_100,24320.21,42.094,1024,224,0.06,1.42,2.54
lcnet_075,22627.19,45.245,1024,224,0.1,1.99,2.36
mnasnet_small,20150.91,50.806,1024,224,0.07,2.16,2.03
levit_128s,19458.78,52.613,1024,224,0.31,1.88,7.78
lcnet_100,18910.66,54.139,1024,224,0.16,2.52,2.95
mobilenetv2_035,18047.72,56.728,1024,224,0.07,2.86,1.68
regnetx_002,17921.55,57.126,1024,224,0.2,2.16,2.68
regnety_002,16656.92,61.462,1024,224,0.2,2.17,3.16
ghostnet_050,16494.57,62.071,1024,224,0.05,1.77,2.59
mnasnet_050,15574.97,65.736,1024,224,0.11,3.07,2.22
mobilenetv2_050,14533.98,70.445,1024,224,0.1,3.64,1.97
tinynet_c,14397.76,71.111,1024,184,0.11,2.87,2.46
semnasnet_050,14065.61,72.79,1024,224,0.11,3.44,2.08
levit_128,13348.5,76.702,1024,224,0.41,2.71,9.21
vit_small_patch32_224,12899.41,79.373,1024,224,1.15,2.5,22.88
mixer_s32_224,12823.61,79.842,1024,224,1.0,2.28,19.1
lcnet_150,12599.24,81.264,1024,224,0.34,3.79,4.5
regnetx_004,12314.46,83.141,1024,224,0.4,3.14,5.16
cs3darknet_focus_s,11852.98,86.381,1024,256,0.69,2.7,3.27
mobilenetv3_large_075,11687.27,87.605,1024,224,0.16,4.0,3.99
resnet10t,11549.51,88.651,1024,224,1.1,2.43,5.44
cs3darknet_s,11540.93,88.716,1024,256,0.72,2.97,3.28
vit_tiny_r_s16_p8_224,10917.33,93.785,1024,224,0.44,2.06,6.34
ese_vovnet19b_slim_dw,10530.7,97.229,1024,224,0.4,5.28,1.9
mobilenetv3_rw,10453.43,97.947,1024,224,0.23,4.41,5.48
hardcorenas_a,10387.47,98.569,1024,224,0.23,4.38,5.26
mobilenetv3_large_100_miil,10298.68,99.419,1024,224,0.23,4.41,5.48
mobilenetv3_large_100,10295.13,99.453,1024,224,0.23,4.41,5.48
tf_mobilenetv3_large_075,10277.2,99.627,1024,224,0.16,4.0,3.99
gernet_s,10228.24,100.105,1024,224,0.75,2.65,8.17
mnasnet_075,10209.23,100.29,1024,224,0.23,4.77,3.17
levit_192,10099.95,101.375,1024,224,0.66,3.2,10.95
tf_mobilenetv3_large_minimal_100,10021.88,102.166,1024,224,0.22,4.4,3.92
hardcorenas_b,9469.88,108.121,1024,224,0.26,5.09,5.18
regnetx_006,9309.45,109.982,1024,224,0.61,3.98,6.2
tinynet_b,9298.6,110.113,1024,188,0.21,4.44,3.73
regnety_004,9296.36,110.137,1024,224,0.41,3.89,4.34
ghostnet_100,9264.87,110.513,1024,224,0.15,3.55,5.18
hardcorenas_c,9196.31,111.338,1024,224,0.28,5.01,5.52
resnet18,9171.4,111.64,1024,224,1.82,2.48,11.69
tf_mobilenetv3_large_100,9170.64,111.649,1024,224,0.23,4.41,5.48
mobilenetv2_075,9151.72,111.88,1024,224,0.22,5.86,2.64
swsl_resnet18,9145.07,111.962,1024,224,1.82,2.48,11.69
mnasnet_100,9128.95,112.159,1024,224,0.33,5.46,4.38
mnasnet_b1,9096.68,112.558,1024,224,0.33,5.46,4.38
gluon_resnet18_v1b,9092.93,112.604,1024,224,1.82,2.48,11.69
ssl_resnet18,9043.33,113.221,1024,224,1.82,2.48,11.69
semnasnet_075,8958.16,114.297,1024,224,0.23,5.54,2.91
hardcorenas_d,8756.1,116.935,1024,224,0.3,4.93,7.5
seresnet18,8678.54,117.981,1024,224,1.82,2.49,11.78
regnety_006,8404.01,121.832,1024,224,0.61,4.33,6.06
mobilenetv2_100,8360.81,122.466,1024,224,0.31,6.68,3.5
legacy_seresnet18,8318.12,123.094,1024,224,1.82,2.49,11.78
spnasnet_100,8246.33,124.165,1024,224,0.35,6.03,4.42
semnasnet_100,8027.18,127.555,1024,224,0.32,6.23,3.89
mnasnet_a1,8013.14,127.779,1024,224,0.32,6.23,3.89
levit_256,7862.24,130.228,1024,224,1.13,4.23,18.89
resnet18d,7721.04,132.614,1024,224,2.06,3.29,11.71
hardcorenas_f,7642.68,133.973,1024,224,0.35,5.57,8.2
hardcorenas_e,7588.05,134.938,1024,224,0.35,5.65,8.07
ese_vovnet19b_slim,7530.8,135.964,1024,224,1.69,3.52,3.17
efficientnet_lite0,7530.79,135.964,1024,224,0.4,6.74,4.65
ghostnet_130,7411.84,138.146,1024,224,0.24,4.6,7.36
regnetx_008,7376.89,138.798,1024,224,0.81,5.15,7.26
tinynet_a,7260.16,141.032,1024,192,0.35,5.41,6.19
tf_efficientnetv2_b0,7117.22,143.865,1024,224,0.73,4.77,7.14
fbnetc_100,7115.49,143.899,1024,224,0.4,6.51,5.57
regnety_008,7108.36,144.037,1024,224,0.81,5.25,6.26
xcit_nano_12_p16_224_dist,7019.86,145.861,1024,224,0.56,4.17,3.05
xcit_nano_12_p16_224,7000.29,146.268,1024,224,0.56,4.17,3.05
edgenext_xx_small,6963.01,147.05,1024,256,0.33,4.21,1.33
levit_256d,6856.41,149.338,1024,224,1.4,4.93,26.21
deit_tiny_patch16_224,6794.46,150.698,1024,224,1.26,5.97,5.72
vit_tiny_patch16_224,6769.81,151.248,1024,224,1.26,5.97,5.72
tf_efficientnet_lite0,6667.82,153.562,1024,224,0.4,6.74,4.65
deit_tiny_distilled_patch16_224,6647.4,154.032,1024,224,1.27,6.01,5.91
efficientnet_b0,6576.16,155.702,1024,224,0.4,6.75,5.29
dla46_c,6538.59,156.596,1024,224,0.58,4.5,1.3
rexnetr_100,6369.79,160.748,1024,224,0.43,7.72,4.88
mnasnet_140,6297.39,162.595,1024,224,0.6,7.71,7.12
rexnet_100,6295.89,162.634,1024,224,0.41,7.44,4.8
efficientnet_b1_pruned,6269.62,163.315,1024,240,0.4,6.21,6.33
mobilenetv2_110d,6263.44,163.477,1024,224,0.45,8.71,4.52
regnetz_005,6057.27,169.042,1024,224,0.52,5.86,7.12
resnetblur18,6056.25,169.07,1024,224,2.34,3.39,11.69
pit_ti_distilled_224,6026.49,169.903,1024,224,0.71,6.23,5.1
pit_ti_224,5988.08,170.993,1024,224,0.7,6.19,4.85
nf_regnet_b0,5936.35,172.485,1024,256,0.64,5.58,8.76
mobilevitv2_050,5906.65,173.353,1024,256,0.48,8.04,1.37
tf_efficientnet_b0_ap,5894.61,173.707,1024,224,0.4,6.75,5.29
tf_efficientnet_b0,5892.32,173.774,1024,224,0.4,6.75,5.29
tf_efficientnet_b0_ns,5891.52,173.799,1024,224,0.4,6.75,5.29
visformer_tiny,5845.93,175.153,1024,224,1.27,5.72,10.32
resnet14t,5834.5,175.495,1024,224,1.69,5.8,10.08
dla46x_c,5690.98,179.922,1024,224,0.54,5.66,1.07
skresnet18,5640.2,181.543,1024,224,1.82,3.24,11.96
semnasnet_140,5544.22,184.685,1024,224,0.6,8.87,6.11
hrnet_w18_small,5451.81,187.816,1024,224,1.61,5.72,13.19
mobilenetv2_140,5399.1,189.649,1024,224,0.6,9.57,6.11
resnet34,5356.43,191.161,1024,224,3.67,3.74,21.8
dla60x_c,5292.02,193.487,1024,224,0.59,6.01,1.32
mobilevit_xxs,5275.05,194.109,1024,256,0.42,8.34,1.27
ese_vovnet19b_dw,5260.83,194.634,1024,224,1.34,8.25,6.54
gluon_resnet34_v1b,5203.76,196.769,1024,224,3.67,3.74,21.8
tv_resnet34,5193.55,197.156,1024,224,3.67,3.74,21.8
efficientnet_lite1,5144.81,199.024,1024,240,0.62,10.14,5.42
mixnet_s,5054.78,202.566,1024,224,0.25,6.25,4.13
seresnet34,5051.53,202.699,1024,224,3.67,3.74,21.96
gernet_m,5028.39,203.632,1024,224,3.02,5.24,21.14
fbnetv3_b,4982.49,205.508,1024,256,0.55,9.1,8.6
selecsls42,4945.53,207.043,1024,224,2.94,4.62,30.35
selecsls42b,4942.3,207.179,1024,224,2.98,4.62,32.46
vit_base_patch32_224_sam,4921.3,208.063,1024,224,4.41,5.01,88.22
vit_base_patch32_224,4918.17,208.197,1024,224,4.41,5.01,88.22
resnet34d,4834.03,211.82,1024,224,3.91,4.54,21.82
rexnetr_130,4789.2,213.803,1024,224,0.68,9.81,7.61
pit_xs_224,4766.56,214.816,1024,224,1.4,7.71,10.62
tf_efficientnetv2_b1,4738.51,216.09,1024,240,1.21,7.34,8.14
legacy_seresnet34,4737.15,216.152,1024,224,3.67,3.74,21.96
pit_xs_distilled_224,4722.37,216.826,1024,224,1.41,7.76,11.0
mixer_b32_224,4707.3,217.523,1024,224,3.24,6.29,60.29
tf_mixnet_s,4706.57,217.551,1024,224,0.25,6.25,4.13
tf_efficientnet_lite1,4662.36,219.62,1024,240,0.62,10.14,5.42
xcit_tiny_12_p16_224_dist,4593.26,222.924,1024,224,1.24,6.29,6.72
xcit_tiny_12_p16_224,4592.09,222.979,1024,224,1.24,6.29,6.72
rexnet_130,4578.43,223.646,1024,224,0.68,9.71,7.56
levit_384,4538.82,225.597,1024,224,2.36,6.26,39.13
mobilenetv2_120d,4530.56,226.009,1024,224,0.69,11.97,5.83
edgenext_x_small,4436.58,230.795,1024,256,0.68,7.5,2.34
cs3darknet_focus_m,4399.27,232.755,1024,288,2.51,6.19,9.3
efficientnet_b0_g16_evos,4394.3,233.017,1024,224,1.01,7.42,8.11
efficientnet_es,4389.85,233.253,1024,224,1.81,8.73,5.44
efficientnet_es_pruned,4389.6,233.266,1024,224,1.81,8.73,5.44
resnet26,4383.27,233.604,1024,224,2.36,7.35,16.0
cs3darknet_m,4330.89,236.429,1024,288,2.63,6.69,9.31
fbnetv3_d,4328.59,236.555,1024,256,0.68,11.1,10.31
repvgg_b0,4286.85,238.858,1024,224,3.41,6.15,15.82
selecsls60,4286.11,238.899,1024,224,3.59,5.52,30.67
darknet17,4270.14,179.843,768,256,3.26,7.18,14.3
selecsls60b,4265.59,240.05,1024,224,3.63,5.52,32.77
efficientnet_b2_pruned,4264.69,240.099,1024,260,0.73,9.13,8.31
tf_efficientnet_es,4239.07,241.551,1024,224,1.81,8.73,5.44
regnetx_016,4196.72,243.986,1024,224,1.62,7.93,9.19
rexnetr_150,4170.12,245.545,1024,224,0.89,11.13,9.78
crossvit_tiny_240,4122.19,248.4,1024,240,1.57,9.08,7.01
dla34,4120.09,248.525,1024,224,3.07,5.02,15.74
mixer_s16_224,4085.83,250.611,1024,224,3.79,5.97,18.53
vit_small_patch32_384,4015.79,254.982,1024,384,3.45,8.25,22.92
rexnet_150,3990.72,256.583,1024,224,0.9,11.21,9.73
resnet26d,3989.2,256.681,1024,224,2.6,8.15,16.01
ecaresnet50d_pruned,3983.23,257.066,1024,224,2.53,6.43,19.94
efficientnet_lite2,3977.91,257.41,1024,260,0.89,12.9,6.09
gmlp_ti16_224,3944.3,259.603,1024,224,1.34,7.55,5.87
mobilevitv2_075,3905.27,262.199,1024,256,1.05,12.06,2.87
crossvit_9_240,3875.46,264.215,1024,240,1.85,9.52,8.55
darknet21,3872.25,198.322,768,256,3.93,7.47,20.86
nf_resnet26,3857.21,265.465,1024,224,2.41,7.35,16.0
convnext_nano_ols,3756.44,272.585,1024,224,2.5,8.37,15.6
convnext_nano_hnf,3749.56,273.084,1024,224,2.46,8.37,15.59
sedarknet21,3744.18,205.107,768,256,3.93,7.47,20.95
efficientnet_b1,3742.67,273.59,1024,256,0.77,12.22,7.79
crossvit_9_dagger_240,3734.14,274.215,1024,240,1.99,9.97,8.78
tf_efficientnet_b1,3731.51,274.409,1024,240,0.71,10.88,7.79
tf_efficientnet_b1_ns,3731.48,274.411,1024,240,0.71,10.88,7.79
tf_efficientnet_b1_ap,3726.19,274.8,1024,240,0.71,10.88,7.79
resnest14d,3644.88,280.93,1024,224,2.76,7.33,10.61
regnety_016,3624.55,282.503,1024,224,1.63,8.04,11.2
tf_efficientnet_lite2,3624.06,282.543,1024,260,0.89,12.9,6.09
vit_tiny_r_s16_p8_384,3594.94,213.622,768,384,1.34,6.49,6.36
tf_efficientnetv2_b2,3593.98,284.91,1024,260,1.72,9.84,10.1
poolformer_s12,3483.41,293.951,1024,224,1.82,5.53,11.92
resmlp_12_224,3460.87,295.868,1024,224,3.01,5.5,15.35
resmlp_12_distilled_224,3458.54,296.067,1024,224,3.01,5.5,15.35
mixnet_m,3455.23,296.35,1024,224,0.36,8.19,5.01
gmixer_12_224,3401.29,301.051,1024,224,2.67,7.26,12.7
resnext26ts,3375.26,303.371,1024,256,2.43,10.52,10.3
nf_ecaresnet26,3365.9,304.215,1024,224,2.41,7.36,16.0
nf_seresnet26,3360.23,304.729,1024,224,2.41,7.36,17.4
gernet_l,3328.59,307.626,1024,256,4.57,8.0,31.08
repvgg_a2,3325.03,307.955,1024,224,5.7,6.26,28.21
tf_mixnet_m,3322.0,308.236,1024,224,0.36,8.19,5.01
efficientnet_b3_pruned,3297.4,310.535,1024,300,1.04,11.86,9.86
nf_regnet_b1,3293.07,310.944,1024,288,1.02,9.2,10.22
seresnext26ts,3291.26,311.115,1024,256,2.43,10.52,10.39
eca_resnext26ts,3290.56,311.182,1024,256,2.43,10.52,10.3
legacy_seresnext26_32x4d,3269.09,313.225,1024,224,2.49,9.39,16.79
skresnet34,3229.96,317.02,1024,224,3.67,5.13,22.28
gcresnext26ts,3229.79,317.037,1024,256,2.43,10.53,10.48
nf_regnet_b2,3193.49,320.64,1024,272,1.22,9.27,14.31
convit_tiny,3179.42,322.058,1024,224,1.26,7.94,5.71
resnet26t,3149.41,325.128,1024,256,3.35,10.52,16.01
rexnetr_200,3135.78,244.904,768,224,1.59,15.11,16.52
ecaresnet101d_pruned,3129.51,327.195,1024,224,3.48,7.69,24.88
seresnext26tn_32x4d,3050.2,335.704,1024,224,2.7,10.09,16.81
seresnext26t_32x4d,3050.01,335.724,1024,224,2.7,10.09,16.81
ecaresnext50t_32x4d,3049.83,335.744,1024,224,2.7,10.09,15.41
ecaresnext26t_32x4d,3048.36,335.905,1024,224,2.7,10.09,15.41
seresnext26d_32x4d,3037.9,337.063,1024,224,2.73,10.19,16.81
deit_small_patch16_224,3002.36,341.052,1024,224,4.61,11.95,22.05
rexnet_200,3001.86,255.828,768,224,1.56,14.91,16.37
vit_small_patch16_224,3000.37,341.279,1024,224,4.61,11.95,22.05
mobilevit_xs,2981.72,257.559,768,256,1.05,16.33,2.32
deit_small_distilled_patch16_224,2950.87,347.001,1024,224,4.63,12.02,22.44
pit_s_224,2945.22,347.668,1024,224,2.88,11.56,23.46
ecaresnetlight,2941.7,348.085,1024,224,4.11,8.42,30.16
coat_lite_tiny,2932.4,349.189,1024,224,1.6,11.65,5.72
eca_botnext26ts_256,2930.99,349.358,1024,256,2.46,11.6,10.59
pit_s_distilled_224,2918.75,350.821,1024,224,2.9,11.64,24.04
tf_efficientnet_b2_ns,2903.13,352.71,1024,260,1.02,13.83,9.11
tf_efficientnet_b2,2902.67,352.766,1024,260,1.02,13.83,9.11
tf_efficientnet_b2_ap,2901.98,352.851,1024,260,1.02,13.83,9.11
eca_halonext26ts,2883.09,355.163,1024,256,2.44,11.46,10.76
tresnet_m,2870.7,356.694,1024,224,5.74,7.31,31.39
botnet26t_256,2862.72,357.688,1024,256,3.32,11.98,12.49
regnetx_032,2852.1,359.019,1024,224,3.2,11.37,15.3
hrnet_w18_small_v2,2845.04,359.912,1024,224,2.62,9.65,15.6
deit3_small_patch16_224_in21ft1k,2837.48,360.868,1024,224,4.61,11.95,22.06
halonet26t,2832.73,361.477,1024,256,3.19,11.69,12.48
resnetv2_50,2829.74,361.858,1024,224,4.11,11.11,25.55
deit3_small_patch16_224,2828.59,362.004,1024,224,4.61,11.95,22.06
vgg11,2795.7,183.125,512,224,7.61,7.44,132.86
haloregnetz_b,2794.73,366.391,1024,224,1.97,11.94,11.68
bat_resnext26ts,2793.93,366.495,1024,256,2.53,12.51,10.73
vit_relpos_base_patch32_plus_rpn_256,2775.87,368.882,1024,256,7.68,8.01,119.42
vit_base_patch32_plus_256,2773.66,369.174,1024,256,7.79,7.76,119.48
dpn68b,2762.53,370.662,1024,224,2.35,10.47,12.61
vit_small_resnet26d_224,2758.05,371.264,1024,224,5.07,11.12,63.61
efficientnet_b2,2753.12,371.929,1024,288,1.12,16.2,9.11
coat_lite_mini,2752.31,372.037,1024,224,2.0,12.25,11.01
efficientnet_b2a,2752.1,372.068,1024,288,1.12,16.2,9.11
efficientnet_b0_gn,2748.63,372.536,1024,224,0.42,6.75,5.29
resnet50,2733.34,374.621,1024,224,4.11,11.11,25.56
ssl_resnet50,2732.73,374.705,1024,224,4.11,11.11,25.56
tv_resnet50,2732.0,374.804,1024,224,4.11,11.11,25.56
gluon_resnet50_v1b,2731.92,374.815,1024,224,4.11,11.11,25.56
swsl_resnet50,2730.89,374.957,1024,224,4.11,11.11,25.56
cspresnet50,2720.51,376.385,1024,256,4.54,11.5,21.62
resnet32ts,2719.03,376.593,1024,256,4.63,11.58,17.96
dpn68,2711.28,377.669,1024,224,2.35,10.47,12.61
mobilevitv2_100,2710.59,283.322,768,256,1.84,16.08,4.9
vovnet39a,2706.48,378.339,1024,224,7.09,6.73,22.6
resnetv2_50t,2687.6,380.997,1024,224,4.32,11.82,25.57
resnet33ts,2683.32,381.605,1024,256,4.76,11.66,19.68
resnetv2_50d,2678.25,382.327,1024,224,4.35,11.92,25.57
efficientnet_em,2663.14,384.496,1024,240,3.04,14.34,6.9
mixnet_l,2651.17,289.672,768,224,0.58,10.84,7.33
visformer_small,2638.82,388.04,1024,224,4.88,11.43,40.22
ese_vovnet39b,2631.4,389.135,1024,224,7.09,6.74,24.57
resnest26d,2624.21,390.2,1024,224,3.64,9.97,17.07
vit_relpos_small_patch16_224,2615.53,391.496,1024,224,4.59,13.05,21.98
seresnet33ts,2613.57,391.79,1024,256,4.76,11.66,19.78
eca_resnet33ts,2609.68,392.373,1024,256,4.76,11.66,19.68
vit_srelpos_small_patch16_224,2607.7,392.67,1024,224,4.59,12.16,21.97
eca_vovnet39b,2607.14,392.755,1024,224,7.09,6.74,22.6
gluon_resnet50_v1c,2599.91,393.848,1024,224,4.35,11.92,25.58
tf_efficientnet_em,2599.16,393.961,1024,240,3.04,14.34,6.9
cspresnet50w,2589.44,395.44,1024,256,5.04,12.19,28.12
resnet50d,2584.02,396.27,1024,224,4.35,11.92,25.58
legacy_seresnet50,2582.34,396.527,1024,224,3.88,10.6,28.09
resnet50t,2580.37,396.829,1024,224,4.32,11.82,25.57
twins_svt_small,2576.63,397.407,1024,224,2.94,13.75,24.06
gluon_resnet50_v1d,2570.89,398.293,1024,224,4.35,11.92,25.58
gcresnet33ts,2569.2,398.556,1024,256,4.76,11.68,19.88
cspresnet50d,2560.0,399.988,1024,256,4.86,12.55,21.64
lambda_resnet26t,2551.69,401.29,1024,256,3.02,11.87,10.96
tf_mixnet_l,2550.79,301.072,768,224,0.58,10.84,7.33
selecsls84,2543.3,402.613,1024,224,5.9,7.57,50.95
vgg11_bn,2541.42,201.45,512,224,7.62,7.44,132.87
dla60,2525.2,405.498,1024,224,4.26,10.16,22.04
cs3darknet_focus_l,2520.03,406.331,1024,288,5.9,10.16,21.15
res2net50_48w_2s,2502.4,409.196,1024,224,4.18,11.72,25.29
cs3darknet_l,2485.99,411.896,1024,288,6.16,10.83,21.16
densenet121,2467.71,414.945,1024,224,2.87,6.9,7.98
xcit_nano_12_p16_384_dist,2466.74,415.111,1024,384,1.64,12.15,3.05
xcit_tiny_24_p16_224_dist,2463.21,415.705,1024,224,2.34,11.82,12.12
tv_densenet121,2461.4,416.011,1024,224,2.87,6.9,7.98
xcit_tiny_24_p16_224,2457.2,416.72,1024,224,2.34,11.82,12.12
seresnet50,2438.84,419.859,1024,224,4.11,11.13,28.09
convnext_tiny_hnfd,2399.93,426.664,1024,224,4.47,13.44,28.59
convnext_tiny_hnf,2395.62,427.433,1024,224,4.47,13.44,28.59
efficientnet_lite3,2383.15,214.83,512,300,1.65,21.85,8.2
efficientnet_b0_g8_gn,2375.8,431.002,1024,224,0.66,6.75,6.56
convnext_tiny_in22ft1k,2362.17,433.485,1024,224,4.47,13.44,28.59
densenet121d,2362.07,433.503,1024,224,3.11,7.7,8.0
convnext_tiny,2359.72,433.936,1024,224,4.47,13.44,28.59
cs3sedarknet_l,2353.08,435.161,1024,288,6.16,10.83,21.91
resnetaa50d,2350.26,435.685,1024,224,5.39,12.44,25.58
efficientnet_cc_b0_4e,2334.0,438.721,1024,224,0.41,9.42,13.31
seresnet50t,2333.68,438.78,1024,224,4.32,11.83,28.1
ecaresnet50d,2316.33,442.066,1024,224,4.35,11.93,25.58
resnetblur50,2298.66,445.465,1024,224,5.16,12.02,25.56
mobilevit_s,2279.76,336.866,768,256,2.03,19.94,5.58
convnext_nano,2276.19,449.862,1024,288,4.06,13.84,15.59
resnetrs50,2276.18,449.864,1024,224,4.48,12.14,35.69
vit_base_resnet26d_224,2262.15,452.654,1024,224,6.97,13.16,101.4
gluon_resnet50_v1s,2257.16,453.655,1024,224,5.47,13.52,25.68
vovnet57a,2253.6,454.372,1024,224,8.95,7.52,36.64
adv_inception_v3,2250.27,455.041,1024,299,5.73,8.97,23.83
gluon_inception_v3,2249.35,455.229,1024,299,5.73,8.97,23.83
tf_inception_v3,2245.22,456.064,1024,299,5.73,8.97,23.83
tf_efficientnet_cc_b0_4e,2243.01,456.518,1024,224,0.41,9.42,13.31
inception_v3,2240.78,456.965,1024,299,5.73,8.97,23.83
tf_efficientnet_cc_b0_8e,2240.71,456.986,1024,224,0.42,9.42,24.01
densenetblur121d,2235.56,458.037,1024,224,3.11,7.9,8.0
resnest50d_1s4x24d,2213.57,462.589,1024,224,4.43,13.57,25.68
res2net50_26w_4s,2209.54,463.432,1024,224,4.28,12.61,25.7
ssl_resnext50_32x4d,2205.13,464.359,1024,224,4.26,14.4,25.03
swsl_resnext50_32x4d,2204.8,464.429,1024,224,4.26,14.4,25.03
gluon_resnext50_32x4d,2203.2,464.765,1024,224,4.26,14.4,25.03
resnext50_32x4d,2199.44,465.561,1024,224,4.26,14.4,25.03
tv_resnext50_32x4d,2198.23,465.818,1024,224,4.26,14.4,25.03
regnetx_040,2190.95,467.362,1024,224,3.99,12.2,22.12
cspresnext50,2182.4,469.194,1024,256,4.05,15.86,20.57
resnetblur50d,2182.09,469.263,1024,224,5.4,12.82,25.58
regnetz_b16,2180.8,469.54,1024,288,2.39,16.43,9.72
ese_vovnet57b,2171.57,471.535,1024,224,8.95,7.52,38.61
tf_efficientnet_lite3,2166.77,236.285,512,300,1.65,21.85,8.2
mobilevitv2_125,2151.63,356.926,768,256,2.86,20.1,7.48
efficientnet_cc_b0_8e,2149.58,476.36,1024,224,0.42,9.42,24.01
semobilevit_s,2143.19,358.331,768,256,2.03,19.95,5.74
twins_pcpvt_small,2142.01,478.043,1024,224,3.83,18.08,24.11
nf_regnet_b3,2133.81,479.88,1024,320,2.05,14.61,18.59
tf_efficientnetv2_b3,2121.62,482.639,1024,300,3.04,15.74,14.36
seresnetaa50d,2118.99,483.236,1024,224,5.4,12.46,28.11
efficientnetv2_rw_t,2117.33,483.616,1024,288,3.19,16.42,13.65
gcresnext50ts,2113.73,484.438,1024,256,3.75,15.46,15.67
edgenext_small,2107.47,485.876,1024,320,1.97,14.16,5.59
resnext50d_32x4d,2094.1,488.98,1024,224,4.5,15.2,25.05
dla60x,2080.97,492.062,1024,224,3.54,13.8,17.35
res2net50_14w_8s,2066.79,495.441,1024,224,4.21,13.28,25.06
gc_efficientnetv2_rw_t,2061.37,496.743,1024,288,3.2,16.45,13.68
sehalonet33ts,2057.14,373.322,768,256,3.55,14.7,13.69
gcresnet50t,2055.89,498.068,1024,256,5.42,14.67,25.9
skresnet50,2048.81,499.79,1024,224,4.11,12.5,25.8
fbnetv3_g,2047.87,500.019,1024,288,1.77,21.09,16.62
nf_ecaresnet50,2039.26,502.129,1024,224,4.21,11.13,25.56
nf_seresnet50,2037.45,502.576,1024,224,4.21,11.13,28.09
cs3darknet_focus_x,2026.01,505.415,1024,256,8.03,10.69,35.02
dla60_res2net,2015.55,508.035,1024,224,4.15,12.34,20.85
lambda_resnet26rpt_256,2015.24,190.536,384,256,3.16,11.87,10.99
seresnext50_32x4d,2010.4,509.339,1024,224,4.26,14.42,27.56
legacy_seresnext50_32x4d,2003.57,511.076,1024,224,4.26,14.42,27.56
repvgg_b1g4,2003.2,511.169,1024,224,8.15,10.64,39.97
gluon_seresnext50_32x4d,2002.45,511.358,1024,224,4.26,14.42,27.56
densenet169,1987.41,515.228,1024,224,3.4,7.3,14.15
res2next50,1967.78,520.369,1024,224,4.2,13.71,24.67
vit_relpos_small_patch16_rpn_224,1966.99,520.579,1024,224,4.59,13.05,21.97
skresnet50d,1957.14,523.201,1024,224,4.36,13.31,25.82
xcit_small_12_p16_224_dist,1952.72,524.382,1024,224,4.82,12.58,26.25
crossvit_small_240,1952.54,524.431,1024,240,5.63,18.17,26.86
xcit_small_12_p16_224,1952.1,524.55,1024,224,4.82,12.58,26.25
cs3sedarknet_xdw,1919.73,533.397,1024,256,5.97,17.18,21.6
swin_tiny_patch4_window7_224,1915.52,534.569,1024,224,4.51,17.06,28.29
vit_relpos_medium_patch16_cls_224,1909.56,536.236,1024,224,8.03,18.24,38.76
mixnet_xl,1903.31,268.993,512,224,0.93,14.57,11.9
dla60_res2next,1893.19,540.873,1024,224,3.49,13.17,17.03
xcit_nano_12_p8_224_dist,1887.0,542.649,1024,224,2.16,15.71,3.05
xcit_nano_12_p8_224,1883.21,543.74,1024,224,2.16,15.71,3.05
cspdarknet53,1881.33,408.211,768,256,6.57,16.81,27.64
gmlp_s16_224,1873.82,546.464,1024,224,4.42,15.1,19.42
edgenext_small_rw,1831.96,558.95,1024,320,2.46,14.85,7.83
ecaresnet26t,1828.87,559.898,1024,320,5.24,16.44,16.01
vit_small_r26_s32_224,1825.94,560.792,1024,224,3.56,9.85,36.43
vgg13,1819.73,281.346,512,224,11.31,12.25,133.05
poolformer_s24,1804.4,567.487,1024,224,3.41,10.68,21.39
crossvit_15_240,1799.06,569.173,1024,240,5.81,19.77,27.53
vit_relpos_medium_patch16_224,1794.09,570.75,1024,224,7.97,17.02,38.75
vit_srelpos_medium_patch16_224,1787.05,573.0,1024,224,7.96,16.21,38.74
mobilevitv2_150,1774.77,288.477,512,256,4.09,24.11,10.59
mobilevitv2_150_in22ft1k,1773.43,288.695,512,256,4.09,24.11,10.59
sebotnet33ts_256,1762.47,217.864,384,256,3.89,17.46,13.7
resmlp_24_224,1761.92,581.171,1024,224,5.96,10.91,30.02
efficientnet_b3,1761.71,290.615,512,320,2.01,26.52,12.23
efficientnet_b3a,1761.71,290.614,512,320,2.01,26.52,12.23
resmlp_24_distilled_224,1760.69,581.576,1024,224,5.96,10.91,30.02
regnetx_064,1757.88,436.877,768,224,6.49,16.37,26.21
resnest50d,1750.78,584.87,1024,224,5.4,14.36,27.48
gmixer_24_224,1741.35,588.036,1024,224,5.28,14.45,24.72
swin_s3_tiny_224,1737.42,589.369,1024,224,4.64,19.13,28.33
crossvit_15_dagger_240,1736.98,589.517,1024,240,6.13,20.43,28.21
vit_base_resnet50d_224,1722.65,594.42,1024,224,8.73,16.92,110.97
resnetv2_101,1717.18,596.314,1024,224,7.83,16.23,44.54
tf_efficientnet_b3_ap,1706.97,299.935,512,300,1.87,23.83,12.23
tf_efficientnet_b3,1705.74,300.151,512,300,1.87,23.83,12.23
tf_efficientnet_b3_ns,1705.51,300.191,512,300,1.87,23.83,12.23
lambda_resnet50ts,1694.68,604.231,1024,256,5.07,17.48,21.54
dla102,1693.75,604.56,1024,224,7.19,14.18,33.27
darknetaa53,1689.16,454.651,768,288,10.08,15.68,36.02
gluon_resnet101_v1b,1679.75,609.599,1024,224,7.83,16.23,44.55
tv_resnet101,1679.18,609.808,1024,224,7.83,16.23,44.55
resnet101,1676.67,610.719,1024,224,7.83,16.23,44.55
repvgg_b1,1663.53,615.546,1024,224,13.16,10.64,57.42
resnetv2_101d,1653.14,619.414,1024,224,8.07,17.04,44.56
gluon_resnet101_v1c,1649.02,620.96,1024,224,8.08,17.04,44.57
vgg13_bn,1642.15,311.774,512,224,11.33,12.25,133.05
cait_xxs24_224,1641.65,623.749,1024,224,2.53,20.29,11.96
res2net50_26w_6s,1639.34,624.627,1024,224,6.33,15.28,37.05
hrnet_w18,1631.65,627.569,1024,224,4.32,16.31,21.3
vit_large_patch32_224,1623.29,630.805,1024,224,15.39,13.3,306.54
wide_resnet50_2,1618.04,632.851,1024,224,11.43,14.4,68.88
gluon_resnet101_v1d,1616.88,633.307,1024,224,8.08,17.04,44.57
xcit_tiny_12_p16_384_dist,1614.06,634.414,1024,384,3.64,18.26,6.72
regnetv_040,1604.78,478.557,768,288,6.6,20.3,20.64
halonet50ts,1600.56,639.764,1024,256,5.3,19.2,22.73
regnety_040,1597.66,480.688,768,288,6.61,20.3,20.65
darknet53,1585.01,484.528,768,288,11.78,15.68,41.61
efficientnet_cc_b1_8e,1576.34,649.593,1024,240,0.75,15.44,39.72
coat_lite_small,1576.03,649.72,1024,224,3.96,22.09,19.84
regnety_032,1576.03,649.722,1024,288,5.29,18.61,19.44
resnetv2_50x1_bit_distilled,1575.9,649.775,1024,224,4.23,11.11,25.55
swinv2_cr_tiny_224,1574.62,650.304,1024,224,4.66,28.45,28.33
legacy_seresnet101,1569.43,652.454,1024,224,7.61,15.74,49.33
vit_base_patch32_384,1551.76,659.885,1024,384,13.06,16.5,88.3
ese_vovnet39b_evos,1551.37,660.05,1024,224,7.07,6.74,24.58
swinv2_cr_tiny_ns_224,1546.02,662.33,1024,224,4.66,28.45,28.33
vit_tiny_patch16_384,1542.96,663.648,1024,384,4.7,25.39,5.79
lamhalobotnet50ts_256,1533.2,667.873,1024,256,5.02,18.44,22.57
tf_efficientnet_cc_b1_8e,1527.24,670.479,1024,240,0.75,15.44,39.72
resnetaa101d,1521.49,673.009,1024,224,9.12,17.56,44.57
densenet201,1515.85,675.514,1024,224,4.34,7.85,20.01
resnetaa50,1510.7,677.817,1024,288,8.52,19.24,25.56
mixer_l32_224,1508.54,678.791,1024,224,11.27,19.86,206.94
seresnet101,1502.48,681.526,1024,224,7.84,16.27,49.33
vit_base_r26_s32_224,1492.4,686.129,1024,224,6.81,12.36,101.38
gluon_resnet101_v1s,1485.37,689.375,1024,224,9.19,18.64,44.67
twins_pcpvt_base,1484.93,689.584,1024,224,6.68,25.25,43.83
mobilevitv2_175,1472.18,347.77,512,256,5.54,28.13,14.25
mobilevitv2_175_in22ft1k,1472.06,347.8,512,256,5.54,28.13,14.25
nf_resnet101,1469.16,696.987,1024,224,8.01,16.23,44.55
resnest50d_4s2x40d,1467.36,697.84,1024,224,4.4,17.94,30.42
vgg16,1464.57,349.576,512,224,15.47,13.56,138.36
resnetv2_50d_frn,1463.93,699.474,1024,224,4.33,11.92,25.59
resnetblur101d,1458.09,702.276,1024,224,9.12,17.94,44.57
ecaresnet101d,1457.01,702.796,1024,224,8.08,17.07,44.57
sequencer2d_s,1455.29,703.627,1024,224,4.96,11.31,27.65
nf_resnet50,1445.9,708.195,1024,288,6.88,18.37,25.56
convnext_small,1445.85,708.22,1024,224,8.71,21.56,50.22
convnext_small_in22ft1k,1443.98,709.135,1024,224,8.71,21.56,50.22
regnetz_c16,1437.42,356.181,512,320,3.92,25.88,13.46
tresnet_l,1432.52,714.812,1024,224,10.88,11.9,55.99
cs3darknet_x,1429.24,716.453,1024,288,10.6,14.36,35.05
dla102x,1397.97,732.475,1024,224,5.89,19.42,26.31
ssl_resnext101_32x4d,1392.96,735.11,1024,224,8.01,21.23,44.18
swsl_resnext101_32x4d,1392.73,735.231,1024,224,8.01,21.23,44.18
resnext101_32x4d,1390.48,736.423,1024,224,8.01,21.23,44.18
botnet50ts_256,1389.99,276.247,384,256,5.54,22.23,22.74
skresnext50_32x4d,1389.9,736.732,1024,224,4.5,17.18,27.48
gluon_resnext101_32x4d,1389.41,736.987,1024,224,8.01,21.23,44.18
nest_tiny,1388.05,553.283,768,224,5.83,25.48,17.06
resnet50_gn,1386.72,738.422,1024,224,4.14,11.11,25.56
resnetv2_50d_evob,1383.3,740.244,1024,224,4.33,11.92,25.59
res2net50_26w_8s,1373.33,745.622,1024,224,8.37,17.95,48.4
halo2botnet50ts_256,1372.33,559.619,768,256,5.02,21.78,22.64
regnetx_080,1370.56,747.125,1024,224,8.02,14.06,39.57
cs3sedarknet_x,1368.84,748.067,1024,288,10.6,14.37,35.4
jx_nest_tiny,1362.62,563.605,768,224,5.83,25.48,17.06
convit_small,1355.18,755.603,1024,224,5.76,17.87,27.78
res2net101_26w_4s,1353.43,756.586,1024,224,8.1,18.45,45.21
xception,1340.72,572.814,768,299,8.4,35.83,22.86
mixer_b16_224_miil,1340.03,764.147,1024,224,12.62,14.53,59.88
repvgg_b2g4,1335.06,766.992,1024,224,12.63,12.9,61.76
vgg16_bn,1335.02,383.503,512,224,15.5,13.56,138.37
mixer_b16_224,1328.05,771.041,1024,224,12.62,14.53,59.88
twins_svt_base,1307.2,783.34,1024,224,8.59,26.33,56.07
dpn92,1299.67,787.878,1024,224,6.54,18.21,37.67
cs3edgenet_x,1289.05,794.37,1024,288,14.59,16.36,47.82
ese_vovnet99b_iabn,1282.63,798.345,1024,224,16.49,11.27,63.2
crossvit_18_240,1272.74,804.553,1024,240,9.05,26.26,43.27
regnety_040s_gn,1271.39,805.405,1024,224,4.03,12.29,20.65
eca_nfnet_l0,1271.38,805.411,1024,288,7.12,17.29,24.14
nfnet_l0,1269.37,806.681,1024,288,7.13,17.29,35.07
seresnext101_32x4d,1268.1,807.494,1024,224,8.02,21.26,48.96
legacy_seresnext101_32x4d,1267.59,807.817,1024,224,8.02,21.26,48.96
gluon_seresnext101_32x4d,1265.67,809.045,1024,224,8.02,21.26,48.96
nf_ecaresnet101,1264.2,809.986,1024,224,8.01,16.27,44.55
vit_relpos_medium_patch16_rpn_224,1263.66,810.331,1024,224,7.97,17.02,38.73
nf_seresnet101,1261.42,811.77,1024,224,8.02,16.27,49.33
mobilevitv2_200,1256.15,305.684,384,256,7.22,32.15,18.45
mobilevitv2_200_in22ft1k,1255.83,305.762,384,256,7.22,32.15,18.45
xception41p,1254.65,408.071,512,299,9.25,39.86,26.91
resnet51q,1254.6,816.185,1024,288,8.07,20.94,35.7
efficientnet_el,1254.42,408.143,512,300,8.0,30.7,10.59
efficientnet_el_pruned,1254.28,408.188,512,300,8.0,30.7,10.59
ese_vovnet99b,1240.88,825.205,1024,224,16.51,11.27,63.2
xcit_tiny_12_p8_224_dist,1237.16,827.688,1024,224,4.81,23.6,6.71
xcit_tiny_12_p8_224,1235.05,829.105,1024,224,4.81,23.6,6.71
crossvit_18_dagger_240,1235.02,829.126,1024,240,9.5,27.03,44.27
vgg19,1227.1,417.229,512,224,19.63,14.86,143.67
tf_efficientnet_el,1226.94,417.286,512,300,8.0,30.7,10.59
poolformer_s36,1217.09,841.334,1024,224,5.0,15.82,30.86
hrnet_w32,1204.83,849.897,1024,224,8.97,22.02,41.23
hrnet_w30,1202.88,851.275,1024,224,8.15,21.21,37.71
resnetv2_152,1196.21,856.023,1024,224,11.55,22.56,60.19
nfnet_f0,1193.84,857.722,1024,256,12.62,18.05,71.49
swin_small_patch4_window7_224,1179.92,867.841,1024,224,8.77,27.47,49.61
resmlp_36_224,1179.88,867.87,1024,224,8.91,16.33,44.69
vit_small_resnet50d_s16_224,1179.15,868.406,1024,224,13.48,24.82,57.53
resmlp_36_distilled_224,1179.01,868.509,1024,224,8.91,16.33,44.69
efficientnet_lite4,1178.02,325.958,384,380,4.04,45.66,13.01
tv_resnet152,1172.68,873.198,1024,224,11.56,22.56,60.19
gluon_resnet152_v1b,1172.67,873.208,1024,224,11.56,22.56,60.19
resnet152,1170.69,874.682,1024,224,11.56,22.56,60.19
mixnet_xxl,1163.99,329.888,384,224,2.04,23.43,23.96
resnetv2_152d,1163.57,880.032,1024,224,11.8,23.36,60.2
ecaresnet50t,1162.34,880.97,1024,320,8.82,24.13,25.57
resnet61q,1160.54,882.331,1024,288,9.87,21.52,36.85
vit_base_patch16_224_miil,1154.75,886.763,1024,224,17.58,23.9,86.54
repvgg_b2,1154.25,887.146,1024,224,20.45,12.9,89.02
inception_v4,1153.57,887.661,1024,299,12.28,15.09,42.68
swinv2_tiny_window8_256,1152.79,888.266,1024,256,5.96,24.57,28.35
densenet161,1147.8,892.122,1024,224,7.79,11.06,28.68
gluon_resnet152_v1c,1146.71,892.979,1024,224,11.8,23.36,60.21
gluon_resnet152_v1d,1141.31,897.204,1024,224,11.8,23.36,60.21
sequencer2d_m,1138.06,899.765,1024,224,6.55,14.26,38.31
vit_base_patch16_224_sam,1132.42,904.242,1024,224,17.58,23.9,86.57
deit_base_patch16_224,1132.42,904.245,1024,224,17.58,23.9,86.57
vit_base_patch16_224,1132.21,904.413,1024,224,17.58,23.9,86.57
dla169,1130.13,906.071,1024,224,11.6,20.2,53.39
regnetx_120,1129.55,453.263,512,224,12.13,21.37,46.11
volo_d1_224,1126.62,908.904,1024,224,6.94,24.43,26.63
vgg19_bn,1122.31,456.189,512,224,19.66,14.86,143.68
deit_base_distilled_patch16_224,1116.6,917.056,1024,224,17.68,24.05,87.34
xception41,1110.46,461.057,512,299,9.28,39.86,26.97
cait_xxs36_224,1104.66,926.97,1024,224,3.77,30.34,17.3
tf_efficientnet_lite4,1091.59,351.767,384,380,4.04,45.66,13.01
convmixer_1024_20_ks9_p14,1091.56,938.092,1024,224,5.55,5.51,24.38
deit3_base_patch16_224,1090.26,939.213,1024,224,17.58,23.9,86.59
deit3_base_patch16_224_in21ft1k,1088.57,940.667,1024,224,17.58,23.9,86.59
legacy_seresnet152,1086.41,942.544,1024,224,11.33,22.08,66.82
tnt_s_patch16_224,1079.54,948.54,1024,224,5.24,24.37,23.76
regnety_120,1077.58,475.125,512,224,12.14,21.38,51.82
repvgg_b3g4,1077.28,950.524,1024,224,17.89,15.1,83.83
vit_relpos_base_patch16_clsgap_224,1077.01,950.767,1024,224,17.6,25.12,86.43
vit_relpos_base_patch16_cls_224,1076.19,951.489,1024,224,17.6,25.12,86.43
gluon_resnet152_v1s,1074.28,953.181,1024,224,12.92,24.96,60.32
twins_pcpvt_large,1061.77,964.416,1024,224,9.84,35.82,60.99
seresnet152,1047.32,977.721,1024,224,11.57,22.61,66.82
beit_base_patch16_224,1045.12,979.774,1024,224,17.58,23.9,86.53
xcit_small_24_p16_224_dist,1038.39,986.125,1024,224,9.1,23.64,47.67
xcit_small_24_p16_224,1037.69,986.793,1024,224,9.1,23.64,47.67
coat_tiny,1036.7,987.731,1024,224,4.35,27.2,5.5
dm_nfnet_f0,1035.11,989.253,1024,256,12.62,18.05,71.49
nf_regnet_b4,1027.0,997.065,1024,384,4.7,28.61,30.21
vit_relpos_base_patch16_224,1017.61,1006.263,1024,224,17.51,24.97,86.43
convnext_base_in22ft1k,1006.85,1017.02,1024,224,15.38,28.75,88.59
convnext_base,1006.73,1017.126,1024,224,15.38,28.75,88.59
pit_b_224,993.61,515.277,512,224,12.42,32.94,73.76
pit_b_distilled_224,985.16,519.696,512,224,12.5,33.07,74.79
tresnet_xl,983.38,1041.292,1024,224,15.17,15.34,78.44
efficientnetv2_s,976.0,1049.166,1024,384,8.44,35.77,21.46
dla102x2,973.1,526.138,512,224,9.34,29.91,41.28
cs3se_edgenet_x,972.26,1053.196,1024,320,18.01,20.21,50.72
vit_small_patch16_36x1_224,972.14,1053.329,1024,224,13.71,35.69,64.67
swinv2_cr_small_224,966.28,1059.712,1024,224,9.07,50.27,49.7
swinv2_cr_small_ns_224,955.69,1071.465,1024,224,9.08,50.27,49.7
tf_efficientnetv2_s_in21ft1k,955.24,1071.964,1024,384,8.44,35.77,21.46
tf_efficientnetv2_s,955.13,1072.086,1024,384,8.44,35.77,21.46
vit_small_patch16_18x2_224,948.32,1079.793,1024,224,13.71,35.69,64.67
wide_resnet101_2,939.08,1090.412,1024,224,22.8,21.23,126.89
regnetx_160,936.53,546.684,512,224,15.99,25.52,54.28
regnety_080,933.52,548.447,512,288,13.22,29.69,39.18
regnetz_b16_evos,933.51,822.691,768,288,2.36,16.43,9.74
efficientnetv2_rw_s,931.24,1099.596,1024,384,8.72,38.03,23.94
resnetv2_50d_gn,920.9,1111.946,1024,288,7.24,19.7,25.57
twins_svt_large,918.22,1115.185,1024,224,15.15,35.1,99.27
efficientnet_b4,917.89,418.339,384,384,4.51,50.04,19.34
regnetz_040,913.72,420.249,384,320,6.35,37.78,27.12
xception65p,910.71,562.184,512,299,13.91,52.48,39.82
regnetz_040h,909.33,422.274,384,320,6.43,37.94,28.94
dpn98,906.73,1129.316,1024,224,11.73,25.2,61.57
repvgg_b3,901.67,1135.661,1024,224,29.16,15.1,123.09
resnetrs101,898.53,1139.62,1024,288,13.56,28.53,63.62
gluon_resnext101_64x4d,887.37,1153.955,1024,224,15.52,31.21,83.46
nest_small,885.28,867.51,768,224,10.35,40.04,38.35
poolformer_m36,879.83,1163.84,1024,224,8.8,22.02,56.17
regnetz_d8,877.84,1166.489,1024,320,6.19,37.08,23.37
jx_nest_small,874.11,878.596,768,224,10.35,40.04,38.35
ssl_resnext101_32x8d,874.01,1171.597,1024,224,16.48,31.21,88.79
swsl_resnext101_32x8d,873.31,1172.532,1024,224,16.48,31.21,88.79
resnext101_32x8d,873.01,1172.932,1024,224,16.48,31.21,88.79
ig_resnext101_32x8d,872.81,1173.211,1024,224,16.48,31.21,88.79
regnetz_d32,869.58,1177.564,1024,320,9.33,37.08,27.58
inception_resnet_v2,868.78,1178.653,1024,299,13.18,25.06,55.84
ens_adv_inception_resnet_v2,868.32,1179.275,1024,299,13.18,25.06,55.84
xcit_tiny_24_p16_384_dist,866.54,1181.7,1024,384,6.87,34.29,12.12
cait_s24_224,865.33,1183.354,1024,224,9.35,40.58,46.92
resnest101e,858.93,894.122,768,256,13.38,28.66,48.28
tf_efficientnet_b4,858.91,447.067,384,380,4.49,49.49,19.34
tf_efficientnet_b4_ap,858.7,447.171,384,380,4.49,49.49,19.34
tf_efficientnet_b4_ns,858.52,447.267,384,380,4.49,49.49,19.34
swin_s3_small_224,853.54,899.766,768,224,9.43,37.84,49.74
regnetv_064,852.1,600.857,512,288,10.55,27.11,30.58
regnety_064,851.33,601.396,512,288,10.56,27.11,30.58
resnet200,847.44,1208.333,1024,224,15.07,32.19,64.67
gluon_seresnext101_64x4d,834.87,1226.518,1024,224,15.53,31.25,88.23
coat_mini,833.41,1228.669,1024,224,6.82,33.68,10.34
swin_base_patch4_window7_224,832.6,1229.869,1024,224,15.47,36.63,87.77
resnet101d,816.8,1253.661,1024,320,16.48,34.77,44.57
gluon_xception65,816.5,627.052,512,299,13.96,52.48,39.92
xception65,811.16,631.185,512,299,13.96,52.48,39.92
resnetv2_50d_evos,810.51,947.543,768,288,7.15,19.7,25.59
convnext_tiny_384_in22ft1k,807.27,634.218,512,384,13.14,39.48,28.59
gmlp_b16_224,789.84,1296.449,1024,224,15.78,30.21,73.08
hrnet_w40,787.85,1299.728,1024,224,12.75,25.29,57.56
crossvit_base_240,787.17,975.639,768,240,21.22,36.33,105.03
hrnet_w44,771.15,1327.87,1024,224,14.94,26.92,67.06
swinv2_tiny_window16_256,763.4,670.672,512,256,6.68,39.02,28.35
mobilevitv2_150_384_in22ft1k,757.55,337.918,256,384,9.2,54.25,10.59
xcit_medium_24_p16_224_dist,748.7,1367.689,1024,224,16.13,31.71,84.4
xcit_medium_24_p16_224,748.18,1368.635,1024,224,16.13,31.71,84.4
tresnet_m_448,743.16,1377.885,1024,448,22.94,29.21,31.39
vit_large_r50_s32_224,742.19,1379.692,1024,224,19.58,24.41,328.99
hrnet_w48,738.63,1386.343,1024,224,17.34,28.56,77.47
vit_base_patch16_plus_240,738.11,1387.321,1024,240,27.41,33.08,117.56
sequencer2d_l,736.17,1390.978,1024,224,9.74,22.12,54.3
xcit_small_12_p16_384_dist,715.91,1430.327,1024,384,14.14,36.51,26.25
swinv2_small_window8_256,710.32,1441.594,1024,256,11.58,40.14,49.73
swin_s3_base_224,693.67,1476.198,1024,224,13.69,48.26,71.13
vit_small_patch16_384,692.4,1109.164,768,384,15.52,50.78,22.2
vit_relpos_base_patch16_plus_240,691.79,1480.194,1024,240,27.3,34.33,117.38
tnt_b_patch16_224,691.78,1480.223,1024,224,14.09,39.01,65.41
swinv2_cr_base_224,688.11,1488.125,1024,224,15.86,59.66,87.88
densenet264d_iabn,687.57,1489.287,1024,224,13.47,14.0,72.74
convit_base,685.88,1492.962,1024,224,17.52,31.77,86.54
swinv2_cr_base_ns_224,682.58,1500.17,1024,224,15.86,59.66,87.88
vit_base_patch16_rpn_224,667.73,1533.544,1024,224,17.49,23.75,86.54
densenet264,664.62,1540.716,1024,224,12.95,12.8,72.69
deit3_small_patch16_384,664.03,1156.564,768,384,15.52,50.78,22.21
poolformer_m48,663.83,1542.547,1024,224,11.59,29.17,73.47
deit3_small_patch16_384_in21ft1k,663.62,1157.274,768,384,15.52,50.78,22.21
efficientnet_b3_gn,662.87,386.187,256,320,2.14,28.83,11.73
dpn131,660.11,1551.238,1024,224,16.09,32.97,79.25
eca_nfnet_l1,655.87,1561.27,1024,320,14.92,34.42,41.41
vit_relpos_base_patch16_rpn_224,655.49,1562.186,1024,224,17.51,24.97,86.41
xcit_tiny_24_p8_224,650.45,1574.283,1024,224,9.21,45.39,12.11
xcit_tiny_24_p8_224_dist,649.22,1577.262,1024,224,9.21,45.39,12.11
xcit_nano_12_p8_384_dist,643.06,1592.369,1024,384,6.34,46.08,3.05
nest_base,629.02,813.95,512,224,17.96,53.39,67.72
volo_d2_224,627.91,1630.781,1024,224,14.34,41.34,58.68
mobilevitv2_175_384_in22ft1k,627.52,407.942,256,384,12.47,63.29,14.25
jx_nest_base,621.88,823.3,512,224,17.96,53.39,67.72
vit_small_r26_s32_384,619.54,619.804,384,384,10.43,29.85,36.47
senet154,618.82,1654.743,1024,224,20.77,38.69,115.09
gluon_senet154,618.51,1655.586,1024,224,20.77,38.69,115.09
legacy_senet154,618.16,1656.503,1024,224,20.77,38.69,115.09
xception71,616.97,829.852,512,299,18.09,69.92,42.34
vit_base_r50_s16_224,613.11,1670.152,1024,224,21.66,35.29,98.66
hrnet_w64,609.7,1679.491,1024,224,28.97,35.09,128.06
regnety_320,607.61,842.637,512,224,32.34,30.26,145.05
dpn107,606.08,1689.539,1024,224,18.38,33.46,86.92
regnetz_c16_evos,598.89,854.904,512,320,3.86,25.88,13.49
ecaresnet200d,592.5,1728.248,1024,256,20.0,43.15,64.69
seresnet200d,591.19,1732.085,1024,256,20.01,43.15,71.86
resnet152d,576.9,1774.999,1024,320,24.08,47.67,60.21
convnext_large,559.02,1831.761,1024,224,34.4,43.13,197.77
convnext_large_in22ft1k,558.96,1831.941,1024,224,34.4,43.13,197.77
regnety_160,558.21,687.896,384,288,26.37,38.07,83.59
efficientnet_b3_g8_gn,557.9,458.854,256,320,3.2,28.83,14.25
xcit_small_12_p8_224,546.6,1873.371,1024,224,18.69,47.21,26.21
xcit_small_12_p8_224_dist,546.45,1873.905,1024,224,18.69,47.21,26.21
resnext101_64x4d,541.68,1417.803,768,288,25.66,51.59,83.46
mobilevitv2_200_384_in22ft1k,527.08,364.262,192,384,16.24,72.34,18.45
halonet_h1,518.76,493.471,256,256,3.0,51.17,8.1
vit_large_patch32_384,517.18,1979.967,1024,384,45.31,43.86,306.63
seresnet152d,516.02,1984.399,1024,320,24.09,47.72,66.84
resnetrs152,512.78,1996.941,1024,320,24.34,48.14,86.62
swinv2_base_window8_256,507.32,1513.812,768,256,20.37,52.59,87.92
seresnext101_32x8d,503.19,1526.235,768,288,27.24,51.63,93.57
convnext_small_384_in22ft1k,494.64,1035.087,512,384,25.58,63.37,50.22
seresnext101d_32x8d,494.43,1553.287,768,288,27.64,52.95,93.59
swin_large_patch4_window7_224,478.67,1604.435,768,224,34.53,54.94,196.53
swinv2_small_window16_256,476.38,1074.753,512,256,12.82,66.29,49.73
regnetz_e8,474.49,1618.577,768,320,15.46,63.94,57.7
regnetx_320,471.27,814.799,384,224,31.81,36.3,107.81
ssl_resnext101_32x16d,471.02,1086.983,512,224,36.27,51.18,194.03
swsl_resnext101_32x16d,470.83,1087.428,512,224,36.27,51.18,194.03
ig_resnext101_32x16d,470.74,1087.624,512,224,36.27,51.18,194.03
mixer_l16_224,470.73,2175.315,1024,224,44.6,41.69,208.2
seresnextaa101d_32x8d,463.39,1657.351,768,288,28.51,56.44,93.59
seresnet269d,463.29,2210.273,1024,256,26.59,53.6,113.67
nf_regnet_b5,450.96,1135.344,512,456,11.7,61.95,49.74
efficientnetv2_m,449.82,2276.453,1024,416,18.6,67.5,54.14
volo_d3_224,439.99,2327.294,1024,224,20.78,60.09,86.33
efficientnet_b5,425.78,601.238,256,456,10.46,98.86,30.39
xcit_large_24_p16_224_dist,423.07,2420.403,1024,224,35.86,47.27,189.1
xcit_large_24_p16_224,422.98,2420.908,1024,224,35.86,47.27,189.1
xcit_tiny_12_p8_384_dist,419.35,2441.847,1024,384,14.13,69.14,6.71
resnet200d,417.0,2455.593,1024,320,31.25,67.33,64.69
efficientnetv2_rw_m,411.82,1864.879,768,416,21.49,79.62,53.24
tf_efficientnet_b5_ns,408.16,627.186,256,456,10.46,98.86,30.39
swinv2_cr_tiny_384,408.1,627.286,256,384,15.34,161.01,28.33
tf_efficientnet_b5,407.78,627.773,256,456,10.46,98.86,30.39
tf_efficientnet_b5_ap,407.68,627.936,256,456,10.46,98.86,30.39
swinv2_cr_large_224,405.25,1895.127,768,224,35.1,78.42,196.68
resnetv2_50x1_bitm,401.93,955.37,384,448,16.62,44.46,25.55
nfnet_f1,399.69,2561.946,1024,320,35.97,46.77,132.63
xcit_small_24_p16_384_dist,382.57,2676.633,1024,384,26.72,68.58,47.67
regnetz_d8_evos,376.87,2037.797,768,320,7.03,38.92,23.46
tresnet_l_448,371.52,2756.242,1024,448,43.5,47.56,55.99
vit_large_patch16_224,369.7,2769.802,1024,224,61.6,63.52,304.33
resnetrs200,368.58,2778.22,1024,320,31.51,67.81,93.21
convnext_xlarge_in22ft1k,368.02,1391.221,512,224,60.98,57.5,350.2
crossvit_15_dagger_408,366.37,698.731,256,408,21.45,95.05,28.5
vit_base_patch16_18x2_224,361.96,2829.064,1024,224,52.51,71.38,256.73
deit3_large_patch16_224,358.07,2859.733,1024,224,61.6,63.52,304.37
deit3_large_patch16_224_in21ft1k,357.9,2861.143,1024,224,61.6,63.52,304.37
dm_nfnet_f1,357.87,2146.026,768,320,35.97,46.77,132.63
tf_efficientnetv2_m,350.54,2190.896,768,480,24.76,89.84,54.14
tf_efficientnetv2_m_in21ft1k,350.14,2193.372,768,480,24.76,89.84,54.14
swinv2_base_window16_256,345.6,1111.087,384,256,22.02,84.71,87.92
swinv2_base_window12to16_192to256_22kft1k,345.47,1111.525,384,256,22.02,84.71,87.92
convnext_base_384_in22ft1k,344.56,1485.926,512,384,45.21,84.49,88.59
beit_large_patch16_224,342.32,2991.347,1024,224,61.6,63.52,304.43
eca_nfnet_l2,322.02,2384.947,768,384,30.05,68.28,56.72
volo_d1_384,293.04,1747.159,512,384,22.75,108.55,26.78
convmixer_768_32,292.83,3496.872,1024,224,19.55,25.95,21.11
resnetv2_152x2_bit_teacher,291.46,2634.992,768,224,46.95,45.11,236.34
deit_base_patch16_384,288.65,1330.327,384,384,55.54,101.56,86.86
vit_base_patch16_384,288.47,1331.141,384,384,55.54,101.56,86.86
resnest200e,288.19,1776.58,512,320,35.69,82.78,70.2
xcit_small_24_p8_224,286.12,3578.848,1024,224,35.81,90.78,47.63
xcit_small_24_p8_224_dist,286.06,3579.677,1024,224,35.81,90.78,47.63
deit_base_distilled_patch16_384,284.56,1349.413,384,384,55.65,101.82,87.63
volo_d4_224,282.61,3623.333,1024,224,44.34,80.22,192.96
deit3_base_patch16_384,277.81,1382.217,384,384,55.54,101.56,86.88
deit3_base_patch16_384_in21ft1k,277.78,1382.367,384,384,55.54,101.56,86.88
tresnet_xl_448,277.15,2771.052,768,448,60.65,61.31,78.44
nasnetalarge,276.88,1386.877,384,331,23.89,90.56,88.75
vit_large_patch14_224,271.51,3771.489,1024,224,81.08,88.79,304.2
cait_xxs24_384,269.82,3795.14,1024,384,9.63,122.66,12.03
crossvit_18_dagger_408,269.4,950.247,256,408,32.47,124.87,44.61
xcit_medium_24_p16_384_dist,269.2,2852.889,768,384,47.39,91.64,84.4
pnasnet5large,264.84,1449.925,384,331,25.04,92.89,86.06
resnetv2_101x1_bitm,252.59,1520.226,384,448,31.65,64.93,44.54
efficientnet_b6,252.26,507.392,128,528,19.4,167.39,43.04
swinv2_cr_small_384,250.03,1023.876,256,384,29.7,298.03,49.7
beit_base_patch16_384,247.68,1550.363,384,384,55.54,101.56,86.74
vit_large_r50_s32_384,246.17,1559.866,384,384,57.43,76.52,329.09
tf_efficientnet_b6_ns,242.42,527.986,128,528,19.4,167.39,43.04
tf_efficientnet_b6,242.34,528.179,128,528,19.4,167.39,43.04
tf_efficientnet_b6_ap,242.3,528.255,128,528,19.4,167.39,43.04
ecaresnet269d,241.69,4236.816,1024,352,50.25,101.25,102.09
resnetrs270,234.11,4373.986,1024,352,51.13,105.48,129.86
nfnet_f2,224.73,4556.614,1024,352,63.22,79.06,193.78
swin_base_patch4_window12_384,220.36,871.278,192,384,47.19,134.78,87.9
xcit_tiny_24_p8_384_dist,219.9,4656.678,1024,384,27.05,132.95,12.11
resmlp_big_24_224,218.18,4693.363,1024,224,100.23,87.31,129.14
resmlp_big_24_224_in22ft1k,217.68,4704.164,1024,224,100.23,87.31,129.14
resmlp_big_24_distilled_224,217.65,4704.831,1024,224,100.23,87.31,129.14
swinv2_large_window12to16_192to256_22kft1k,211.96,1207.756,256,256,47.81,121.53,196.74
efficientnetv2_l,206.63,2477.808,512,480,56.4,157.99,118.52
tf_efficientnetv2_l,204.52,2503.355,512,480,56.4,157.99,118.52
tf_efficientnetv2_l_in21ft1k,204.48,2503.917,512,480,56.4,157.99,118.52
ig_resnext101_32x32d,202.59,1263.594,256,224,87.29,91.12,468.53
xcit_medium_24_p8_224,202.12,5066.293,1024,224,63.53,121.23,84.32
xcit_medium_24_p8_224_dist,201.88,5072.196,1024,224,63.53,121.23,84.32
dm_nfnet_f2,200.18,3836.576,768,352,63.22,79.06,193.78
convnext_large_384_in22ft1k,190.55,1343.472,256,384,101.1,126.74,197.77
vit_base_patch8_224,188.25,1359.85,256,224,78.22,161.69,86.58
volo_d5_224,187.56,5459.662,1024,224,72.4,118.11,295.46
cait_xs24_384,186.33,4121.716,768,384,19.28,183.98,26.67
xcit_small_12_p8_384_dist,183.57,2091.823,384,384,54.92,138.29,26.21
eca_nfnet_l3,182.91,2799.141,512,448,52.55,118.4,72.04
cait_xxs36_384,180.41,5675.791,1024,384,14.35,183.7,17.37
swinv2_cr_base_384,178.38,1435.085,256,384,50.57,333.68,87.88
vit_base_resnet50_384,177.85,2159.087,384,384,67.43,135.03,98.95
vit_base_r50_s16_384,177.6,2162.196,384,384,67.43,135.03,98.95
swinv2_cr_huge_224,175.47,2188.347,384,224,115.97,121.08,657.83
convmixer_1536_20,167.1,6128.044,1024,224,48.68,33.03,51.63
volo_d2_384,164.75,1553.889,256,384,46.17,184.51,58.87
resnetrs350,156.77,4898.75,768,384,77.59,154.74,163.96
xcit_large_24_p16_384_dist,154.33,3317.602,512,384,105.35,137.17,189.1
vit_huge_patch14_224,146.32,6998.359,1024,224,167.4,139.41,632.05
efficientnet_b7,145.11,661.558,96,600,38.33,289.94,66.35
cait_s24_384,144.99,3531.336,512,384,32.17,245.31,47.06
deit3_huge_patch14_224,142.26,7197.843,1024,224,167.4,139.41,632.13
deit3_huge_patch14_224_in21ft1k,142.17,7202.758,1024,224,167.4,139.41,632.13
tf_efficientnet_b7_ns,140.64,682.566,96,600,38.33,289.94,66.35
tf_efficientnet_b7_ap,140.61,682.704,96,600,38.33,289.94,66.35
tf_efficientnet_b7,140.6,682.756,96,600,38.33,289.94,66.35
efficientnetv2_xl,139.56,2751.573,384,512,93.85,247.32,208.12
tf_efficientnetv2_xl_in21ft1k,138.42,2774.117,384,512,93.85,247.32,208.12
resnest269e,135.65,2830.833,384,416,77.69,171.98,110.93
swin_large_patch4_window12_384,130.35,981.936,128,384,104.08,202.16,196.74
convnext_xlarge_384_in22ft1k,125.25,1532.9,192,384,179.2,168.99,350.2
nfnet_f3,124.74,4104.555,512,416,115.58,141.78,254.92
ig_resnext101_32x48d,118.28,1623.193,192,224,153.57,131.06,828.41
xcit_large_24_p8_224,115.22,4443.765,512,224,141.23,181.56,188.93
xcit_large_24_p8_224_dist,115.18,4445.056,512,224,141.23,181.56,188.93
resnetrs420,112.12,6849.78,768,416,108.45,213.79,191.89
dm_nfnet_f3,110.18,4647.097,512,416,115.58,141.78,254.92
swinv2_cr_large_384,108.04,1184.75,128,384,108.95,404.96,196.68
resnetv2_50x3_bitm,102.09,1253.798,128,448,145.7,133.37,217.32
resnetv2_152x2_bit_teacher_384,98.91,2588.163,256,384,136.16,132.56,236.34
vit_large_patch16_384,97.45,2626.88,256,384,191.21,270.24,304.72
cait_s36_384,97.05,5275.469,512,384,47.99,367.4,68.37
xcit_small_24_p8_384_dist,96.34,3985.916,384,384,105.24,265.91,47.63
vit_giant_patch14_224,95.73,8022.929,768,224,267.18,192.64,1012.61
deit3_large_patch16_384,94.64,2704.996,256,384,191.21,270.24,304.76
deit3_large_patch16_384_in21ft1k,94.52,2708.314,256,384,191.21,270.24,304.76
swinv2_base_window12to24_192to384_22kft1k,94.37,678.174,64,384,55.25,280.36,87.92
efficientnet_b8,91.29,1051.594,96,672,63.48,442.89,87.41
tf_efficientnet_b8,88.95,1079.277,96,672,63.48,442.89,87.41
tf_efficientnet_b8_ap,88.84,1080.533,96,672,63.48,442.89,87.41
beit_large_patch16_384,84.67,3023.634,256,384,191.21,270.24,305.0
resnetv2_152x2_bitm,73.09,2626.956,192,448,184.99,180.43,236.34
volo_d3_448,72.41,2651.496,192,448,96.33,446.83,86.63
nfnet_f4,69.91,5493.031,384,512,216.26,262.26,316.07
xcit_medium_24_p8_384_dist,67.93,3768.466,256,384,186.67,354.73,84.32
dm_nfnet_f4,62.55,4092.528,256,512,216.26,262.26,316.07
resnetv2_101x3_bitm,61.05,2096.759,128,448,280.33,194.78,387.93
swinv2_large_window12to24_192to384_22kft1k,59.71,803.821,48,384,116.15,407.83,196.74
vit_gigantic_patch14_224,57.59,8890.782,512,224,483.95,275.37,1844.44
tf_efficientnet_l2_ns_475,56.35,1135.833,64,475,172.11,609.89,480.31
volo_d4_448,52.92,2418.622,128,448,197.13,527.35,193.41
swinv2_cr_giant_224,50.53,2532.906,128,224,483.85,309.15,2598.76
nfnet_f5,49.64,5157.064,256,544,290.97,349.71,377.21
swinv2_cr_huge_384,47.06,1360.056,64,384,352.04,583.18,657.94
dm_nfnet_f5,44.17,5795.363,256,544,290.97,349.71,377.21
xcit_large_24_p8_384_dist,38.64,4968.379,192,384,415.0,531.82,188.93
nfnet_f6,37.99,6738.223,256,576,378.69,452.2,438.36
volo_d5_448,36.49,3507.831,128,448,315.06,737.92,295.91
beit_large_patch16_512,33.88,2833.282,96,512,362.24,656.39,305.67
dm_nfnet_f6,33.83,7567.962,256,576,378.69,452.2,438.36
cait_m36_384,31.72,8071.786,256,384,173.11,734.81,271.22
nfnet_f7,30.38,8426.213,256,608,480.39,570.85,499.5
volo_d5_512,25.58,3752.221,96,512,425.09,1105.37,296.09
resnetv2_152x4_bitm,22.67,4234.474,96,480,844.84,414.26,936.53
efficientnet_l2,20.51,1169.975,24,800,479.12,1707.39,480.31
tf_efficientnet_l2_ns,20.15,1191.261,24,800,479.12,1707.39,480.31
swinv2_cr_giant_384,14.62,2188.205,32,384,1450.71,1394.86,2598.76
cait_m48_448,13.47,9503.031,128,448,329.41,1708.23,356.46
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt113-cu117-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,49277.65,20.77,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,45562.75,22.464,1024,224,0.03,0.92,1.59
lcnet_035,41026.68,24.949,1024,224,0.03,1.04,1.64
lcnet_050,37575.13,27.242,1024,224,0.05,1.26,1.88
mobilenetv3_small_075,33062.39,30.961,1024,224,0.05,1.3,2.04
mobilenetv3_small_100,30012.26,34.109,1024,224,0.06,1.42,2.54
tf_mobilenetv3_small_minimal_100,28698.14,35.672,1024,224,0.06,1.41,2.04
tf_mobilenetv3_small_075,27407.51,37.352,1024,224,0.05,1.3,2.04
tinynet_d,27236.47,37.585,1024,152,0.05,1.42,2.34
tf_mobilenetv3_small_100,25103.65,40.781,1024,224,0.06,1.42,2.54
lcnet_075,24140.95,42.406,1024,224,0.1,1.99,2.36
mnasnet_small,20706.43,49.443,1024,224,0.07,2.16,2.03
levit_128s,20595.72,49.709,1024,224,0.31,1.88,7.78
lcnet_100,19684.75,52.01,1024,224,0.16,2.52,2.95
mobilenetv2_035,18358.82,55.767,1024,224,0.07,2.86,1.68
regnetx_002,18244.04,56.117,1024,224,0.2,2.16,2.68
ghostnet_050,17564.96,58.287,1024,224,0.05,1.77,2.59
regnety_002,17006.07,60.202,1024,224,0.2,2.17,3.16
mnasnet_050,15925.32,64.29,1024,224,0.11,3.07,2.22
vit_tiny_r_s16_p8_224,15068.38,67.946,1024,224,0.44,2.06,6.34
mobilenetv2_050,14843.74,68.974,1024,224,0.1,3.64,1.97
tinynet_c,14634.69,69.959,1024,184,0.11,2.87,2.46
semnasnet_050,14248.78,71.855,1024,224,0.11,3.44,2.08
levit_128,14164.26,72.284,1024,224,0.41,2.71,9.21
vit_small_patch32_224,13811.36,74.131,1024,224,1.15,2.5,22.88
mixer_s32_224,13352.85,76.677,1024,224,1.0,2.28,19.1
cs3darknet_focus_s,12798.44,79.999,1024,256,0.69,2.7,3.27
lcnet_150,12783.12,80.094,1024,224,0.34,3.79,4.5
cs3darknet_s,12395.11,82.602,1024,256,0.72,2.97,3.28
regnetx_004,12366.39,82.791,1024,224,0.4,3.14,5.16
mobilenetv3_large_075,12001.32,85.313,1024,224,0.16,4.0,3.99
levit_192,11882.81,86.163,1024,224,0.66,3.2,10.95
resnet10t,11615.84,88.145,1024,224,1.1,2.43,5.44
ese_vovnet19b_slim_dw,11539.4,88.729,1024,224,0.4,5.28,1.9
gernet_s,11496.77,89.058,1024,224,0.75,2.65,8.17
mobilenetv3_rw,10873.77,94.16,1024,224,0.23,4.41,5.48
mobilenetv3_large_100,10705.06,95.645,1024,224,0.23,4.41,5.48
hardcorenas_a,10554.34,97.012,1024,224,0.23,4.38,5.26
tf_mobilenetv3_large_075,10511.12,97.41,1024,224,0.16,4.0,3.99
tf_mobilenetv3_large_minimal_100,10371.16,98.725,1024,224,0.22,4.4,3.92
mnasnet_075,10345.17,98.972,1024,224,0.23,4.77,3.17
hardcorenas_b,9695.74,105.601,1024,224,0.26,5.09,5.18
regnety_004,9655.22,106.046,1024,224,0.41,3.89,4.34
ghostnet_100,9483.99,107.96,1024,224,0.15,3.55,5.18
hardcorenas_c,9481.05,107.994,1024,224,0.28,5.01,5.52
tf_mobilenetv3_large_100,9456.79,108.271,1024,224,0.23,4.41,5.48
regnetx_006,9408.22,108.83,1024,224,0.61,3.98,6.2
mobilenetv2_075,9313.88,109.932,1024,224,0.22,5.86,2.64
tinynet_b,9291.99,110.191,1024,188,0.21,4.44,3.73
mnasnet_b1,9286.4,110.258,1024,224,0.33,5.46,4.38
mnasnet_100,9263.52,110.53,1024,224,0.33,5.46,4.38
gluon_resnet18_v1b,9078.31,112.785,1024,224,1.82,2.48,11.69
semnasnet_075,9069.42,112.895,1024,224,0.23,5.54,2.91
resnet18,9045.63,113.192,1024,224,1.82,2.48,11.69
ssl_resnet18,9045.4,113.196,1024,224,1.82,2.48,11.69
swsl_resnet18,9040.4,113.258,1024,224,1.82,2.48,11.69
levit_256,8921.47,114.768,1024,224,1.13,4.23,18.89
hardcorenas_d,8879.46,115.311,1024,224,0.3,4.93,7.5
regnety_006,8666.48,118.144,1024,224,0.61,4.33,6.06
seresnet18,8542.99,119.851,1024,224,1.82,2.49,11.78
mobilenetv2_100,8507.29,120.356,1024,224,0.31,6.68,3.5
spnasnet_100,8342.04,122.741,1024,224,0.35,6.03,4.42
legacy_seresnet18,8310.8,123.202,1024,224,1.82,2.49,11.78
semnasnet_100,8284.16,123.599,1024,224,0.32,6.23,3.89
mnasnet_a1,8283.57,123.607,1024,224,0.32,6.23,3.89
regnetx_008,7852.75,130.39,1024,224,0.81,5.15,7.26
hardcorenas_f,7809.07,131.117,1024,224,0.35,5.57,8.2
hardcorenas_e,7730.97,132.444,1024,224,0.35,5.65,8.07
efficientnet_lite0,7722.75,132.584,1024,224,0.4,6.74,4.65
levit_256d,7689.03,133.165,1024,224,1.4,4.93,26.21
xcit_nano_12_p16_224_dist,7674.8,133.413,1024,224,0.56,4.17,3.05
xcit_nano_12_p16_224,7670.11,133.492,1024,224,0.56,4.17,3.05
resnet18d,7636.48,134.082,1024,224,2.06,3.29,11.71
ghostnet_130,7625.58,134.274,1024,224,0.24,4.6,7.36
tf_efficientnetv2_b0,7614.25,134.473,1024,224,0.73,4.77,7.14
ese_vovnet19b_slim,7588.4,134.932,1024,224,1.69,3.52,3.17
deit_tiny_distilled_patch16_224,7449.3,137.451,1024,224,1.27,6.01,5.91
deit_tiny_patch16_224,7398.73,138.391,1024,224,1.26,5.97,5.72
vit_tiny_patch16_224,7390.78,138.538,1024,224,1.26,5.97,5.72
regnety_008,7366.88,138.989,1024,224,0.81,5.25,6.26
tinynet_a,7358.6,139.145,1024,192,0.35,5.41,6.19
dla46_c,7311.64,140.038,1024,224,0.58,4.5,1.3
fbnetc_100,7303.94,140.187,1024,224,0.4,6.51,5.57
mobilevitv2_050,7248.37,141.262,1024,256,0.48,8.04,1.37
tf_efficientnet_lite0,6816.26,150.218,1024,224,0.4,6.74,4.65
pit_ti_distilled_224,6788.49,150.832,1024,224,0.71,6.23,5.1
pit_ti_224,6762.99,151.401,1024,224,0.7,6.19,4.85
efficientnet_b0,6687.26,153.115,1024,224,0.4,6.75,5.29
visformer_tiny,6618.81,154.698,1024,224,1.27,5.72,10.32
rexnet_100,6608.65,154.937,1024,224,0.41,7.44,4.8
mnasnet_140,6580.58,155.597,1024,224,0.6,7.71,7.12
efficientnet_b1_pruned,6513.48,157.201,1024,240,0.4,6.21,6.33
rexnetr_100,6491.35,157.737,1024,224,0.43,7.72,4.88
mobilenetv2_110d,6395.98,160.089,1024,224,0.45,8.71,4.52
resnet14t,6341.58,161.462,1024,224,1.69,5.8,10.08
regnetz_005,6208.75,164.916,1024,224,0.52,5.86,7.12
dla46x_c,6145.64,166.61,1024,224,0.54,5.66,1.07
nf_regnet_b0,6055.0,169.104,1024,256,0.64,5.58,8.76
tf_efficientnet_b0,5992.76,170.862,1024,224,0.4,6.75,5.29
hrnet_w18_small,5908.15,173.308,1024,224,1.61,5.72,13.19
edgenext_xx_small,5886.07,173.957,1024,288,0.33,4.21,1.33
semnasnet_140,5856.63,174.833,1024,224,0.6,8.87,6.11
resnetblur18,5839.81,175.336,1024,224,2.34,3.39,11.69
ese_vovnet19b_dw,5825.11,175.779,1024,224,1.34,8.25,6.54
dla60x_c,5790.89,176.817,1024,224,0.59,6.01,1.32
mobilenetv2_140,5780.41,177.139,1024,224,0.6,9.57,6.11
skresnet18,5648.81,181.265,1024,224,1.82,3.24,11.96
mobilevit_xxs,5528.18,185.22,1024,256,0.42,8.34,1.27
efficientnet_b0_gn,5401.88,189.551,1024,224,0.42,6.75,5.29
convnext_atto,5364.13,190.886,1024,288,0.91,6.3,3.7
gluon_resnet34_v1b,5344.34,191.593,1024,224,3.67,3.74,21.8
resnet34,5335.05,191.926,1024,224,3.67,3.74,21.8
efficientnet_lite1,5334.12,191.959,1024,240,0.62,10.14,5.42
tv_resnet34,5332.7,192.011,1024,224,3.67,3.74,21.8
vit_base_patch32_224,5287.0,193.67,1024,224,4.41,5.01,88.22
vit_base_patch32_clip_224,5281.4,193.877,1024,224,4.41,5.01,88.22
levit_384,5276.74,194.047,1024,224,2.36,6.26,39.13
pit_xs_distilled_224,5241.4,195.357,1024,224,1.41,7.76,11.0
pit_xs_224,5237.09,195.517,1024,224,1.4,7.71,10.62
selecsls42,5225.99,195.932,1024,224,2.94,4.62,30.35
selecsls42b,5201.55,196.853,1024,224,2.98,4.62,32.46
gernet_m,5124.67,199.807,1024,224,3.02,5.24,21.14
pvt_v2_b0,5122.72,199.882,1024,224,0.57,7.99,3.67
tf_efficientnetv2_b1,5122.21,199.903,1024,240,1.21,7.34,8.14
mixnet_s,5079.84,201.57,1024,224,0.25,6.25,4.13
convnext_atto_ols,5062.64,202.255,1024,288,0.96,6.8,3.7
seresnet34,5028.88,203.611,1024,224,3.67,3.74,21.96
rexnetr_130,5003.96,204.626,1024,224,0.68,9.81,7.61
fbnetv3_b,5003.0,204.666,1024,256,0.55,9.1,8.6
mixer_b32_224,4982.51,205.508,1024,224,3.24,6.29,60.29
xcit_tiny_12_p16_224_dist,4879.26,209.853,1024,224,1.24,6.29,6.72
legacy_seresnet34,4875.12,210.034,1024,224,3.67,3.74,21.96
xcit_tiny_12_p16_224,4870.16,210.244,1024,224,1.24,6.29,6.72
resnet34d,4834.78,211.786,1024,224,3.91,4.54,21.82
tf_efficientnet_lite1,4822.03,212.348,1024,240,0.62,10.14,5.42
resnet26,4794.98,213.545,1024,224,2.36,7.35,16.0
mobilenetv2_120d,4786.27,213.934,1024,224,0.69,11.97,5.83
rexnet_130,4770.1,214.659,1024,224,0.68,9.71,7.56
efficientnet_b0_g16_evos,4743.69,215.854,1024,224,1.01,7.42,8.11
efficientnet_es,4736.89,216.163,1024,224,1.81,8.73,5.44
efficientnet_es_pruned,4735.25,216.239,1024,224,1.81,8.73,5.44
tf_mixnet_s,4735.17,216.242,1024,224,0.25,6.25,4.13
gmlp_ti16_224,4709.0,217.445,1024,224,1.34,7.55,5.87
convnext_femto,4672.08,219.162,1024,288,1.3,7.56,5.22
mobilevitv2_075,4638.17,220.764,1024,256,1.05,12.06,2.87
resmlp_12_224,4601.92,222.504,1024,224,3.01,5.5,15.35
resmlp_12_distilled_224,4597.97,222.695,1024,224,3.01,5.5,15.35
gmixer_12_224,4543.02,225.388,1024,224,2.67,7.26,12.7
fbnetv3_d,4532.2,225.927,1024,256,0.68,11.1,10.31
tf_efficientnet_es,4518.93,226.591,1024,224,1.81,8.73,5.44
selecsls60,4510.1,227.034,1024,224,3.59,5.52,30.67
mixer_s16_224,4509.29,227.075,1024,224,3.79,5.97,18.53
regnetx_016,4507.02,227.189,1024,224,1.62,7.93,9.19
selecsls60b,4490.35,228.033,1024,224,3.63,5.52,32.77
cs3darknet_focus_m,4487.64,228.171,1024,288,2.51,6.19,9.3
dla34,4481.03,228.505,1024,224,3.07,5.02,15.74
crossvit_tiny_240,4476.83,228.722,1024,240,1.57,9.08,7.01
convnext_femto_ols,4473.25,228.904,1024,288,1.35,8.06,5.23
vit_tiny_r_s16_p8_384,4463.13,229.423,1024,384,1.34,6.49,6.36
cs3darknet_m,4452.94,229.949,1024,288,2.63,6.69,9.31
repvgg_b0,4433.11,230.978,1024,224,3.41,6.15,15.82
resnet26d,4354.59,235.143,1024,224,2.6,8.15,16.01
rexnetr_150,4349.97,235.392,1024,224,0.89,11.13,9.78
resnetaa34d,4309.77,237.588,1024,224,4.43,5.07,21.82
efficientnet_b2_pruned,4309.58,237.598,1024,260,0.73,9.13,8.31
darknet17,4296.61,238.316,1024,256,3.26,7.18,14.3
vit_small_patch32_384,4250.58,240.897,1024,384,3.45,8.25,22.92
crossvit_9_240,4201.98,243.683,1024,240,1.85,9.52,8.55
nf_resnet26,4197.39,243.949,1024,224,2.41,7.35,16.0
efficientnet_b0_g8_gn,4190.39,244.357,1024,224,0.66,6.75,6.56
rexnet_150,4186.31,244.594,1024,224,0.9,11.21,9.73
ecaresnet50d_pruned,4182.62,244.81,1024,224,2.53,6.43,19.94
efficientformer_l1,4075.83,251.225,1024,224,1.3,5.53,12.29
poolformer_s12,4050.19,252.815,1024,224,1.82,5.53,11.92
regnety_016,4035.9,253.712,1024,224,1.63,8.04,11.2
efficientnet_lite2,4013.48,255.128,1024,260,0.89,12.9,6.09
crossvit_9_dagger_240,3992.98,256.437,1024,240,1.99,9.97,8.78
efficientnet_cc_b0_8e,3929.29,260.595,1024,224,0.42,9.42,24.01
efficientnet_cc_b0_4e,3918.01,261.346,1024,224,0.41,9.42,13.31
darknet21,3914.26,261.596,1024,256,3.93,7.47,20.86
efficientnet_b1,3876.9,264.116,1024,256,0.77,12.22,7.79
tf_efficientnet_b1,3834.3,267.052,1024,240,0.71,10.88,7.79
resnest14d,3793.21,269.944,1024,224,2.76,7.33,10.61
sedarknet21,3784.73,270.549,1024,256,3.93,7.47,20.95
resnext26ts,3775.5,271.211,1024,256,2.43,10.52,10.3
tf_efficientnetv2_b2,3727.06,274.735,1024,260,1.72,9.84,10.1
convnext_pico,3702.78,276.537,1024,288,2.27,10.08,9.05
edgenext_x_small,3692.42,277.311,1024,288,0.68,7.5,2.34
tf_efficientnet_cc_b0_8e,3691.33,277.395,1024,224,0.42,9.42,24.01
dpn48b,3689.99,277.494,1024,224,1.69,8.92,9.13
eca_resnext26ts,3675.59,278.583,1024,256,2.43,10.52,10.3
seresnext26ts,3670.33,278.98,1024,256,2.43,10.52,10.39
tf_efficientnet_cc_b0_4e,3665.41,279.357,1024,224,0.41,9.42,13.31
tf_efficientnet_lite2,3662.0,279.618,1024,260,0.89,12.9,6.09
nf_ecaresnet26,3619.99,282.862,1024,224,2.41,7.36,16.0
nf_seresnet26,3618.8,282.955,1024,224,2.41,7.36,17.4
gcresnext26ts,3594.7,284.852,1024,256,2.43,10.53,10.48
mobilevitv2_100,3589.19,213.964,768,256,1.84,16.08,4.9
gernet_l,3556.24,287.933,1024,256,4.57,8.0,31.08
legacy_seresnext26_32x4d,3545.88,288.774,1024,224,2.49,9.39,16.79
convnext_pico_ols,3532.27,289.886,1024,288,2.37,10.74,9.06
resnet26t,3503.33,292.28,1024,256,3.35,10.52,16.01
repvgg_a2,3454.82,296.386,1024,224,5.7,6.26,28.21
mixnet_m,3418.52,299.526,1024,224,0.36,8.19,5.01
efficientnet_b3_pruned,3356.7,305.049,1024,300,1.04,11.86,9.86
nf_regnet_b1,3352.23,305.456,1024,288,1.02,9.2,10.22
ecaresnext50t_32x4d,3339.2,306.649,1024,224,2.7,10.09,15.41
ecaresnext26t_32x4d,3337.18,306.833,1024,224,2.7,10.09,15.41
seresnext26tn_32x4d,3327.66,307.711,1024,224,2.7,10.09,16.81
seresnext26t_32x4d,3327.23,307.751,1024,224,2.7,10.09,16.81
seresnext26d_32x4d,3303.57,309.954,1024,224,2.73,10.19,16.81
tf_mixnet_m,3301.19,310.17,1024,224,0.36,8.19,5.01
convit_tiny,3286.62,311.554,1024,224,1.26,7.94,5.71
mobilevit_xs,3278.19,234.265,768,256,1.05,16.33,2.32
pit_s_224,3268.88,313.245,1024,224,2.88,11.56,23.46
pit_s_distilled_224,3266.72,313.452,1024,224,2.9,11.64,24.04
skresnet34,3242.45,315.8,1024,224,3.67,5.13,22.28
eca_botnext26ts_256,3224.24,317.583,1024,256,2.46,11.6,10.59
ecaresnet101d_pruned,3223.88,317.616,1024,224,3.48,7.69,24.88
deit_small_distilled_patch16_224,3220.79,317.922,1024,224,4.63,12.02,22.44
ecaresnetlight,3215.57,318.439,1024,224,4.11,8.42,30.16
deit_small_patch16_224,3209.05,319.085,1024,224,4.61,11.95,22.05
vit_small_patch16_224,3199.98,319.99,1024,224,4.61,11.95,22.05
eca_halonext26ts,3173.71,322.639,1024,256,2.44,11.46,10.76
convnextv2_atto,3162.98,323.733,1024,288,0.91,6.3,3.71
resnetv2_50,3158.28,324.214,1024,224,4.11,11.11,25.55
nf_regnet_b2,3133.63,326.765,1024,272,1.22,9.27,14.31
rexnetr_200,3133.12,245.111,768,224,1.59,15.11,16.52
botnet26t_256,3123.98,327.772,1024,256,3.32,11.98,12.49
coat_lite_tiny,3113.54,328.874,1024,224,1.6,11.65,5.72
vit_small_r26_s32_224,3112.34,329.001,1024,224,3.56,9.85,36.43
bat_resnext26ts,3103.95,329.89,1024,256,2.53,12.51,10.73
halonet26t,3103.39,329.95,1024,256,3.19,11.69,12.48
pvt_v2_b1,3095.14,330.828,1024,224,2.12,15.39,14.01
cspresnet50,3063.22,334.278,1024,256,4.54,11.5,21.62
resnet32ts,3055.79,335.09,1024,256,4.63,11.58,17.96
rexnet_200,3051.5,251.668,768,224,1.56,14.91,16.37
lambda_resnet26t,3046.2,336.144,1024,256,3.02,11.87,10.96
ssl_resnet50,3030.48,337.887,1024,224,4.11,11.11,25.56
gluon_resnet50_v1b,3027.43,338.23,1024,224,4.11,11.11,25.56
tv_resnet50,3027.39,338.232,1024,224,4.11,11.11,25.56
swsl_resnet50,3027.07,338.268,1024,224,4.11,11.11,25.56
resnet50,3025.4,338.455,1024,224,4.11,11.11,25.56
deit3_small_patch16_224_in21ft1k,3023.02,338.721,1024,224,4.61,11.95,22.06
deit3_small_patch16_224,3017.77,339.312,1024,224,4.61,11.95,22.06
tresnet_m,3006.54,340.578,1024,224,5.74,7.31,31.39
resnet33ts,3005.78,340.665,1024,256,4.76,11.66,19.68
vit_small_resnet26d_224,2994.08,341.995,1024,224,5.07,11.12,63.61
resnetv2_50t,2989.06,342.569,1024,224,4.32,11.82,25.57
regnetx_032,2988.15,342.675,1024,224,3.2,11.37,15.3
dpn68b,2981.13,343.481,1024,224,2.35,10.47,12.61
hrnet_w18_small_v2,2978.67,343.765,1024,224,2.62,9.65,15.6
dpn68,2975.29,344.155,1024,224,2.35,10.47,12.61
resnetv2_50d,2971.15,344.633,1024,224,4.35,11.92,25.57
efficientnet_em,2938.12,348.51,1024,240,3.04,14.34,6.9
vit_base_patch32_plus_256,2934.64,348.925,1024,256,7.79,7.76,119.48
coat_lite_mini,2921.75,350.462,1024,224,2.0,12.25,11.01
tf_efficientnet_b2,2919.63,350.718,1024,260,1.02,13.83,9.11
seresnet33ts,2919.51,350.732,1024,256,4.76,11.66,19.78
eca_resnet33ts,2917.21,351.008,1024,256,4.76,11.66,19.68
haloregnetz_b,2890.29,354.276,1024,224,1.97,11.94,11.68
coatnet_pico_rw_224,2884.58,354.98,1024,224,2.05,14.62,10.85
dla60,2883.99,355.049,1024,224,4.26,10.16,22.04
gluon_resnet50_v1c,2872.58,356.463,1024,224,4.35,11.92,25.58
resnet50t,2869.49,356.844,1024,224,4.32,11.82,25.57
gcresnet33ts,2863.36,357.609,1024,256,4.76,11.68,19.88
gluon_resnet50_v1d,2853.24,358.879,1024,224,4.35,11.92,25.58
cspresnet50d,2852.98,358.911,1024,256,4.86,12.55,21.64
resnet50d,2850.55,359.218,1024,224,4.35,11.92,25.58
vovnet39a,2845.31,359.878,1024,224,7.09,6.73,22.6
cspresnet50w,2835.31,361.148,1024,256,5.04,12.19,28.12
vgg11,2827.53,362.143,1024,224,7.61,7.44,132.86
tf_efficientnet_em,2826.28,362.303,1024,240,3.04,14.34,6.9
visformer_small,2818.88,363.251,1024,224,4.88,11.43,40.22
vit_relpos_small_patch16_224,2792.87,366.637,1024,224,4.59,13.05,21.98
vit_relpos_base_patch32_plus_rpn_256,2784.26,367.771,1024,256,7.68,8.01,119.42
vit_srelpos_small_patch16_224,2781.72,368.106,1024,224,4.59,12.16,21.97
resnest26d,2772.97,369.267,1024,224,3.64,9.97,17.07
cs3darknet_focus_l,2770.5,369.596,1024,288,5.9,10.16,21.15
efficientnet_b2a,2767.64,369.979,1024,288,1.12,16.2,9.11
efficientnet_b2,2766.98,370.065,1024,288,1.12,16.2,9.11
ese_vovnet39b,2760.12,370.986,1024,224,7.09,6.74,24.57
legacy_seresnet50,2753.49,371.881,1024,224,3.88,10.6,28.09
densenet121,2749.79,372.378,1024,224,2.87,6.9,7.98
tv_densenet121,2747.16,372.735,1024,224,2.87,6.9,7.98
eca_vovnet39b,2736.53,374.185,1024,224,7.09,6.74,22.6
coatnet_nano_cc_224,2716.19,376.986,1024,224,2.24,15.02,13.76
convnextv2_femto,2710.95,377.714,1024,288,1.3,7.56,5.23
resnetv2_50x1_bit_distilled,2704.93,378.554,1024,224,4.23,11.11,25.55
selecsls84,2697.2,379.64,1024,224,5.9,7.57,50.95
flexivit_small,2693.55,380.153,1024,240,5.35,14.18,22.06
twins_svt_small,2691.25,380.48,1024,224,2.94,13.75,24.06
mixnet_l,2678.25,382.327,1024,224,0.58,10.84,7.33
seresnet50,2674.61,382.848,1024,224,4.11,11.13,28.09
xcit_nano_12_p16_384_dist,2668.39,383.74,1024,384,1.64,12.15,3.05
cs3darknet_l,2649.93,386.412,1024,288,6.16,10.83,21.16
coatnet_nano_rw_224,2633.36,388.844,1024,224,2.41,15.41,15.14
coatnext_nano_rw_224,2627.24,389.75,1024,224,2.47,12.8,14.7
xcit_tiny_24_p16_224_dist,2617.14,391.253,1024,224,2.34,11.82,12.12
densenet121d,2616.98,391.278,1024,224,3.11,7.7,8.0
xcit_tiny_24_p16_224,2614.91,391.584,1024,224,2.34,11.82,12.12
resnet50_gn,2599.07,393.975,1024,224,4.14,11.11,25.56
vit_relpos_small_patch16_rpn_224,2596.73,394.33,1024,224,4.59,13.05,21.97
res2net50_48w_2s,2593.21,394.865,1024,224,4.18,11.72,25.29
mobilevit_s,2587.93,296.749,768,256,2.03,19.94,5.58
convnext_nano,2579.36,396.983,1024,288,4.06,13.84,15.59
tf_mixnet_l,2577.4,397.288,1024,224,0.58,10.84,7.33
resnetaa50d,2573.35,397.912,1024,224,5.39,12.44,25.58
vgg11_bn,2556.04,400.607,1024,224,7.62,7.44,132.87
seresnet50t,2550.33,401.504,1024,224,4.32,11.83,28.1
ecaresnet50d,2544.16,402.478,1024,224,4.35,11.93,25.58
gcvit_xxtiny,2518.13,406.639,1024,224,2.14,15.36,12.0
cs3sedarknet_l,2502.51,409.176,1024,288,6.16,10.83,21.91
resnetrs50,2497.73,409.96,1024,224,4.48,12.14,35.69
mobilevitv2_125,2489.87,308.438,768,256,2.86,20.1,7.48
resnetblur50,2484.87,412.08,1024,224,5.16,12.02,25.56
cspresnext50,2483.24,412.352,1024,256,4.05,15.86,20.57
gluon_resnet50_v1s,2459.02,416.413,1024,224,5.47,13.52,25.68
efficientnet_cc_b1_8e,2458.85,416.443,1024,240,0.75,15.44,39.72
vit_base_resnet26d_224,2458.01,416.584,1024,224,6.97,13.16,101.4
densenetblur121d,2444.58,418.873,1024,224,3.11,7.9,8.0
tv_resnext50_32x4d,2431.41,421.143,1024,224,4.26,14.4,25.03
ssl_resnext50_32x4d,2431.35,421.155,1024,224,4.26,14.4,25.03
swsl_resnext50_32x4d,2430.87,421.236,1024,224,4.26,14.4,25.03
resnext50_32x4d,2429.56,421.462,1024,224,4.26,14.4,25.03
gluon_resnext50_32x4d,2428.35,421.674,1024,224,4.26,14.4,25.03
dla60x,2414.82,424.035,1024,224,3.54,13.8,17.35
efficientnet_lite3,2407.43,212.664,512,300,1.65,21.85,8.2
regnetx_040,2406.98,425.416,1024,224,3.99,12.2,22.12
semobilevit_s,2404.63,319.371,768,256,2.03,19.95,5.74
gcresnext50ts,2402.57,426.196,1024,256,3.75,15.46,15.67
regnety_040s_gn,2385.11,429.317,1024,224,4.03,12.29,20.65
resnetblur50d,2367.52,432.507,1024,224,5.4,12.82,25.58
vovnet57a,2360.79,433.737,1024,224,8.95,7.52,36.64
tf_efficientnet_cc_b1_8e,2357.71,434.307,1024,240,0.75,15.44,39.72
resmlp_24_distilled_224,2351.85,435.39,1024,224,5.96,10.91,30.02
resmlp_24_224,2345.81,436.509,1024,224,5.96,10.91,30.02
res2net50_14w_8s,2341.48,437.317,1024,224,4.21,13.28,25.06
coatnet_rmlp_nano_rw_224,2340.53,437.494,1024,224,2.62,20.34,15.15
sehalonet33ts,2339.44,328.271,768,256,3.55,14.7,13.69
res2net50_26w_4s,2338.49,437.876,1024,224,4.28,12.61,25.7
convnext_nano_ols,2328.37,439.779,1024,288,4.38,15.5,15.65
lambda_resnet26rpt_256,2324.88,165.158,384,256,3.16,11.87,10.99
gmixer_24_224,2324.82,440.451,1024,224,5.28,14.45,24.72
gcresnet50t,2321.78,441.028,1024,256,5.42,14.67,25.9
resnext50d_32x4d,2317.05,441.929,1024,224,4.5,15.2,25.05
resnest50d_1s4x24d,2309.9,443.296,1024,224,4.43,13.57,25.68
seresnetaa50d,2309.78,443.319,1024,224,5.4,12.46,28.11
dla60_res2net,2301.91,444.834,1024,224,4.15,12.34,20.85
vit_base_r26_s32_224,2301.77,444.864,1024,224,6.81,12.36,101.38
twins_pcpvt_small,2290.09,447.132,1024,224,3.83,18.08,24.11
regnetz_b16,2286.62,447.81,1024,288,2.39,16.43,9.72
ese_vovnet57b,2267.23,451.64,1024,224,8.95,7.52,38.61
gluon_inception_v3,2265.31,452.024,1024,299,5.73,8.97,23.83
inception_v3,2260.97,452.888,1024,299,5.73,8.97,23.83
adv_inception_v3,2258.89,453.305,1024,299,5.73,8.97,23.83
tf_inception_v3,2255.73,453.943,1024,299,5.73,8.97,23.83
densenet169,2232.91,458.582,1024,224,3.4,7.3,14.15
tf_efficientnetv2_b3,2223.64,460.493,1024,300,3.04,15.74,14.36
nf_ecaresnet50,2211.52,463.019,1024,224,4.21,11.13,25.56
nf_seresnet50,2207.21,463.921,1024,224,4.21,11.13,28.09
skresnet50,2206.75,464.017,1024,224,4.11,12.5,25.8
edgenext_small,2206.31,464.109,1024,320,1.97,14.16,5.59
seresnext50_32x4d,2197.09,466.058,1024,224,4.26,14.42,27.56
gluon_seresnext50_32x4d,2196.94,466.091,1024,224,4.26,14.42,27.56
xcit_small_12_p16_224_dist,2195.81,466.33,1024,224,4.82,12.58,26.25
legacy_seresnext50_32x4d,2193.34,466.856,1024,224,4.26,14.42,27.56
xcit_small_12_p16_224,2190.16,467.534,1024,224,4.82,12.58,26.25
repvgg_b1g4,2188.83,467.817,1024,224,8.15,10.64,39.97
tf_efficientnet_lite3,2188.37,233.953,512,300,1.65,21.85,8.2
efficientnetv2_rw_t,2170.03,471.87,1024,288,3.19,16.42,13.65
gmlp_s16_224,2164.56,473.061,1024,224,4.42,15.1,19.42
dla60_res2next,2126.26,481.583,1024,224,3.49,13.17,17.03
gc_efficientnetv2_rw_t,2126.09,481.621,1024,288,3.2,16.45,13.68
skresnet50d,2112.57,484.703,1024,224,4.36,13.31,25.82
mobilevitv2_150,2105.0,243.219,512,256,4.09,24.11,10.59
mobilevitv2_150_in22ft1k,2104.51,243.274,512,256,4.09,24.11,10.59
convnextv2_pico,2092.16,489.434,1024,288,2.27,10.08,9.07
poolformer_s24,2090.38,489.851,1024,224,3.41,10.68,21.39
cs3sedarknet_xdw,2090.04,489.929,1024,256,5.97,17.18,21.6
res2next50,2085.23,491.055,1024,224,4.2,13.71,24.67
cspdarknet53,2084.51,491.231,1024,256,6.57,16.81,27.64
fbnetv3_g,2084.48,491.238,1024,288,1.77,21.09,16.62
crossvit_small_240,2074.04,493.709,1024,240,5.63,18.17,26.86
deit3_medium_patch16_224_in21ft1k,2064.27,496.046,1024,224,8.0,15.93,38.85
deit3_medium_patch16_224,2063.34,496.268,1024,224,8.0,15.93,38.85
xcit_nano_12_p8_224_dist,2049.01,499.742,1024,224,2.16,15.71,3.05
xcit_nano_12_p8_224,2044.48,500.848,1024,224,2.16,15.71,3.05
nf_regnet_b3,2035.39,503.085,1024,320,2.05,14.61,18.59
cs3darknet_focus_x,2017.73,507.488,1024,256,8.03,10.69,35.02
vit_relpos_medium_patch16_cls_224,2000.38,511.89,1024,224,8.03,18.24,38.76
lambda_resnet50ts,1991.21,514.246,1024,256,5.07,17.48,21.54
swin_tiny_patch4_window7_224,1978.72,517.495,1024,224,4.51,17.06,28.29
sebotnet33ts_256,1959.75,195.932,384,256,3.89,17.46,13.7
coatnet_0_rw_224,1957.32,523.148,1024,224,4.43,18.73,27.44
ecaresnet26t,1953.32,524.224,1024,320,5.24,16.44,16.01
regnetx_080,1942.5,527.144,1024,224,8.02,14.06,39.57
gcvit_xtiny,1941.57,527.393,1024,224,2.93,20.26,19.98
resnetv2_101,1925.46,531.806,1024,224,7.83,16.23,44.54
regnetx_064,1920.06,533.303,1024,224,6.49,16.37,26.21
mixnet_xl,1918.85,533.64,1024,224,0.93,14.57,11.9
edgenext_small_rw,1912.9,535.3,1024,320,2.46,14.85,7.83
vit_relpos_medium_patch16_224,1907.96,536.687,1024,224,7.97,17.02,38.75
vit_srelpos_medium_patch16_224,1900.57,538.773,1024,224,7.96,16.21,38.74
resnest50d,1896.74,539.858,1024,224,5.4,14.36,27.48
crossvit_15_240,1894.86,540.397,1024,240,5.81,19.77,27.53
vit_base_resnet50d_224,1892.78,540.989,1024,224,8.73,16.92,110.97
gluon_resnet101_v1b,1879.26,544.883,1024,224,7.83,16.23,44.55
tv_resnet101,1878.26,545.172,1024,224,7.83,16.23,44.55
resnet101,1875.25,546.047,1024,224,7.83,16.23,44.55
dla102,1873.79,546.472,1024,224,7.19,14.18,33.27
efficientformer_l3,1868.08,548.142,1024,224,3.93,12.01,31.41
maxvit_rmlp_pico_rw_256,1866.73,411.402,768,256,1.85,24.86,7.52
resnetv2_101d,1855.94,551.727,1024,224,8.07,17.04,44.56
pvt_v2_b2,1835.92,557.745,1024,224,4.05,27.53,25.36
maxvit_pico_rw_256,1829.44,419.787,768,256,1.83,22.3,7.46
vgg13,1820.36,562.512,1024,224,11.31,12.25,133.05
lamhalobotnet50ts_256,1818.57,563.067,1024,256,5.02,18.44,22.57
crossvit_15_dagger_240,1817.96,563.255,1024,240,6.13,20.43,28.21
gluon_resnet101_v1c,1816.14,563.82,1024,224,8.08,17.04,44.57
res2net50_26w_6s,1811.81,565.168,1024,224,6.33,15.28,37.05
gluon_resnet101_v1d,1808.21,566.295,1024,224,8.08,17.04,44.57
swin_s3_tiny_224,1803.67,567.72,1024,224,4.64,19.13,28.33
coatnet_rmlp_0_rw_224,1803.63,567.733,1024,224,4.72,24.89,27.45
vit_relpos_medium_patch16_rpn_224,1770.72,578.284,1024,224,7.97,17.02,38.73
halonet50ts,1765.73,579.917,1024,256,5.3,19.2,22.73
repvgg_b1,1760.92,581.5,1024,224,13.16,10.64,57.42
coatnet_bn_0_rw_224,1753.99,583.799,1024,224,4.67,22.04,27.44
wide_resnet50_2,1747.87,585.844,1024,224,11.43,14.4,68.88
efficientnet_b3,1741.21,294.036,512,320,2.01,26.52,12.23
efficientnet_b3a,1740.84,294.1,512,320,2.01,26.52,12.23
densenet201,1738.22,589.096,1024,224,4.34,7.85,20.01
coatnet_0_224,1727.45,296.376,512,224,4.58,24.01,25.04
darknetaa53,1721.33,594.876,1024,288,10.08,15.68,36.02
tf_efficientnet_b3,1720.61,297.558,512,300,1.87,23.83,12.23
cait_xxs24_224,1720.1,595.301,1024,224,2.53,20.29,11.96
vit_large_patch32_224,1718.53,595.845,1024,224,15.41,13.32,327.9
mobilevitv2_175,1697.71,301.572,512,256,5.54,28.13,14.25
mobilevitv2_175_in22ft1k,1697.51,301.606,512,256,5.54,28.13,14.25
xcit_tiny_12_p16_384_dist,1694.92,604.145,1024,384,3.64,18.26,6.72
pvt_v2_b2_li,1694.45,604.311,1024,224,3.91,27.6,22.55
coat_lite_small,1694.41,604.328,1024,224,3.96,22.09,19.84
resnetaa101d,1692.59,604.976,1024,224,9.12,17.56,44.57
legacy_seresnet101,1686.93,607.005,1024,224,7.61,15.74,49.33
tresnet_v2_l,1685.52,607.515,1024,224,8.81,16.34,46.17
hrnet_w18,1679.12,609.832,1024,224,4.32,16.31,21.3
vit_medium_patch16_gap_240,1667.0,614.264,1024,240,9.22,18.81,44.4
vit_tiny_patch16_384,1660.88,616.528,1024,384,4.7,25.39,5.79
regnetv_040,1659.81,616.926,1024,288,6.6,20.3,20.64
convnext_tiny_hnf,1659.73,616.951,1024,288,7.39,22.21,28.59
seresnet101,1655.13,618.666,1024,224,7.84,16.27,49.33
vit_base_patch32_384,1651.29,620.109,1024,384,13.06,16.5,88.3
vit_base_patch32_clip_384,1649.72,620.7,1024,384,13.06,16.5,88.3
regnety_040,1647.66,621.47,1024,288,6.61,20.3,20.65
regnety_032,1645.25,622.383,1024,288,5.29,18.61,19.44
gluon_resnet101_v1s,1642.29,623.505,1024,224,9.19,18.64,44.67
vgg13_bn,1634.19,626.596,1024,224,11.33,12.25,133.05
resnetaa50,1631.05,627.803,1024,288,8.52,19.24,25.56
mixer_b16_224_miil,1628.71,628.706,1024,224,12.62,14.53,59.88
mixer_b16_224,1627.79,629.061,1024,224,12.62,14.53,59.88
convnext_tiny,1626.95,629.384,1024,288,7.39,22.21,28.59
nf_resnet101,1620.77,631.785,1024,224,8.01,16.23,44.55
swinv2_cr_tiny_224,1618.15,632.807,1024,224,4.66,28.45,28.33
ecaresnet101d,1609.33,636.276,1024,224,8.08,17.07,44.57
twins_pcpvt_base,1605.41,637.831,1024,224,6.68,25.25,43.83
dla102x,1601.78,639.274,1024,224,5.89,19.42,26.31
ese_vovnet39b_evos,1601.47,639.4,1024,224,7.07,6.74,24.58
darknet53,1597.03,641.177,1024,288,11.78,15.68,41.61
resnetblur101d,1596.24,641.494,1024,224,9.12,17.94,44.57
resnet51q,1592.08,643.172,1024,288,8.07,20.94,35.7
swinv2_cr_tiny_ns_224,1591.39,643.448,1024,224,4.66,28.45,28.33
mixer_l32_224,1583.03,646.85,1024,224,11.27,19.86,206.94
resmlp_36_distilled_224,1577.86,648.967,1024,224,8.91,16.33,44.69
resmlp_36_224,1577.4,649.158,1024,224,8.91,16.33,44.69
resnetv2_50d_gn,1561.87,655.61,1024,288,7.24,19.7,25.57
botnet50ts_256,1556.81,246.643,384,256,5.54,22.23,22.74
nf_resnet50,1548.83,661.132,1024,288,6.88,18.37,25.56
resnetv2_50d_frn,1547.35,661.764,1024,224,4.33,11.92,25.59
halo2botnet50ts_256,1546.64,496.545,768,256,5.02,21.78,22.64
mvitv2_tiny,1534.63,667.247,1024,224,4.7,21.16,24.17
gluon_resnext101_32x4d,1505.04,680.366,1024,224,8.01,21.23,44.18
swsl_resnext101_32x4d,1504.46,680.63,1024,224,8.01,21.23,44.18
cs3darknet_x,1504.38,680.665,1024,288,10.6,14.36,35.05
ssl_resnext101_32x4d,1503.93,680.869,1024,224,8.01,21.23,44.18
resnext101_32x4d,1503.63,681.005,1024,224,8.01,21.23,44.18
resnest50d_4s2x40d,1497.58,683.755,1024,224,4.4,17.94,30.42
convnextv2_nano,1488.75,515.858,768,288,4.06,13.84,15.62
skresnext50_32x4d,1478.83,692.427,1024,224,4.5,17.18,27.48
mobilevitv2_200,1478.44,519.454,768,256,7.22,32.15,18.45
tresnet_l,1477.44,693.076,1024,224,10.88,11.9,55.99
mobilevitv2_200_in22ft1k,1477.37,519.83,768,256,7.22,32.15,18.45
vgg16,1475.59,693.946,1024,224,15.47,13.56,138.36
regnetz_c16,1475.58,693.953,1024,320,3.92,25.88,13.46
resnetv2_50d_evob,1468.61,697.244,1024,224,4.33,11.92,25.59
vit_medium_patch16_gap_256,1467.03,697.996,1024,256,10.59,22.15,38.86
res2net50_26w_8s,1466.52,698.239,1024,224,8.37,17.95,48.4
sequencer2d_s,1465.84,698.562,1024,224,4.96,11.31,27.65
eca_nfnet_l0,1461.61,700.586,1024,288,7.12,17.29,24.14
nfnet_l0,1460.27,701.228,1024,288,7.13,17.29,35.07
cs3sedarknet_x,1435.72,713.217,1024,288,10.6,14.37,35.4
resnet61q,1434.01,714.068,1024,288,9.87,21.52,36.85
res2net101_26w_4s,1424.71,718.728,1024,224,8.1,18.45,45.21
repvgg_b2g4,1415.15,723.581,1024,224,12.63,12.9,61.76
nest_tiny,1413.2,543.434,768,224,5.83,25.48,17.06
poolformer_s36,1408.65,726.922,1024,224,5.0,15.82,30.86
maxvit_rmlp_nano_rw_256,1404.06,546.971,768,256,4.47,31.92,15.5
convit_small,1397.72,732.608,1024,224,5.76,17.87,27.78
jx_nest_tiny,1387.89,553.347,768,224,5.83,25.48,17.06
maxvit_nano_rw_256,1378.18,557.246,768,256,4.46,30.28,15.45
nf_ecaresnet101,1373.28,745.649,1024,224,8.01,16.27,44.55
nf_seresnet101,1369.04,747.958,1024,224,8.02,16.27,49.33
gluon_seresnext101_32x4d,1358.35,753.84,1024,224,8.02,21.26,48.96
legacy_seresnext101_32x4d,1357.27,754.442,1024,224,8.02,21.26,48.96
efficientnet_b3_gn,1357.0,282.964,384,320,2.14,28.83,11.73
nfnet_f0,1356.65,754.786,1024,256,12.62,18.05,71.49
seresnext101_32x4d,1356.0,755.148,1024,224,8.02,21.26,48.96
resnetv2_152,1353.28,756.668,1024,224,11.55,22.56,60.19
xception,1353.17,567.542,768,299,8.4,35.83,22.86
twins_svt_base,1350.54,758.199,1024,224,8.59,26.33,56.07
crossvit_18_240,1343.82,761.996,1024,240,9.05,26.26,43.27
ese_vovnet99b_iabn,1343.72,762.049,1024,224,16.49,11.27,63.2
maxxvit_rmlp_nano_rw_256,1341.45,763.341,1024,256,4.37,26.05,16.78
regnetx_120,1339.05,764.708,1024,224,12.13,21.37,46.11
vgg16_bn,1336.79,765.998,1024,224,15.5,13.56,138.37
dpn92,1330.6,769.562,1024,224,6.54,18.21,37.67
tv_resnet152,1329.75,770.054,1024,224,11.56,22.56,60.19
gcvit_tiny,1328.61,770.718,1024,224,4.79,29.82,28.22
gluon_resnet152_v1b,1328.2,770.954,1024,224,11.56,22.56,60.19
resnet152,1327.13,771.578,1024,224,11.56,22.56,60.19
ese_vovnet99b,1316.93,777.554,1024,224,16.51,11.27,63.2
pvt_v2_b3,1316.31,777.917,1024,224,6.92,37.7,45.24
xcit_tiny_12_p8_224_dist,1300.55,787.348,1024,224,4.81,23.6,6.71
xcit_tiny_12_p8_224,1299.96,787.704,1024,224,4.81,23.6,6.71
crossvit_18_dagger_240,1298.96,788.312,1024,240,9.5,27.03,44.27
hrnet_w32,1297.82,789.002,1024,224,8.97,22.02,41.23
gluon_resnet152_v1c,1296.47,789.825,1024,224,11.8,23.36,60.21
resnetv2_152d,1296.37,789.881,1024,224,11.8,23.36,60.2
gluon_resnet152_v1d,1293.21,791.811,1024,224,11.8,23.36,60.21
vit_small_resnet50d_s16_224,1288.35,794.801,1024,224,13.48,24.82,57.53
cs3edgenet_x,1281.15,799.266,1024,288,14.59,16.36,47.82
edgenext_base,1272.74,804.548,1024,320,6.01,24.32,18.51
regnety_120,1268.38,807.318,1024,224,12.14,21.38,51.82
dla169,1258.34,813.753,1024,224,11.6,20.2,53.39
hrnet_w30,1252.2,817.74,1024,224,8.15,21.21,37.71
xception41p,1249.06,409.896,512,299,9.25,39.86,26.91
maxxvitv2_nano_rw_256,1248.81,819.967,1024,256,6.26,23.05,23.7
ecaresnet50t,1243.91,823.198,1024,320,8.82,24.13,25.57
vgg19,1237.03,827.774,1024,224,19.63,14.86,143.67
swin_small_patch4_window7_224,1228.67,833.406,1024,224,8.77,27.47,49.61
efficientnet_el_pruned,1220.93,838.69,1024,300,8.0,30.7,10.59
densenet161,1220.41,839.05,1024,224,7.79,11.06,28.68
efficientnet_el,1218.76,840.187,1024,300,8.0,30.7,10.59
deit_base_distilled_patch16_224,1211.4,845.292,1024,224,17.68,24.05,87.34
vit_base_patch16_224,1209.0,846.969,1024,224,17.58,23.9,86.57
vit_base_patch16_224_miil,1208.72,847.163,1024,224,17.59,23.91,94.4
deit_base_patch16_224,1208.56,847.275,1024,224,17.58,23.9,86.57
vit_base_patch16_clip_224,1205.77,849.236,1024,224,17.58,23.9,86.57
gluon_resnet152_v1s,1205.41,849.488,1024,224,12.92,24.96,60.32
coatnet_rmlp_1_rw_224,1201.89,851.979,1024,224,7.85,35.47,41.69
maxvit_tiny_rw_224,1200.3,853.107,1024,224,5.11,33.11,29.06
mixnet_xxl,1193.04,643.721,768,224,2.04,23.43,23.96
tf_efficientnet_el,1192.11,858.967,1024,300,8.0,30.7,10.59
swinv2_tiny_window8_256,1191.01,859.761,1024,256,5.96,24.57,28.35
volo_d1_224,1190.57,860.079,1024,224,6.94,24.43,26.63
repvgg_b2,1183.91,864.916,1024,224,20.45,12.9,89.02
legacy_seresnet152,1181.09,866.978,1024,224,11.33,22.08,66.82
xcit_small_24_p16_224_dist,1175.31,871.245,1024,224,9.1,23.64,47.67
xcit_small_24_p16_224,1174.76,871.656,1024,224,9.1,23.64,47.67
inception_v4,1168.76,876.127,1024,299,12.28,15.09,42.68
seresnet152,1166.02,878.19,1024,224,11.57,22.61,66.82
twins_pcpvt_large,1163.18,880.331,1024,224,9.84,35.82,60.99
deit3_base_patch16_224,1159.4,883.201,1024,224,17.58,23.9,86.59
deit3_base_patch16_224_in21ft1k,1159.14,883.404,1024,224,17.58,23.9,86.59
cait_xxs36_224,1156.4,885.493,1024,224,3.77,30.34,17.3
vit_base_patch32_clip_448,1154.9,886.645,1024,448,17.93,23.9,88.34
regnetx_160,1153.07,888.048,1024,224,15.99,25.52,54.28
dm_nfnet_f0,1152.75,888.293,1024,256,12.62,18.05,71.49
sequencer2d_m,1147.71,892.201,1024,224,6.55,14.26,38.31
repvgg_b3g4,1145.87,893.631,1024,224,17.89,15.1,83.83
mvitv2_small_cls,1144.7,894.542,1024,224,7.04,28.17,34.87
mvitv2_small,1143.83,895.224,1024,224,7.0,28.08,34.87
efficientnet_lite4,1139.64,336.935,384,380,4.04,45.66,13.01
tnt_s_patch16_224,1135.12,902.091,1024,224,5.24,24.37,23.76
convmixer_1024_20_ks9_p14,1130.85,905.497,1024,224,5.55,5.51,24.38
vgg19_bn,1127.16,908.464,1024,224,19.66,14.86,143.68
vit_relpos_base_patch16_clsgap_224,1124.58,910.547,1024,224,17.6,25.12,86.43
vit_relpos_base_patch16_cls_224,1122.76,912.026,1024,224,17.6,25.12,86.43
coatnet_rmlp_1_rw2_224,1119.61,914.591,1024,224,8.11,40.13,41.72
beit_base_patch16_224,1109.32,923.073,1024,224,17.58,23.9,86.53
xception41,1107.6,462.251,512,299,9.28,39.86,26.97
tresnet_xl,1106.51,925.423,1024,224,15.17,15.34,78.44
beitv2_base_patch16_224,1106.05,925.798,1024,224,17.58,23.9,86.53
coat_tiny,1099.16,931.604,1024,224,4.35,27.2,5.5
vit_base_patch16_gap_224,1085.51,943.323,1024,224,17.49,25.59,86.57
maxvit_tiny_tf_224,1081.57,710.062,768,224,5.6,35.78,30.92
vit_relpos_base_patch16_224,1078.21,949.713,1024,224,17.51,24.97,86.43
nf_regnet_b4,1075.82,951.823,1024,384,4.7,28.61,30.21
coatnet_1_rw_224,1074.48,953.005,1024,224,8.04,34.6,41.72
dla102x2,1070.83,956.252,1024,224,9.34,29.91,41.28
pit_b_224,1066.8,479.928,512,224,12.42,32.94,73.76
pit_b_distilled_224,1063.31,481.504,512,224,12.5,33.07,74.79
tf_efficientnet_lite4,1058.68,362.703,384,380,4.04,45.66,13.01
efficientnetv2_s,1057.28,968.508,1024,384,8.44,35.77,21.46
vit_large_r50_s32_224,1034.79,989.556,1024,224,19.58,24.41,328.99
vit_small_patch16_36x1_224,1032.1,992.142,1024,224,13.71,35.69,64.67
efficientnet_b3_g8_gn,1031.26,496.465,512,320,3.2,28.83,14.25
tf_efficientnetv2_s,1029.13,995.002,1024,384,8.44,35.77,21.46
flexivit_base,1028.55,995.558,1024,240,20.29,28.36,86.59
vit_base_patch16_rpn_224,1016.66,1007.208,1024,224,17.49,23.75,86.54
vit_small_r26_s32_384,1011.11,1012.73,1024,384,10.43,29.85,36.47
vit_small_patch16_18x2_224,1005.34,1018.547,1024,224,13.71,35.69,64.67
swinv2_cr_small_224,1000.71,1023.259,1024,224,9.07,50.27,49.7
efficientnetv2_rw_s,995.91,1028.19,1024,384,8.72,38.03,23.94
wide_resnet101_2,995.32,1028.801,1024,224,22.8,21.23,126.89
swinv2_cr_small_ns_224,989.25,1035.114,1024,224,9.08,50.27,49.7
vit_relpos_base_patch16_rpn_224,986.84,1037.641,1024,224,17.51,24.97,86.41
coatnet_1_224,984.69,519.944,512,224,8.7,39.0,42.23
resnet200,983.36,1041.314,1024,224,15.07,32.19,64.67
dpn98,982.09,1042.657,1024,224,11.73,25.2,61.57
convnext_small,981.97,1042.782,1024,288,14.39,35.65,50.22
cs3se_edgenet_x,975.89,1049.279,1024,320,18.01,20.21,50.72
regnety_080,969.67,1056.01,1024,288,13.22,29.69,39.18
poolformer_m36,966.97,1058.965,1024,224,8.8,22.02,56.17
resnest101e,963.69,1062.57,1024,256,13.38,28.66,48.28
regnetz_b16_evos,955.65,803.632,768,288,2.36,16.43,9.74
twins_svt_large,954.95,1072.291,1024,224,15.15,35.1,99.27
pvt_v2_b4,952.02,1075.594,1024,224,10.14,53.74,62.56
gluon_resnext101_64x4d,944.48,1084.183,1024,224,15.52,31.21,83.46
regnetv_064,944.32,1084.367,1024,288,10.55,27.11,30.58
regnety_064,944.18,1084.526,1024,288,10.56,27.11,30.58
maxvit_rmlp_tiny_rw_256,941.64,815.588,768,256,6.77,46.92,29.15
regnetz_d8,936.16,1093.814,1024,320,6.19,37.08,23.37
resnetrs101,936.12,1093.858,1024,288,13.56,28.53,63.62
regnetz_d32,933.58,1096.833,1024,320,9.33,37.08,27.58
ig_resnext101_32x8d,930.9,1099.997,1024,224,16.48,31.21,88.79
swsl_resnext101_32x8d,930.28,1100.725,1024,224,16.48,31.21,88.79
resnext101_32x8d,929.98,1101.084,1024,224,16.48,31.21,88.79
ssl_resnext101_32x8d,929.0,1102.24,1024,224,16.48,31.21,88.79
convnextv2_tiny,925.13,553.423,512,288,7.39,22.21,28.64
convnextv2_small,924.53,1107.57,1024,224,8.71,21.56,50.32
maxvit_tiny_rw_256,921.72,833.209,768,256,6.74,44.35,29.07
inception_resnet_v2,917.69,1115.834,1024,299,13.18,25.06,55.84
ens_adv_inception_resnet_v2,917.66,1115.871,1024,299,13.18,25.06,55.84
maxxvit_rmlp_tiny_rw_256,914.74,1119.428,1024,256,6.66,39.76,29.64
xcit_tiny_24_p16_384_dist,912.61,1122.045,1024,384,6.87,34.29,12.12
cait_s24_224,908.65,1126.929,1024,224,9.35,40.58,46.92
pvt_v2_b5,904.89,1131.615,1024,224,11.76,50.92,81.96
nest_small,902.63,850.834,768,224,10.35,40.04,38.35
repvgg_b3,901.73,1135.583,1024,224,29.16,15.1,123.09
maxvit_tiny_pm_256,896.67,1141.994,1024,256,6.61,47.9,30.09
xception65p,896.53,571.079,512,299,13.91,52.48,39.82
swin_s3_small_224,896.35,856.792,768,224,9.43,37.84,49.74
jx_nest_small,892.32,860.663,768,224,10.35,40.04,38.35
efficientnet_b4,890.89,431.018,384,384,4.51,50.04,19.34
gmlp_b16_224,885.75,1156.072,1024,224,15.78,30.21,73.08
gluon_seresnext101_64x4d,885.23,1156.747,1024,224,15.53,31.25,88.23
hrnet_w40,881.9,1161.12,1024,224,12.75,25.29,57.56
efficientformer_l7,877.43,1167.027,1024,224,10.17,24.45,82.23
coat_mini,874.29,1171.227,1024,224,6.82,33.68,10.34
resnet101d,871.81,1174.559,1024,320,16.48,34.77,44.57
swin_base_patch4_window7_224,870.1,1176.867,1024,224,15.47,36.63,87.77
regnetz_040,868.17,884.605,768,320,6.35,37.78,27.12
regnetz_040h,862.76,890.151,768,320,6.43,37.94,28.94
mobilevitv2_150_384_in22ft1k,848.7,301.627,256,384,9.2,54.25,10.59
resnetv2_50d_evos,844.34,909.573,768,288,7.15,19.7,25.59
tf_efficientnet_b4,838.16,458.136,384,380,4.49,49.49,19.34
crossvit_base_240,835.31,919.411,768,240,21.22,36.33,105.03
vit_base_r50_s16_224,821.15,1247.01,1024,224,21.67,35.31,114.69
xcit_medium_24_p16_224_dist,819.59,1249.397,1024,224,16.13,31.71,84.4
xcit_medium_24_p16_224,818.73,1250.697,1024,224,16.13,31.71,84.4
gcvit_small,807.46,1268.151,1024,224,8.57,41.61,51.09
gluon_xception65,806.21,635.055,512,299,13.96,52.48,39.92
xception65,800.01,639.983,512,299,13.96,52.48,39.92
mvitv2_base,799.31,1281.092,1024,224,10.16,40.5,51.47
hrnet_w44,789.29,1297.348,1024,224,14.94,26.92,67.06
vit_base_patch16_plus_240,780.68,1311.665,1024,240,27.41,33.08,117.56
hrnet_w48,780.39,1312.147,1024,224,17.34,28.56,77.47
swinv2_tiny_window16_256,778.19,657.926,512,256,6.68,39.02,28.35
tresnet_m_448,775.99,1319.596,1024,448,22.94,29.21,31.39
xcit_small_12_p16_384_dist,760.88,1345.804,1024,384,14.14,36.51,26.25
vit_small_patch16_384,750.95,1022.685,768,384,15.52,50.78,22.2
maxvit_rmlp_small_rw_224,745.49,1373.585,1024,224,10.75,49.3,64.9
sequencer2d_l,742.48,1379.149,1024,224,9.74,22.12,54.3
swinv2_small_window8_256,738.39,1386.788,1024,256,11.58,40.14,49.73
swin_s3_base_224,730.45,1401.854,1024,224,13.69,48.26,71.13
poolformer_m48,729.44,1403.808,1024,224,11.59,29.17,73.47
densenet264d_iabn,727.43,1407.671,1024,224,13.47,14.0,72.74
vit_relpos_base_patch16_plus_240,723.43,1415.468,1024,240,27.3,34.33,117.38
dpn131,722.72,1416.854,1024,224,16.09,32.97,79.25
tnt_b_patch16_224,722.12,1418.026,1024,224,14.09,39.01,65.41
deit3_small_patch16_384,717.36,1070.572,768,384,15.52,50.78,22.21
deit3_small_patch16_384_in21ft1k,716.76,1071.477,768,384,15.52,50.78,22.21
swinv2_cr_base_224,715.64,1430.874,1024,224,15.86,59.66,87.88
eca_nfnet_l1,713.15,1435.867,1024,320,14.92,34.42,41.41
coatnet_2_rw_224,709.88,721.237,512,224,15.09,49.22,73.87
swinv2_cr_base_ns_224,709.69,1442.871,1024,224,15.86,59.66,87.88
coatnet_rmlp_2_rw_224,708.85,722.285,512,224,15.18,54.78,73.88
convit_base,706.65,1449.076,1024,224,17.52,31.77,86.54
mobilevitv2_175_384_in22ft1k,703.41,363.928,256,384,12.47,63.29,14.25
maxvit_small_tf_224,701.58,729.767,512,224,11.66,53.17,68.93
densenet264,701.03,1460.686,1024,224,12.95,12.8,72.69
ecaresnet200d,694.19,1475.094,1024,256,20.0,43.15,64.69
resnetv2_50x1_bitm,691.29,740.624,512,448,16.62,44.46,25.55
seresnet200d,691.25,1481.355,1024,256,20.01,43.15,71.86
xcit_tiny_24_p8_224,684.73,1495.467,1024,224,9.21,45.39,12.11
xcit_tiny_24_p8_224_dist,684.22,1496.573,1024,224,9.21,45.39,12.11
convnext_base,682.42,1500.518,1024,288,25.43,47.53,88.59
volo_d2_224,663.51,1543.3,1024,224,14.34,41.34,58.68
coatnet_2_224,660.84,581.062,384,224,16.5,52.67,74.68
legacy_senet154,654.15,1565.387,1024,224,20.77,38.69,115.09
gluon_senet154,654.04,1565.641,1024,224,20.77,38.69,115.09
senet154,653.94,1565.866,1024,224,20.77,38.69,115.09
xcit_nano_12_p8_384_dist,646.53,1583.823,1024,384,6.34,46.08,3.05
dpn107,646.38,1584.202,1024,224,18.38,33.46,86.92
nest_base,640.55,799.298,512,224,17.96,53.39,67.72
jx_nest_base,633.53,808.151,512,224,17.96,53.39,67.72
mobilevitv2_200_384_in22ft1k,626.31,408.731,256,384,16.24,72.34,18.45
xception71,619.72,826.163,512,299,18.09,69.92,42.34
hrnet_w64,618.15,1656.539,1024,224,28.97,35.09,128.06
resnet152d,618.09,1656.699,1024,320,24.08,47.67,60.21
regnetz_c16_evos,604.19,847.399,512,320,3.86,25.88,13.49
gcvit_base,594.61,1722.135,1024,224,14.87,55.48,90.32
regnety_160,594.3,1292.258,768,288,26.37,38.07,83.59
maxxvit_rmlp_small_rw_256,588.15,1741.023,1024,256,14.67,58.38,66.01
xcit_small_12_p8_224,582.04,1759.324,1024,224,18.69,47.21,26.21
xcit_small_12_p8_224_dist,581.74,1760.224,1024,224,18.69,47.21,26.21
maxvit_rmlp_small_rw_256,575.72,1333.976,768,256,14.15,66.09,64.9
regnetx_320,551.07,1393.631,768,224,31.81,36.3,107.81
seresnet152d,547.51,1870.27,1024,320,24.09,47.72,66.84
resnetrs152,544.33,1881.196,1024,320,24.34,48.14,86.62
vit_large_patch32_384,543.23,1884.997,1024,384,45.31,43.86,306.63
halonet_h1,540.47,473.65,256,256,3.0,51.17,8.1
seresnet269d,540.42,1894.818,1024,256,26.59,53.6,113.67
swinv2_base_window8_256,529.22,1451.182,768,256,20.37,52.59,87.92
maxxvitv2_rmlp_base_rw_224,523.43,1956.308,1024,224,24.2,62.77,116.09
resnext101_64x4d,521.77,1962.525,1024,288,25.66,51.59,83.46
regnetz_e8,521.5,1472.647,768,320,15.46,63.94,57.7
mixer_l16_224,518.26,1975.807,1024,224,44.6,41.69,208.2
vit_medium_patch16_gap_384,508.63,1006.611,512,384,26.08,67.54,39.03
swin_large_patch4_window7_224,501.11,1532.586,768,224,34.53,54.94,196.53
regnety_320,490.98,2085.591,1024,224,32.34,30.26,145.05
swinv2_small_window16_256,487.64,1049.932,512,256,12.82,66.29,49.73
seresnext101_32x8d,483.23,2119.074,1024,288,27.24,51.63,93.57
vit_small_patch8_224,478.05,1071.009,512,224,22.44,80.84,21.67
ig_resnext101_32x16d,477.64,2143.862,1024,224,36.27,51.18,194.03
swsl_resnext101_32x16d,476.69,2148.145,1024,224,36.27,51.18,194.03
ssl_resnext101_32x16d,476.06,2150.954,1024,224,36.27,51.18,194.03
seresnext101d_32x8d,475.05,2155.547,1024,288,27.64,52.95,93.59
nf_regnet_b5,470.14,1089.029,512,456,11.7,61.95,49.74
xcit_large_24_p16_224_dist,468.86,2184.017,1024,224,35.86,47.27,189.1
xcit_large_24_p16_224,468.75,2184.529,1024,224,35.86,47.27,189.1
volo_d3_224,463.72,2208.199,1024,224,20.78,60.09,86.33
nfnet_f1,463.52,2209.163,1024,320,35.97,46.77,132.63
efficientnet_b5,460.91,555.412,256,448,9.59,93.56,30.39
resnet200d,453.15,2259.739,1024,320,31.25,67.33,64.69
efficientnetv2_m,451.89,2266.018,1024,416,18.6,67.5,54.14
seresnextaa101d_32x8d,447.26,2289.498,1024,288,28.51,56.44,93.59
efficientnetv2_rw_m,437.1,1757.005,768,416,21.49,79.62,53.24
swinv2_cr_large_224,422.08,1819.551,768,224,35.1,78.42,196.68
coatnet_rmlp_3_rw_224,421.87,910.226,384,224,33.56,79.47,165.15
xcit_tiny_12_p8_384_dist,421.04,2432.044,1024,384,14.13,69.14,6.71
swinv2_cr_tiny_384,419.77,609.847,256,384,15.34,161.01,28.33
maxvit_rmlp_base_rw_224,419.03,1832.808,768,224,23.15,92.64,116.14
resnetv2_152x2_bit_teacher,418.89,2444.553,1024,224,46.95,45.11,236.34
resnetv2_101x1_bitm,418.36,1223.813,512,448,31.65,64.93,44.54
dm_nfnet_f1,409.02,1877.643,768,320,35.97,46.77,132.63
xcit_small_24_p16_384_dist,407.47,2513.062,1024,384,26.72,68.58,47.67
coatnet_3_rw_224,404.39,633.033,256,224,33.44,73.83,181.81
tf_efficientnet_b5,403.59,634.298,256,456,10.46,98.86,30.39
convnextv2_base,402.92,1270.715,512,288,25.43,47.53,88.72
resnetrs200,396.11,2585.123,1024,320,31.51,67.81,93.21
tresnet_l_448,395.6,2588.481,1024,448,43.5,47.56,55.99
eva_large_patch14_196,391.22,2617.408,1024,196,61.57,63.52,304.14
vit_large_patch16_224,389.92,2626.132,1024,224,61.6,63.52,304.33
regnetz_d8_evos,389.86,1969.937,768,320,7.03,38.92,23.46
maxvit_base_tf_224,387.71,1320.545,512,224,24.04,95.01,119.47
coatnet_3_224,387.35,660.882,256,224,36.56,79.01,166.97
crossvit_15_dagger_408,386.57,662.227,256,408,21.45,95.05,28.5
vit_base_patch16_18x2_224,384.3,2664.545,1024,224,52.51,71.38,256.73
deit3_large_patch16_224,376.93,2716.643,1024,224,61.6,63.52,304.37
deit3_large_patch16_224_in21ft1k,376.54,2719.504,1024,224,61.6,63.52,304.37
tf_efficientnetv2_m,374.38,2051.373,768,480,24.76,89.84,54.14
convnext_large,371.39,1378.579,512,288,56.87,71.29,197.77
beitv2_large_patch16_224,360.12,2843.465,1024,224,61.6,63.52,304.43
beit_large_patch16_224,359.86,2845.558,1024,224,61.6,63.52,304.43
swinv2_base_window12to16_192to256_22kft1k,359.31,1068.705,384,256,22.02,84.71,87.92
swinv2_base_window16_256,359.09,1069.342,384,256,22.02,84.71,87.92
eca_nfnet_l2,347.1,2212.621,768,384,30.05,68.28,56.72
flexivit_large,333.31,3072.173,1024,240,70.99,75.39,304.36
vit_large_r50_s32_384,332.86,3076.333,1024,384,57.43,76.52,329.09
maxxvitv2_rmlp_large_rw_224,330.79,3095.576,1024,224,44.14,87.15,215.42
resnest200e,317.25,3227.754,1024,320,35.69,82.78,70.2
maxvit_tiny_tf_384,317.22,807.002,256,384,17.53,123.42,30.98
convmixer_768_32,309.28,3310.892,1024,224,19.55,25.95,21.11
deit_base_patch16_384,306.13,1254.335,384,384,55.54,101.56,86.86
vit_base_patch16_384,306.13,1254.349,384,384,55.54,101.56,86.86
vit_base_patch16_clip_384,305.56,1256.673,384,384,55.54,101.56,86.86
xcit_small_24_p8_224_dist,305.18,3355.41,1024,224,35.81,90.78,47.63
deit_base_distilled_patch16_384,304.96,1259.16,384,384,55.65,101.82,87.63
xcit_small_24_p8_224,304.86,3358.887,1024,224,35.81,90.78,47.63
nasnetalarge,300.31,1278.679,384,331,23.89,90.56,88.75
volo_d1_384,299.05,1712.072,512,384,22.75,108.55,26.78
volo_d4_224,295.86,3461.069,1024,224,44.34,80.22,192.96
deit3_base_patch16_384,294.03,1305.985,384,384,55.54,101.56,86.88
deit3_base_patch16_384_in21ft1k,293.78,1307.085,384,384,55.54,101.56,86.88
tresnet_xl_448,292.43,2626.294,768,448,60.65,61.31,78.44
pnasnet5large,285.95,1342.894,384,331,25.04,92.89,86.06
vit_large_patch14_224,285.66,3584.705,1024,224,81.08,88.79,304.2
vit_large_patch14_clip_224,285.43,3587.599,1024,224,81.08,88.79,304.2
crossvit_18_dagger_408,283.82,901.967,256,408,32.47,124.87,44.61
xcit_medium_24_p16_384_dist,282.22,3628.317,1024,384,47.39,91.64,84.4
cait_xxs24_384,275.38,3718.492,1024,384,9.63,122.66,12.03
regnety_640,271.79,2825.663,768,224,64.16,42.5,281.38
maxvit_large_tf_224,268.97,1427.67,384,224,43.68,127.35,211.79
nfnet_f2,263.0,3893.59,1024,352,63.22,79.06,193.78
beit_base_patch16_384,260.66,1473.146,384,384,55.54,101.56,86.74
swinv2_cr_small_384,258.79,989.214,256,384,29.7,298.03,49.7
ecaresnet269d,257.79,3972.16,1024,352,50.25,101.25,102.09
resnetrs270,249.11,4110.633,1024,352,51.13,105.48,129.86
mvitv2_large,248.64,2059.181,512,224,43.87,112.02,217.99
efficientnet_b6,246.42,519.432,128,528,19.4,167.39,43.04
convnext_xlarge,241.35,2121.412,512,288,100.8,95.05,350.2
convnextv2_large,238.64,1072.708,256,288,56.87,71.29,197.96
tf_efficientnet_b6,236.4,541.434,128,528,19.4,167.39,43.04
swin_base_patch4_window12_384,235.04,816.885,192,384,47.19,134.78,87.9
dm_nfnet_f2,234.34,3277.279,768,352,63.22,79.06,193.78
coatnet_4_224,228.52,1120.23,256,224,62.48,129.26,275.43
vit_base_r50_s16_384,227.31,1689.303,384,384,67.43,135.03,98.95
efficientnetv2_l,221.97,2306.653,512,480,56.4,157.99,118.52
xcit_tiny_24_p8_384_dist,221.23,4628.611,1024,384,27.05,132.95,12.11
ig_resnext101_32x32d,220.61,2320.857,512,224,87.29,91.12,468.53
swinv2_large_window12to16_192to256_22kft1k,219.46,1166.485,256,256,47.81,121.53,196.74
tf_efficientnetv2_l,219.35,2334.183,512,480,56.4,157.99,118.52
resmlp_big_24_224,214.31,4778.166,1024,224,100.23,87.31,129.14
resmlp_big_24_224_in22ft1k,214.13,4782.043,1024,224,100.23,87.31,129.14
resmlp_big_24_distilled_224,214.04,4784.169,1024,224,100.23,87.31,129.14
xcit_medium_24_p8_224_dist,210.1,4873.763,1024,224,63.53,121.23,84.32
xcit_medium_24_p8_224,210.01,4875.864,1024,224,63.53,121.23,84.32
maxvit_small_tf_384,208.79,919.556,192,384,35.87,183.65,69.02
vit_base_patch8_224,199.59,1282.637,256,224,78.22,161.69,86.58
eca_nfnet_l3,199.58,2565.434,512,448,52.55,118.4,72.04
volo_d5_224,196.25,5217.924,1024,224,72.4,118.11,295.46
xcit_small_12_p8_384_dist,194.27,2635.521,512,384,54.92,138.29,26.21
cait_xs24_384,192.73,3984.863,768,384,19.28,183.98,26.67
swinv2_cr_base_384,184.92,1384.392,256,384,50.57,333.68,87.88
cait_xxs36_384,184.35,5554.56,1024,384,14.35,183.7,17.37
swinv2_cr_huge_224,183.61,2091.395,384,224,115.97,121.08,657.83
convnext_xxlarge,183.01,2098.268,384,224,151.66,95.29,846.47
coatnet_rmlp_2_rw_384,178.88,715.532,128,384,47.69,209.43,73.88
convmixer_1536_20,173.51,5901.752,1024,224,48.68,33.03,51.63
volo_d2_384,168.46,1519.603,256,384,46.17,184.51,58.87
resnetrs350,168.28,6085.136,1024,384,77.59,154.74,163.96
xcit_large_24_p16_384_dist,160.71,4778.847,768,384,105.35,137.17,189.1
resnetv2_152x2_bit_teacher_384,159.55,1604.488,256,384,136.16,132.56,236.34
maxvit_xlarge_tf_224,155.79,1643.178,256,224,97.49,191.02,474.95
maxvit_tiny_tf_512,155.64,822.373,128,512,33.49,257.59,31.05
regnety_1280,155.18,2474.502,384,224,127.66,71.58,644.81
vit_huge_patch14_224,154.03,6647.897,1024,224,167.43,139.43,658.75
vit_huge_patch14_clip_224,153.92,6652.944,1024,224,167.4,139.41,632.05
maxxvitv2_rmlp_base_rw_384,153.34,1669.502,256,384,72.98,213.74,116.09
efficientnetv2_xl,152.49,3357.61,512,512,93.85,247.32,208.12
tf_efficientnetv2_xl,151.4,2536.254,384,512,93.85,247.32,208.12
deit3_huge_patch14_224_in21ft1k,149.08,6868.834,1024,224,167.4,139.41,632.13
deit3_huge_patch14_224,149.01,6871.974,1024,224,167.4,139.41,632.13
cait_s24_384,148.46,3448.684,512,384,32.17,245.31,47.06
resnest269e,147.61,3468.584,512,416,77.69,171.98,110.93
nfnet_f3,147.43,3472.717,512,416,115.58,141.78,254.92
efficientnet_b7,142.41,674.084,96,600,38.33,289.94,66.35
resnetv2_50x3_bitm,138.27,1388.564,192,448,145.7,133.37,217.32
tf_efficientnet_b7,137.89,696.181,96,600,38.33,289.94,66.35
swin_large_patch4_window12_384,137.6,930.229,128,384,104.08,202.16,196.74
ig_resnext101_32x48d,132.29,2902.628,384,224,153.57,131.06,828.41
dm_nfnet_f3,127.59,4012.898,512,416,115.58,141.78,254.92
coatnet_5_224,125.18,1022.512,128,224,145.49,194.24,687.47
maxvit_rmlp_base_rw_384,121.26,2111.079,256,384,70.97,318.95,116.14
xcit_large_24_p8_224,119.97,6401.598,768,224,141.23,181.56,188.93
xcit_large_24_p8_224_dist,119.94,6403.17,768,224,141.23,181.56,188.93
resnetrs420,119.93,6403.598,768,416,108.45,213.79,191.89
resnetv2_152x2_bitm,117.33,2181.801,256,448,184.99,180.43,236.34
maxvit_base_tf_384,113.69,1688.826,192,384,73.8,332.9,119.65
swinv2_cr_large_384,113.07,1132.03,128,384,108.95,404.96,196.68
eva_large_patch14_336,102.65,2493.904,256,336,191.1,270.24,304.53
vit_large_patch14_clip_336,102.47,2498.286,256,336,191.11,270.24,304.53
vit_large_patch16_384,102.37,2500.639,256,384,191.21,270.24,304.72
xcit_small_24_p8_384_dist,102.36,5001.728,512,384,105.24,265.91,47.63
eva_giant_patch14_224,101.75,10063.521,1024,224,267.18,192.64,1012.56
vit_giant_patch14_224,100.42,7648.057,768,224,267.18,192.64,1012.61
vit_giant_patch14_clip_224,100.32,7655.265,768,224,267.18,192.64,1012.65
cait_s36_384,99.37,5152.338,512,384,47.99,367.4,68.37
deit3_large_patch16_384,99.34,2577.037,256,384,191.21,270.24,304.76
deit3_large_patch16_384_in21ft1k,99.27,2578.907,256,384,191.21,270.24,304.76
regnety_2560,97.99,2612.623,256,224,257.07,87.48,826.14
maxvit_small_tf_512,97.85,981.11,96,512,67.26,383.77,69.13
swinv2_base_window12to24_192to384_22kft1k,95.95,666.98,64,384,55.25,280.36,87.92
efficientnet_b8,95.3,1007.298,96,672,63.48,442.89,87.41
tf_efficientnet_b8,92.65,1036.1,96,672,63.48,442.89,87.41
beit_large_patch16_384,88.55,2890.891,256,384,191.21,270.24,305.0
resnetv2_101x3_bitm,83.1,2310.491,192,448,280.33,194.78,387.93
maxvit_large_tf_384,80.34,1593.284,128,384,132.55,445.84,212.03
nfnet_f4,79.54,4827.723,384,512,216.26,262.26,316.07
volo_d3_448,73.5,2612.274,192,448,96.33,446.83,86.63
dm_nfnet_f4,71.41,3584.699,256,512,216.26,262.26,316.07
xcit_medium_24_p8_384_dist,70.91,5415.294,384,384,186.67,354.73,84.32
swinv2_large_window12to24_192to384_22kft1k,60.84,788.97,48,384,116.15,407.83,196.74
vit_gigantic_patch14_clip_224,60.15,8511.823,512,224,483.96,275.37,1844.91
vit_gigantic_patch14_224,60.11,8517.291,512,224,483.95,275.37,1844.44
nfnet_f5,58.02,4412.387,256,544,290.97,349.71,377.21
vit_huge_patch14_clip_336,57.29,4468.831,256,336,390.97,407.54,632.46
convnextv2_huge,56.06,1712.576,96,384,337.96,232.35,660.29
volo_d4_448,54.47,2349.801,128,448,197.13,527.35,193.41
tf_efficientnet_l2,54.12,1182.593,64,475,172.11,609.89,480.31
maxvit_base_tf_512,52.65,1823.292,96,512,138.02,703.99,119.88
swinv2_cr_giant_224,52.12,2455.882,128,224,483.85,309.15,2598.76
dm_nfnet_f5,50.7,5049.339,256,544,290.97,349.71,377.21
swinv2_cr_huge_384,48.86,1309.971,64,384,352.04,583.18,657.94
maxvit_xlarge_tf_384,46.24,2076.289,96,384,292.78,668.76,475.32
nfnet_f6,44.3,5778.548,256,576,378.69,452.2,438.36
xcit_large_24_p8_384_dist,40.2,6368.127,256,384,415.0,531.82,188.93
eva_giant_patch14_336,39.77,6436.237,256,336,620.64,550.67,1013.01
dm_nfnet_f6,39.62,6461.626,256,576,378.69,452.2,438.36
maxvit_large_tf_512,38.67,1654.908,64,512,244.75,942.15,212.33
volo_d5_448,37.56,3408.043,128,448,315.06,737.92,295.91
beit_large_patch16_512,35.36,2715.28,96,512,362.24,656.39,305.67
nfnet_f7,34.74,7370.0,256,608,480.39,570.85,499.5
cait_m36_384,32.36,7912.123,256,384,173.11,734.81,271.22
resnetv2_152x4_bitm,30.0,4266.89,128,480,844.84,414.26,936.53
volo_d5_512,26.35,4857.602,128,512,425.09,1105.37,296.09
maxvit_xlarge_tf_512,23.12,2076.455,48,512,534.14,1413.22,475.77
efficientnet_l2,21.26,1505.032,32,800,479.12,1707.39,480.31
swinv2_cr_giant_384,15.03,2129.6,32,384,1450.71,1394.86,2598.76
cait_m48_448,13.69,9353.048,128,448,329.41,1708.23,356.46
eva_giant_patch14_560,10.36,4631.037,48,560,1906.76,2577.17,1014.45
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt111-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,param_count
tinynet_e,68298.73,14.982,1024,106,2.04
mobilenetv3_small_050,48773.32,20.985,1024,224,1.59
lcnet_035,47045.94,21.755,1024,224,1.64
lcnet_050,41541.83,24.639,1024,224,1.88
mobilenetv3_small_075,37803.23,27.076,1024,224,2.04
mobilenetv3_small_100,34839.31,29.381,1024,224,2.54
tinynet_d,34615.54,29.571,1024,152,2.34
tf_mobilenetv3_small_minimal_100,31097.5,32.918,1024,224,2.04
tf_mobilenetv3_small_075,30498.6,33.564,1024,224,2.04
tf_mobilenetv3_small_100,28466.28,35.962,1024,224,2.54
lcnet_075,26999.4,37.915,1024,224,2.36
mnasnet_small,23228.74,44.072,1024,224,2.03
lcnet_100,22774.77,44.951,1024,224,2.95
levit_128s,21485.8,47.648,1024,224,7.78
mobilenetv2_035,20032.08,51.106,1024,224,1.68
ghostnet_050,18639.82,54.925,1024,224,2.59
mnasnet_050,18244.9,56.115,1024,224,2.22
regnetx_002,17821.98,57.446,1024,224,2.68
tinynet_c,17586.87,58.214,1024,184,2.46
regnety_002,16673.08,61.405,1024,224,3.16
mobilenetv2_050,16415.14,62.371,1024,224,1.97
semnasnet_050,16295.23,62.83,1024,224,2.08
lcnet_150,15040.68,68.071,1024,224,4.5
levit_128,14657.83,69.849,1024,224,9.21
regnetx_004,14440.03,70.903,1024,224,5.16
gernet_s,14051.59,72.863,1024,224,8.17
mobilenetv3_large_075,13658.47,74.961,1024,224,3.99
levit_192,12892.86,79.412,1024,224,10.95
mnasnet_075,12457.54,82.188,1024,224,3.17
mobilenetv3_rw,12442.0,82.291,1024,224,5.48
hardcorenas_a,12441.72,82.293,1024,224,5.26
mixer_s32_224,12325.03,83.072,1024,224,19.1
mobilenetv3_large_100,12253.83,83.554,1024,224,5.48
mobilenetv3_large_100_miil,12253.15,83.559,1024,224,5.48
vit_small_patch32_224,12098.59,84.625,1024,224,22.88
tf_mobilenetv3_large_075,11757.16,87.085,1024,224,3.99
tinynet_b,11714.84,87.399,1024,188,3.73
hardcorenas_b,11307.89,90.545,1024,224,5.18
hardcorenas_c,11295.65,90.643,1024,224,5.52
ese_vovnet19b_slim_dw,11295.18,90.646,1024,224,1.9
tf_mobilenetv3_large_minimal_100,11279.9,90.77,1024,224,3.92
mnasnet_b1,10903.65,93.902,1024,224,4.38
mnasnet_100,10903.28,93.906,1024,224,4.38
swsl_resnet18,10835.46,94.49,1024,224,11.69
ssl_resnet18,10829.31,94.547,1024,224,11.69
resnet18,10826.05,94.576,1024,224,11.69
gluon_resnet18_v1b,10791.4,94.879,1024,224,11.69
tf_mobilenetv3_large_100,10638.58,96.242,1024,224,5.48
hardcorenas_d,10551.44,97.037,1024,224,7.5
semnasnet_075,10519.79,97.329,1024,224,2.91
ghostnet_100,10434.77,98.122,1024,224,5.18
mobilenetv2_075,10372.86,98.708,1024,224,2.64
seresnet18,10183.7,100.541,1024,224,11.78
regnety_006,9982.58,102.567,1024,224,6.06
vit_tiny_r_s16_p8_224,9895.77,103.465,1024,224,6.34
spnasnet_100,9875.93,103.675,1024,224,4.42
legacy_seresnet18,9845.25,103.999,1024,224,11.78
regnety_004,9552.58,107.183,1024,224,4.34
levit_256,9434.24,108.53,1024,224,18.89
tinynet_a,9412.38,108.782,1024,192,6.19
hardcorenas_f,9390.96,109.029,1024,224,8.2
semnasnet_100,9334.36,109.69,1024,224,3.89
mnasnet_a1,9318.68,109.875,1024,224,3.89
mobilenetv2_100,9260.72,110.564,1024,224,3.5
hardcorenas_e,9255.53,110.624,1024,224,8.07
tf_efficientnetv2_b0,9250.71,110.683,1024,224,7.14
fbnetc_100,9032.97,113.35,1024,224,5.57
efficientnet_lite0,8999.14,113.778,1024,224,4.65
resnet18d,8913.81,114.867,1024,224,11.71
ese_vovnet19b_slim,8715.26,117.484,1024,224,3.17
regnetx_008,8458.52,121.05,1024,224,7.26
levit_256d,8024.27,127.602,1024,224,26.21
regnetx_006,7937.85,128.991,1024,224,6.2
regnety_008,7871.11,130.085,1024,224,6.26
tf_efficientnet_lite0,7813.92,131.036,1024,224,4.65
efficientnet_b0,7681.79,133.291,1024,224,5.29
ghostnet_130,7655.04,133.756,1024,224,7.36
mnasnet_140,7500.9,136.506,1024,224,7.12
xcit_nano_12_p16_224_dist,7406.15,138.252,1024,224,3.05
xcit_nano_12_p16_224,7390.69,138.541,1024,224,3.05
rexnetr_100,7266.82,140.903,1024,224,4.88
mobilenetv2_110d,7027.38,145.704,1024,224,4.52
tf_efficientnet_b0_ap,6815.39,150.235,1024,224,5.29
tf_efficientnet_b0,6815.06,150.243,1024,224,5.29
tf_efficientnet_b0_ns,6813.82,150.27,1024,224,5.29
regnetz_005,6657.81,153.793,1024,224,7.12
hrnet_w18_small,6626.19,154.526,1024,224,13.19
gernet_m,6452.0,158.699,1024,224,21.14
semnasnet_140,6407.4,159.804,1024,224,6.11
tv_resnet34,6289.18,162.807,1024,224,21.8
gluon_resnet34_v1b,6283.02,162.967,1024,224,21.8
resnet34,6262.0,163.515,1024,224,21.8
vit_tiny_patch16_224,6224.71,164.493,1024,224,5.72
mobilenetv2_140,6223.01,164.539,1024,224,6.11
deit_tiny_patch16_224,6218.5,164.657,1024,224,5.72
ese_vovnet19b_dw,6178.2,165.733,1024,224,6.54
deit_tiny_distilled_patch16_224,6117.9,167.365,1024,224,5.91
efficientnet_b1_pruned,6023.92,169.977,1024,240,6.33
efficientnet_lite1,5983.27,171.132,1024,240,5.42
tf_efficientnetv2_b1,5921.06,172.93,1024,240,8.14
selecsls42,5908.85,173.287,1024,224,30.35
fbnetv3_b,5903.05,173.458,1024,256,8.6
seresnet34,5898.43,173.594,1024,224,21.96
selecsls42b,5885.09,173.988,1024,224,32.46
rexnet_100,5871.63,174.387,1024,224,4.8
pit_ti_distilled_224,5837.73,175.397,1024,224,5.1
pit_ti_224,5807.14,176.321,1024,224,4.85
efficientnet_es,5711.75,179.268,1024,224,5.44
efficientnet_es_pruned,5710.76,179.298,1024,224,5.44
resnet26,5694.28,179.817,1024,224,16.0
legacy_seresnet34,5670.37,180.577,1024,224,21.96
levit_384,5643.3,181.443,1024,224,39.13
resnet34d,5571.88,183.768,1024,224,21.82
resnetblur18,5507.37,185.919,1024,224,11.69
rexnetr_130,5484.64,186.691,1024,224,7.61
nf_regnet_b0,5429.0,188.605,1024,256,8.76
tf_efficientnet_es,5424.57,188.76,1024,224,5.44
tf_efficientnet_lite1,5360.34,191.021,1024,240,5.42
skresnet18,5350.54,191.371,1024,224,11.96
selecsls60,5263.66,194.53,1024,224,30.67
selecsls60b,5251.8,194.969,1024,224,32.77
mobilenetv2_120d,5160.96,198.401,1024,224,5.83
mobilevit_xxs,5125.56,199.771,1024,256,1.27
repvgg_b0,5049.08,202.797,1024,224,15.82
resnet26d,4891.07,209.349,1024,224,16.01
fbnetv3_d,4812.34,212.774,1024,256,10.31
rexnetr_150,4791.0,213.722,1024,224,9.78
xcit_tiny_12_p16_224_dist,4778.77,214.269,1024,224,6.72
xcit_tiny_12_p16_224,4774.3,214.469,1024,224,6.72
visformer_tiny,4714.23,217.203,1024,224,10.32
nf_resnet26,4639.54,220.7,1024,224,16.0
efficientnet_lite2,4604.04,222.402,1024,260,6.09
pit_xs_224,4582.73,223.434,1024,224,10.62
pit_xs_distilled_224,4539.81,225.546,1024,224,11.0
resmlp_12_distilled_224,4519.92,226.541,1024,224,15.35
resmlp_12_224,4518.97,226.589,1024,224,15.35
tf_efficientnetv2_b2,4403.08,232.553,1024,260,10.1
vit_base_patch32_224_sam,4330.12,236.472,1024,224,88.22
vit_base_patch32_224,4316.0,237.246,1024,224,88.22
mixer_b32_224,4294.72,238.421,1024,224,60.29
tf_efficientnet_b1_ns,4267.56,239.936,1024,240,7.79
tf_efficientnet_b1_ap,4266.36,240.004,1024,240,7.79
tf_efficientnet_b1,4266.12,240.017,1024,240,7.79
efficientnet_b0_g16_evos,4260.91,240.312,1024,224,8.11
legacy_seresnext26_32x4d,4210.62,243.183,1024,224,16.79
mixer_s16_224,4210.38,243.197,1024,224,18.53
gernet_l,4176.68,245.159,1024,256,31.08
tf_efficientnet_lite2,4154.51,246.467,1024,260,6.09
gmixer_12_224,4127.0,248.109,1024,224,12.7
efficientnet_b1,4101.97,249.625,1024,256,7.79
resnext26ts,4091.38,250.27,1024,256,10.3
rexnet_130,4013.83,255.106,1024,224,7.56
seresnext26ts,3966.87,258.123,1024,256,10.39
nf_seresnet26,3954.94,258.905,1024,224,17.4
eca_resnext26ts,3953.54,258.996,1024,256,10.3
nf_ecaresnet26,3938.91,259.956,1024,224,16.0
repvgg_a2,3868.66,264.68,1024,224,28.21
gmlp_ti16_224,3865.39,264.903,1024,224,5.87
crossvit_tiny_240,3854.54,265.648,1024,240,7.01
efficientnet_b2_pruned,3821.39,267.953,1024,260,8.31
vit_small_patch32_384,3802.4,269.289,1024,384,22.92
resnet26t,3789.73,270.19,1024,256,16.01
regnetx_016,3776.49,271.139,1024,224,9.19
rexnet_150,3774.39,271.29,1024,224,9.73
seresnext26t_32x4d,3769.71,271.627,1024,224,16.81
seresnext26tn_32x4d,3769.68,271.629,1024,224,16.81
gcresnext26ts,3763.59,272.069,1024,256,10.48
seresnext26d_32x4d,3760.94,272.26,1024,224,16.81
ecaresnext50t_32x4d,3751.71,272.931,1024,224,15.41
ecaresnext26t_32x4d,3745.62,273.373,1024,224,15.41
ecaresnet50d_pruned,3714.28,275.68,1024,224,19.94
resnetv2_50,3703.17,276.505,1024,224,25.55
eca_botnext26ts_256,3685.99,277.797,1024,256,10.59
crossvit_9_240,3640.93,281.235,1024,240,8.55
ecaresnetlight,3588.5,285.344,1024,224,30.16
eca_halonext26ts,3573.18,286.568,1024,256,10.76
crossvit_9_dagger_240,3562.28,287.444,1024,240,8.78
poolformer_s12,3555.09,288.026,1024,224,11.92
swsl_resnet50,3536.6,289.53,1024,224,25.56
gluon_resnet50_v1b,3534.52,289.702,1024,224,25.56
tv_resnet50,3534.18,289.73,1024,224,25.56
resnet50,3528.73,290.177,1024,224,25.56
ssl_resnet50,3522.22,290.713,1024,224,25.56
rexnetr_200,3521.98,145.362,512,224,16.52
dla46_c,3494.33,293.033,1024,224,1.3
botnet26t_256,3447.34,297.026,1024,256,12.49
efficientnet_em,3434.61,298.129,1024,240,6.9
dpn68,3423.83,299.067,1024,224,12.61
resnet32ts,3414.56,299.879,1024,256,17.96
dpn68b,3412.63,300.049,1024,224,12.61
regnety_016,3408.85,300.382,1024,224,11.2
halonet26t,3379.19,303.017,1024,256,12.48
resnetv2_50t,3374.73,303.416,1024,224,25.57
resnetv2_50d,3370.34,303.812,1024,224,25.57
resnet33ts,3369.92,303.852,1024,256,19.68
nf_regnet_b1,3357.92,304.938,1024,288,10.22
gluon_resnet50_v1c,3339.61,306.611,1024,224,25.58
nf_regnet_b2,3329.81,307.512,1024,272,14.31
tf_efficientnet_b2_ap,3315.4,308.848,1024,260,9.11
tf_efficientnet_b2_ns,3314.91,308.894,1024,260,9.11
tf_efficientnet_em,3313.67,309.011,1024,240,6.9
tf_efficientnet_b2,3313.08,309.063,1024,260,9.11
seresnet33ts,3285.4,311.671,1024,256,19.78
eca_resnet33ts,3264.82,313.634,1024,256,19.68
resnet50t,3209.81,319.009,1024,224,25.57
bat_resnext26ts,3208.41,319.147,1024,256,10.73
legacy_seresnet50,3208.39,319.15,1024,224,28.09
gluon_resnet50_v1d,3207.43,319.246,1024,224,25.58
convnext_nano_hnf,3203.44,319.644,1024,224,15.59
resnet50d,3201.53,319.835,1024,224,25.58
convit_tiny,3182.1,321.788,1024,224,5.71
gcresnet33ts,3115.24,328.694,1024,256,19.88
vit_small_resnet26d_224,3114.54,328.764,1024,224,63.61
efficientnet_b2,3113.3,328.898,1024,288,9.11
efficientnet_b2a,3112.75,328.957,1024,288,9.11
efficientnet_b3_pruned,3098.33,330.489,1024,300,9.86
mobilevit_xs,3098.23,165.245,512,256,2.32
vovnet39a,3094.91,330.85,1024,224,22.6
seresnet50,3084.99,331.918,1024,224,28.09
haloregnetz_b,3050.82,335.635,1024,224,11.68
skresnet34,3016.36,339.47,1024,224,22.28
ese_vovnet39b,2991.04,342.343,1024,224,24.57
eca_vovnet39b,2984.95,343.042,1024,224,22.6
cspresnext50,2981.83,343.401,1024,224,20.57
selecsls84,2979.47,343.673,1024,224,50.95
res2net50_48w_2s,2961.27,345.783,1024,224,25.29
ssl_resnext50_32x4d,2883.39,355.125,1024,224,25.03
resnext50_32x4d,2879.89,355.557,1024,224,25.03
swsl_resnext50_32x4d,2879.83,355.563,1024,224,25.03
tv_resnext50_32x4d,2876.25,356.006,1024,224,25.03
gluon_resnext50_32x4d,2870.17,356.762,1024,224,25.03
resnetaa50d,2869.21,356.878,1024,224,25.58
tv_densenet121,2861.74,357.812,1024,224,7.98
densenet121,2859.4,358.103,1024,224,7.98
mixnet_s,2849.57,359.34,1024,224,4.13
seresnet50t,2844.85,359.936,1024,224,28.1
resnetrs50,2844.33,360.0,1024,224,35.69
deit_small_patch16_224,2839.86,360.568,1024,224,22.05
vit_small_patch16_224,2838.21,360.777,1024,224,22.05
ecaresnet101d_pruned,2833.47,361.381,1024,224,24.88
coat_lite_tiny,2832.3,361.531,1024,224,5.72
gluon_resnet50_v1s,2824.57,362.521,1024,224,25.68
pit_s_224,2821.07,362.969,1024,224,23.46
ecaresnet50d,2815.88,363.64,1024,224,25.58
rexnet_200,2812.74,182.018,512,224,16.37
pit_s_distilled_224,2802.25,365.406,1024,224,24.04
deit_small_distilled_patch16_224,2791.58,366.805,1024,224,22.44
dla34,2790.34,366.966,1024,224,15.74
dla46x_c,2773.06,369.253,1024,224,1.07
cspresnet50,2754.0,371.811,1024,256,21.62
hrnet_w18_small_v2,2736.24,374.217,1024,224,15.6
densenet121d,2731.76,374.837,1024,224,8.0
tf_mixnet_s,2728.68,375.26,1024,224,4.13
efficientnet_lite3,2718.45,188.332,512,300,8.2
regnetz_b16,2715.55,377.076,1024,288,9.72
dla60x_c,2712.17,377.543,1024,224,1.32
vit_tiny_r_s16_p8_384,2700.75,189.562,512,384,6.36
coat_lite_mini,2663.5,384.444,1024,224,11.01
resnext50d_32x4d,2662.22,384.629,1024,224,25.05
resnetblur50,2619.08,390.962,1024,224,25.56
cspresnet50d,2596.32,394.393,1024,256,21.64
vgg11_bn,2593.2,197.428,512,224,132.87
vit_base2_patch32_256,2576.99,397.35,1024,256,119.46
seresnetaa50d,2576.43,397.436,1024,224,28.11
cspresnet50w,2574.64,397.712,1024,256,28.12
seresnext50_32x4d,2574.54,397.729,1024,224,27.56
lambda_resnet26rpt_256,2573.59,397.876,1024,256,10.99
legacy_seresnext50_32x4d,2571.71,398.166,1024,224,27.56
xcit_nano_12_p16_384_dist,2569.25,398.547,1024,384,3.05
gluon_seresnext50_32x4d,2564.07,399.352,1024,224,27.56
xcit_tiny_24_p16_224_dist,2557.0,400.456,1024,224,12.12
xcit_tiny_24_p16_224,2556.28,400.571,1024,224,12.12
efficientnetv2_rw_t,2545.32,402.294,1024,288,13.65
vovnet57a,2538.22,403.418,1024,224,36.64
fbnetv3_g,2530.36,404.673,1024,288,16.62
gcresnet50t,2514.25,407.265,1024,256,25.9
res2net50_26w_4s,2505.47,408.693,1024,224,25.7
tf_efficientnetv2_b3,2496.85,410.104,1024,300,14.36
twins_svt_small,2488.33,411.508,1024,224,24.06
vit_base_resnet26d_224,2472.64,414.118,1024,224,101.4
ese_vovnet57b,2446.54,418.537,1024,224,38.61
densenetblur121d,2445.52,418.711,1024,224,8.0
tf_efficientnet_lite3,2442.63,209.598,512,300,8.2
resnetblur50d,2422.6,422.672,1024,224,25.58
mobilevit_s,2415.06,211.991,512,256,5.58
resnest14d,2409.35,424.998,1024,224,10.61
tf_inception_v3,2400.27,426.605,1024,299,23.83
gluon_inception_v3,2398.26,426.963,1024,299,23.83
nf_seresnet50,2397.62,427.078,1024,224,28.09
inception_v3,2397.12,427.166,1024,299,23.83
nf_ecaresnet50,2388.81,428.654,1024,224,25.56
adv_inception_v3,2388.59,428.689,1024,299,23.83
densenet169,2362.23,433.477,1024,224,14.15
sehalonet33ts,2337.63,219.014,512,256,13.69
resmlp_24_224,2312.09,442.876,1024,224,30.02
gc_efficientnetv2_rw_t,2311.11,443.065,1024,288,13.68
resmlp_24_distilled_224,2308.78,443.512,1024,224,30.02
convnext_tiny,2275.45,450.007,1024,224,28.59
gcresnext50ts,2263.71,452.341,1024,256,15.67
res2net50_14w_8s,2252.88,454.515,1024,224,25.06
semobilevit_s,2250.59,227.485,512,256,5.74
darknet53,2248.73,227.672,512,256,41.61
resnetv2_101,2235.55,458.038,1024,224,44.54
xcit_small_12_p16_224_dist,2216.94,461.885,1024,224,26.25
xcit_small_12_p16_224,2212.11,462.891,1024,224,26.25
skresnet50,2193.01,466.925,1024,224,25.8
resnet101,2170.93,471.673,1024,224,44.55
tv_resnet101,2164.75,473.02,1024,224,44.55
gluon_resnet101_v1b,2163.02,473.399,1024,224,44.55
res2next50,2156.68,474.791,1024,224,24.67
ecaresnet26t,2145.15,477.343,1024,320,16.01
nf_regnet_b3,2126.07,481.627,1024,320,18.59
gmixer_24_224,2110.93,485.081,1024,224,24.72
resnetv2_101d,2090.05,489.925,1024,224,44.56
gluon_resnet101_v1c,2088.96,490.184,1024,224,44.57
dla60,2087.19,490.597,1024,224,22.04
convnext_tiny_hnf,2073.34,493.877,1024,224,28.59
skresnet50d,2063.75,496.172,1024,224,25.82
twins_pcpvt_small,2048.61,499.837,1024,224,24.11
gluon_resnet101_v1d,2036.64,502.777,1024,224,44.57
vgg13,2019.99,506.92,1024,224,133.05
efficientnet_b0_gn,2017.66,253.748,512,224,5.29
wide_resnet50_2,2001.07,511.71,1024,224,68.88
sebotnet33ts_256,1999.77,192.009,384,256,13.7
xcit_nano_12_p8_224,1978.07,517.663,1024,224,3.05
xcit_nano_12_p8_224_dist,1977.13,517.91,1024,224,3.05
vit_base_resnet50d_224,1950.99,524.848,1024,224,110.97
repvgg_b1,1936.11,528.882,1024,224,57.42
legacy_seresnet101,1932.53,529.863,1024,224,49.33
dla60x,1896.77,539.852,1024,224,17.35
resnetaa101d,1893.61,540.751,1024,224,44.57
tf_efficientnet_b3_ns,1893.08,270.444,512,300,12.23
tf_efficientnet_b3_ap,1892.83,270.481,512,300,12.23
tf_efficientnet_b3,1892.52,270.524,512,300,12.23
seresnet101,1891.69,541.301,1024,224,49.33
gluon_resnet101_v1s,1874.89,546.152,1024,224,44.67
resnet51q,1869.52,547.722,1024,288,35.7
efficientnet_b3,1853.06,276.288,512,320,12.23
efficientnet_b3a,1852.72,276.339,512,320,12.23
crossvit_small_240,1844.12,555.265,1024,240,26.86
poolformer_s24,1843.54,555.44,1024,224,21.39
swin_tiny_patch4_window7_224,1831.44,559.111,1024,224,28.29
halonet50ts,1826.61,560.589,1024,256,22.73
densenet201,1818.74,563.014,1024,224,20.01
gmlp_s16_224,1810.78,565.489,1024,224,19.42
ssl_resnext101_32x4d,1796.49,569.986,1024,224,44.18
resnext101_32x4d,1794.8,570.526,1024,224,44.18
swsl_resnext101_32x4d,1794.2,570.714,1024,224,44.18
convnext_tiny_hnfd,1793.87,570.819,1024,224,28.63
gluon_resnext101_32x4d,1791.38,571.613,1024,224,44.18
cspdarknet53,1782.45,287.233,512,256,27.64
nf_resnet101,1778.39,575.79,1024,224,44.55
vit_small_r26_s32_224,1778.32,575.808,1024,224,36.43
ecaresnet101d,1778.2,575.85,1024,224,44.57
regnetz_c16,1772.58,288.833,512,320,13.46
res2net50_26w_6s,1761.45,581.325,1024,224,37.05
resnest26d,1761.21,581.405,1024,224,17.07
dla60_res2net,1720.09,595.302,1024,224,20.85
nf_resnet50,1719.27,595.589,1024,288,25.56
crossvit_15_240,1700.36,602.213,1024,240,27.53
resnetblur101d,1688.41,606.472,1024,224,44.57
resnet61q,1677.9,610.274,1024,288,36.85
swin_s3_tiny_224,1668.56,613.692,1024,224,28.33
xcit_tiny_12_p16_384_dist,1651.99,619.843,1024,384,6.72
crossvit_15_dagger_240,1651.71,619.951,1024,240,28.21
resnetv2_50d_frn,1650.73,620.316,1024,224,25.59
vgg13_bn,1642.03,311.797,512,224,133.05
efficientnet_b0_g8_gn,1637.47,312.665,512,224,6.56
vgg16,1632.03,627.428,1024,224,138.36
cait_xxs24_224,1630.9,627.86,1024,224,11.96
repvgg_b1g4,1614.79,634.126,1024,224,39.97
regnetx_032,1605.58,637.76,1024,224,15.3
seresnext101_32x4d,1600.47,639.797,1024,224,48.96
gluon_seresnext101_32x4d,1597.12,641.141,1024,224,48.96
legacy_seresnext101_32x4d,1596.46,641.406,1024,224,48.96
botnet50ts_256,1594.64,321.062,512,256,22.74
res2net101_26w_4s,1591.38,643.454,1024,224,45.21
regnetx_040,1586.28,645.521,1024,224,22.12
ese_vovnet39b_evos,1584.69,646.169,1024,224,24.58
resnetv2_50d_evob,1578.15,648.842,1024,224,25.59
resnetv2_50x1_bit_distilled,1562.05,655.535,1024,224,25.55
visformer_small,1557.14,657.604,1024,224,40.22
xception41p,1555.03,329.24,512,299,26.91
resnetv2_152,1552.32,659.642,1024,224,60.19
dla102,1552.08,659.747,1024,224,33.27
resmlp_36_224,1551.92,659.816,1024,224,44.69
dla60_res2next,1551.9,659.821,1024,224,17.03
resmlp_36_distilled_224,1551.61,659.949,1024,224,44.69
resnest50d_1s4x24d,1548.62,661.22,1024,224,25.68
xception,1546.37,331.084,512,299,22.86
hrnet_w32,1543.69,663.332,1024,224,41.23
halo2botnet50ts_256,1528.74,669.82,1024,256,22.64
tv_resnet152,1516.25,675.334,1024,224,60.19
coat_lite_small,1515.55,675.647,1024,224,19.84
mixer_b16_224,1515.3,675.76,1024,224,59.88
mixer_b16_224_miil,1514.28,676.214,1024,224,59.88
gluon_resnet152_v1b,1512.22,677.139,1024,224,60.19
resnet152,1507.31,679.341,1024,224,60.19
efficientnet_el,1505.49,340.076,512,300,10.59
efficientnet_el_pruned,1505.01,340.185,512,300,10.59
vit_large_patch32_224,1500.0,682.653,1024,224,306.54
swin_v2_cr_tiny_224,1497.15,683.946,1024,224,28.33
res2net50_26w_8s,1487.1,688.575,1024,224,48.4
nf_seresnet101,1486.78,688.723,1024,224,49.33
resnetv2_152d,1485.01,689.541,1024,224,60.2
nf_ecaresnet101,1477.89,692.868,1024,224,44.55
gluon_resnet152_v1c,1474.38,694.516,1024,224,60.21
swin_v2_cr_tiny_ns_224,1472.66,695.323,1024,224,28.33
tf_efficientnet_el,1464.69,349.551,512,300,10.59
vit_tiny_patch16_384,1460.68,701.028,1024,384,5.79
vit_base_r26_s32_224,1452.81,704.825,1024,224,101.38
gluon_resnet152_v1d,1447.4,707.462,1024,224,60.21
hrnet_w18,1432.55,714.792,1024,224,21.3
mixnet_m,1431.87,715.134,1024,224,5.01
mixer_l32_224,1426.61,717.775,1024,224,206.94
dla102x,1416.93,722.674,1024,224,26.31
tf_mixnet_m,1413.24,724.561,1024,224,5.01
convnext_small,1410.61,725.911,1024,224,50.22
twins_pcpvt_base,1405.32,728.645,1024,224,43.83
ecaresnet50t,1382.49,740.677,1024,320,25.57
nest_tiny,1378.24,371.475,512,224,17.06
vit_base_patch32_384,1377.87,743.165,1024,384,88.3
convit_small,1368.52,748.238,1024,224,27.78
regnety_032,1367.75,748.662,1024,288,19.44
vgg19,1367.17,748.977,1024,224,143.67
gluon_resnet152_v1s,1363.8,750.829,1024,224,60.32
vgg16_bn,1363.24,375.563,512,224,138.37
jx_nest_tiny,1354.35,378.027,512,224,17.06
ese_vovnet99b,1351.66,757.57,1024,224,63.2
xception41,1337.66,382.745,512,299,26.97
legacy_seresnet152,1335.99,766.462,1024,224,66.82
seresnet152,1317.5,777.213,1024,224,66.82
dpn92,1303.11,785.8,1024,224,37.67
efficientnet_lite4,1287.19,298.314,384,380,13.01
tresnet_m,1283.27,797.947,1024,224,31.39
inception_v4,1282.29,798.555,1024,299,42.68
densenet161,1276.9,801.927,1024,224,28.68
xcit_tiny_12_p8_224_dist,1263.32,810.55,1024,224,6.71
xcit_tiny_12_p8_224,1260.86,812.129,1024,224,6.71
regnetx_080,1259.62,812.931,1024,224,39.57
skresnext50_32x4d,1255.24,815.767,1024,224,27.48
vit_small_resnet50d_s16_224,1252.87,817.305,1024,224,57.53
twins_svt_base,1249.6,819.445,1024,224,56.07
poolformer_s36,1245.02,822.461,1024,224,30.86
repvgg_b2,1243.81,823.263,1024,224,89.02
volo_d1_224,1232.57,830.769,1024,224,26.63
hrnet_w30,1221.32,838.421,1024,224,37.71
crossvit_18_240,1205.55,849.387,1024,240,43.27
resnest50d,1199.06,853.99,1024,224,27.48
tf_efficientnet_lite4,1182.46,324.733,384,380,13.01
xcit_small_24_p16_224_dist,1182.35,866.056,1024,224,47.67
xcit_small_24_p16_224,1181.92,866.371,1024,224,47.67
crossvit_18_dagger_240,1172.17,873.578,1024,240,44.27
regnetx_064,1166.56,438.885,512,224,26.21
vgg19_bn,1163.98,439.857,512,224,143.68
nf_regnet_b4,1158.13,884.17,1024,384,30.21
efficientnetv2_s,1149.66,890.683,1024,384,21.46
regnetz_d8,1143.01,895.867,1024,320,23.37
resnet50_gn,1132.8,903.934,1024,224,25.56
dla169,1130.6,905.699,1024,224,53.39
wide_resnet101_2,1125.72,909.622,1024,224,126.89
swin_small_patch4_window7_224,1123.67,911.288,1024,224,49.61
tf_efficientnetv2_s,1123.66,911.292,1024,384,21.46
tf_efficientnetv2_s_in21ft1k,1121.7,912.889,1024,384,21.46
gluon_resnext101_64x4d,1118.73,915.313,1024,224,83.46
mixnet_l,1117.6,458.111,512,224,7.33
xception65p,1115.45,458.995,512,299,39.82
vit_base_patch16_224_miil,1108.89,923.433,1024,224,86.54
nfnet_l0,1101.58,929.561,1024,288,35.07
eca_nfnet_l0,1100.75,930.261,1024,288,24.14
tf_mixnet_l,1100.74,465.128,512,224,7.33
dpn98,1097.38,933.115,1024,224,61.57
resnet200,1096.34,934.004,1024,224,64.67
resnetrs101,1095.82,934.443,1024,288,63.62
cait_xxs36_224,1095.14,935.023,1024,224,17.3
efficientnetv2_rw_s,1089.64,939.747,1024,384,23.94
vit_base_patch16_224_sam,1073.62,953.767,1024,224,86.57
vit_base_patch16_224,1073.46,953.912,1024,224,86.57
deit_base_patch16_224,1071.76,955.424,1024,224,86.57
inception_resnet_v2,1061.78,964.405,1024,299,55.84
ens_adv_inception_resnet_v2,1058.32,967.554,1024,299,55.84
deit_base_distilled_patch16_224,1057.89,967.952,1024,224,87.34
tnt_s_patch16_224,1053.73,971.773,1024,224,23.76
dla102x2,1044.22,490.307,512,224,41.28
regnetz_040,1039.59,369.364,384,320,27.12
gluon_seresnext101_64x4d,1039.37,985.196,1024,224,88.23
regnetz_040h,1034.17,371.302,384,320,28.94
resnext101_32x8d,1033.81,990.5,1024,224,88.79
ssl_resnext101_32x8d,1033.7,990.597,1024,224,88.79
swsl_resnext101_32x8d,1033.38,990.91,1024,224,88.79
ig_resnext101_32x8d,1028.15,995.947,1024,224,88.79
regnetz_d32,1009.91,1013.934,1024,320,27.58
resnest50d_4s2x40d,1008.21,1015.647,1024,224,30.42
repvgg_b3,1001.81,1022.132,1024,224,123.09
twins_pcpvt_large,999.56,1024.437,1024,224,60.99
resnet101d,999.12,1024.886,1024,320,44.57
beit_base_patch16_224,993.76,1030.414,1024,224,86.53
convnext_base,982.94,1041.763,1024,224,88.59
convnext_base_in22ft1k,982.23,1042.511,1024,224,88.59
hrnet_w40,980.59,1044.254,1024,224,57.56
coat_tiny,977.86,1047.174,1024,224,5.5
efficientnet_b4,974.19,394.16,384,384,19.34
gluon_xception65,972.42,526.509,512,299,39.92
xception65,965.78,530.126,512,299,39.92
pit_b_224,946.3,541.042,512,224,73.76
regnetz_b16_evos,945.35,541.586,512,288,9.74
pit_b_distilled_224,942.16,543.418,512,224,74.79
tf_efficientnet_b4,925.87,414.732,384,380,19.34
tf_efficientnet_b4_ap,925.37,414.955,384,380,19.34
tf_efficientnet_b4_ns,925.29,414.99,384,380,19.34
vit_small_patch16_36x1_224,920.52,1112.401,1024,224,64.67
swin_v2_cr_small_224,915.78,1118.157,1024,224,49.7
vit_small_patch16_18x2_224,899.5,1138.389,1024,224,64.67
xcit_tiny_24_p16_384_dist,885.13,1156.874,1024,384,12.12
twins_svt_large,884.59,1157.58,1024,224,99.27
nest_small,880.02,581.79,512,224,38.35
hrnet_w48,878.91,1165.063,1024,224,77.47
cait_s24_224,871.81,1174.556,1024,224,46.92
jx_nest_small,870.35,588.255,512,224,38.35
poolformer_m36,860.43,1190.082,1024,224,56.17
regnety_040,849.33,602.817,512,288,20.65
regnetv_040,849.16,602.939,512,288,20.64
nfnet_f0,843.53,1213.933,1024,256,71.49
resnetv2_50d_evos,834.99,1226.346,1024,288,25.59
swin_s3_small_224,821.03,623.593,512,224,49.74
repvgg_b2g4,812.87,1259.724,1024,224,61.76
xcit_medium_24_p16_224_dist,811.19,1262.323,1024,224,84.4
xcit_medium_24_p16_224,810.62,1263.211,1024,224,84.4
dpn131,808.5,1266.522,1024,224,79.25
swin_base_patch4_window7_224,797.2,1284.488,1024,224,87.77
hrnet_w44,791.54,1293.66,1024,224,67.06
coat_mini,786.27,1302.337,1024,224,10.34
regnetx_120,777.95,658.124,512,224,46.11
gmlp_b16_224,772.78,1325.073,1024,224,73.08
dm_nfnet_f0,761.49,1344.713,1024,256,71.49
regnety_120,754.35,678.713,512,224,51.82
densenet264,750.52,1364.368,1024,224,72.69
mixnet_xl,748.42,684.091,512,224,11.9
crossvit_base_240,747.2,685.212,512,240,105.03
xcit_small_12_p16_384_dist,744.87,1374.713,1024,384,26.25
resnetv2_50d_gn,742.22,689.806,512,288,25.57
xception71,741.19,690.762,512,299,42.34
hrnet_w64,723.1,1416.105,1024,224,128.06
vit_large_r50_s32_224,721.71,1418.838,1024,224,328.99
regnety_040s_gn,714.09,716.982,512,224,20.65
seresnet200d,712.88,1436.411,1024,256,71.86
resnet152d,711.54,1439.114,1024,320,60.21
ecaresnet200d,707.4,1447.533,1024,256,64.69
dpn107,701.19,1460.351,1024,224,86.92
cspresnext50_iabn,693.03,1477.558,1024,256,20.57
senet154,692.07,1479.599,1024,224,115.09
legacy_senet154,691.5,1480.826,1024,224,115.09
gluon_senet154,689.56,1484.981,1024,224,115.09
convit_base,687.54,1489.363,1024,224,86.54
vit_small_patch16_384,683.58,748.981,512,384,22.2
volo_d2_224,677.6,1511.204,1024,224,58.68
tnt_b_patch16_224,675.63,1515.603,1024,224,65.41
xcit_nano_12_p8_384_dist,672.93,1521.688,1024,384,3.05
resnext101_64x4d,667.5,767.03,512,288,83.46
xcit_tiny_24_p8_224,665.57,1538.51,1024,224,12.11
swin_s3_base_224,665.44,1538.819,1024,224,71.13
xcit_tiny_24_p8_224_dist,665.24,1539.279,1024,224,12.11
swin_v2_cr_base_224,655.51,1562.118,1024,224,87.88
poolformer_m48,648.5,1579.004,1024,224,73.47
repvgg_b3g4,644.06,1589.905,1024,224,83.83
tresnet_l,638.84,1602.889,1024,224,55.99
resnetrs152,628.07,1630.372,1024,320,86.62
nest_base,626.36,817.402,512,224,67.72
seresnet152d,626.17,1635.31,1024,320,66.84
regnetx_160,623.38,821.322,512,224,54.28
jx_nest_base,620.6,824.996,512,224,67.72
regnetz_e8,607.41,842.906,512,320,57.7
ese_vovnet99b_iabn,606.83,1687.442,1024,224,63.2
regnetz_c16_evos,598.38,855.63,512,320,13.49
vit_base_r50_s16_224,595.98,1718.162,1024,224,98.66
resnest101e,584.85,875.416,512,256,48.28
vit_small_r26_s32_384,582.99,878.207,512,384,36.47
convmixer_768_32,574.69,1781.818,1024,224,21.11
seresnext101_32x8d,574.1,891.822,512,288,93.57
cspdarknet53_iabn,571.87,1790.614,1024,256,27.64
xcit_small_12_p8_224,568.57,1800.991,1024,224,26.21
xcit_small_12_p8_224_dist,567.71,1803.708,1024,224,26.21
seresnet269d,558.78,1832.551,1024,256,113.67
convnext_large_in22ft1k,544.26,940.706,512,224,197.77
convnext_large,544.24,940.754,512,224,197.77
regnety_080,536.38,954.53,512,288,39.18
resnet200d,524.48,1952.377,1024,320,64.69
mixnet_xxl,489.07,785.144,384,224,23.96
halonet_h1,488.76,523.759,256,256,8.1
eca_nfnet_l1,486.84,2103.345,1024,320,41.41
mixer_l16_224,485.71,2108.232,1024,224,208.2
efficientnetv2_m,483.16,2119.353,1024,416,54.14
vit_large_patch32_384,479.45,2135.754,1024,384,306.63
volo_d3_224,473.36,2163.252,1024,224,86.33
tresnet_xl,471.89,2169.959,1024,224,78.44
regnety_064,471.86,1085.046,512,288,30.58
regnetv_064,468.27,1093.376,512,288,30.58
efficientnet_b5,467.72,547.327,256,456,30.39
xcit_large_24_p16_224_dist,464.03,2206.748,1024,224,189.1
xcit_large_24_p16_224,463.92,2207.253,1024,224,189.1
efficientnet_b3_gn,459.87,417.496,192,320,11.73
swin_large_patch4_window7_224,457.64,1118.756,512,224,196.53
resnetrs200,456.26,2244.305,1024,320,93.21
tf_efficientnet_b5_ap,447.29,572.321,256,456,30.39
tf_efficientnet_b5,447.21,572.425,256,456,30.39
tf_efficientnet_b5_ns,446.89,572.83,256,456,30.39
xcit_tiny_12_p8_384_dist,429.76,2382.721,1024,384,6.71
regnety_320,415.34,1232.707,512,224,145.05
regnety_160,414.45,926.515,384,288,83.59
xcit_small_24_p16_384_dist,397.04,2579.039,1024,384,47.67
efficientnetv2_rw_m,393.18,1302.2,512,416,53.24
regnetz_d8_evos,387.65,1320.76,512,320,23.46
swin_v2_cr_large_224,386.59,1324.379,512,224,196.68
efficientnet_b3_g8_gn,385.43,498.136,192,320,14.25
swin_v2_cr_tiny_384,366.25,698.968,256,384,28.33
convnext_xlarge_in22ft1k,359.4,1424.579,512,224,350.2
tf_efficientnetv2_m,359.31,1424.935,512,480,54.14
tf_efficientnetv2_m_in21ft1k,358.34,1428.804,512,480,54.14
vit_large_patch16_224,354.58,2887.929,1024,224,304.33
crossvit_15_dagger_408,354.15,722.849,256,408,28.5
ssl_resnext101_32x16d,347.17,1474.787,512,224,194.03
swsl_resnext101_32x16d,346.95,1475.72,512,224,194.03
vit_base_patch16_18x2_224,346.41,2956.033,1024,224,256.73
ig_resnext101_32x16d,345.97,1479.87,512,224,194.03
convnext_base_384_in22ft1k,335.81,1143.488,384,384,88.59
beit_large_patch16_224,328.45,3117.61,1024,224,304.43
tresnet_m_448,321.84,3181.675,1024,448,31.39
volo_d1_384,310.06,1651.255,512,384,26.78
volo_d4_224,303.03,3379.23,1024,224,192.96
pnasnet5large,299.35,1282.762,384,331,86.06
xcit_small_24_p8_224,297.54,3441.571,1024,224,47.63
xcit_small_24_p8_224_dist,297.52,3441.791,1024,224,47.63
ecaresnet269d,293.49,3489.006,1024,352,102.09
nasnetalarge,291.8,1315.945,384,331,88.75
resnetrs270,287.51,3561.559,1024,352,129.86
nfnet_f1,286.44,3574.909,1024,320,132.63
resnetv2_152x2_bit_teacher,280.03,1828.366,512,224,236.34
xcit_medium_24_p16_384_dist,278.67,1837.25,512,384,84.4
vit_base_patch16_384,277.57,1383.439,384,384,86.86
deit_base_patch16_384,277.28,1384.877,384,384,86.86
deit_base_distilled_patch16_384,273.16,1405.736,384,384,87.63
cait_xxs24_384,268.69,3811.003,1024,384,12.03
efficientnet_b6,268.02,477.57,128,528,43.04
regnetx_320,262.23,1464.357,384,224,107.81
dm_nfnet_f1,261.81,3911.156,1024,320,132.63
vit_large_patch14_224,261.02,3923.008,1024,224,304.2
crossvit_18_dagger_408,259.11,740.985,192,408,44.61
tf_efficientnet_b6_ns,257.39,497.28,128,528,43.04
tf_efficientnet_b6_ap,257.15,497.757,128,528,43.04
tf_efficientnet_b6,257.08,497.876,128,528,43.04
beit_base_patch16_384,239.23,1605.166,384,384,86.74
vit_large_r50_s32_384,236.31,1624.94,384,384,329.09
eca_nfnet_l2,232.83,2198.978,512,384,56.72
xcit_tiny_24_p8_384_dist,226.12,4528.604,1024,384,12.11
swin_v2_cr_small_384,224.11,1142.266,256,384,49.7
swin_base_patch4_window12_384,212.75,902.462,192,384,87.9
resmlp_big_24_distilled_224,209.1,4897.073,1024,224,129.14
resmlp_big_24_224,208.19,4918.549,1024,224,129.14
resmlp_big_24_224_in22ft1k,208.06,4921.598,1024,224,129.14
xcit_medium_24_p8_224,208.01,4922.91,1024,224,84.32
xcit_medium_24_p8_224_dist,207.93,4924.607,1024,224,84.32
resnest200e,201.92,2535.671,512,320,70.2
volo_d5_224,199.55,5131.435,1024,224,295.46
xcit_small_12_p8_384_dist,193.71,2643.156,512,384,26.21
resnetrs350,188.78,2712.164,512,384,163.96
cait_xs24_384,188.52,2715.867,512,384,26.67
efficientnetv2_l,186.87,2739.852,512,480,118.52
convnext_large_384_in22ft1k,186.32,1373.958,256,384,197.77
tf_efficientnetv2_l,185.75,2756.36,512,480,118.52
tf_efficientnetv2_l_in21ft1k,185.69,2757.265,512,480,118.52
vit_base_patch8_224,182.98,1399.048,256,224,86.58
cait_xxs36_384,179.96,5689.985,1024,384,17.37
volo_d2_384,173.85,1472.545,256,384,58.87
vit_base_r50_s16_384,171.75,2235.786,384,384,98.95
vit_base_resnet50_384,171.74,2235.946,384,384,98.95
swin_v2_cr_huge_224,170.46,2252.707,384,224,657.83
densenet264d_iabn,163.84,6249.837,1024,224,72.74
efficientnet_b7,161.28,595.212,96,600,66.35
nfnet_f2,161.05,6358.083,1024,352,193.78
swin_v2_cr_base_384,160.58,1195.67,192,384,87.88
xcit_large_24_p16_384_dist,158.84,3223.264,512,384,189.1
tf_efficientnet_b7,155.96,615.536,96,600,66.35
tf_efficientnet_b7_ap,155.94,615.594,96,600,66.35
tf_efficientnet_b7_ns,155.94,615.59,96,600,66.35
tresnet_l_448,152.84,6699.593,1024,448,55.99
dm_nfnet_f2,147.62,3468.342,512,352,193.78
cait_s24_384,145.6,3516.436,512,384,47.06
efficientnetv2_xl,141.55,2712.868,384,512,208.12
vit_huge_patch14_224,141.3,7246.972,1024,224,632.05
tf_efficientnetv2_xl_in21ft1k,141.27,2718.106,384,512,208.12
resnetrs420,134.22,3814.641,512,416,191.89
swin_large_patch4_window12_384,125.78,1017.629,128,384,196.74
eca_nfnet_l3,122.95,4164.413,512,448,72.04
convnext_xlarge_384_in22ft1k,122.83,1563.128,192,384,350.2
xcit_large_24_p8_224,118.58,4317.779,512,224,188.93
xcit_large_24_p8_224_dist,118.55,4318.826,512,224,188.93
tresnet_xl_448,113.53,9019.968,1024,448,78.44
efficientnet_cc_b0_8e,109.15,9.151,1,224,24.01
efficientnet_cc_b0_4e,106.25,9.403,1,224,13.31
tf_efficientnet_cc_b0_4e,105.28,9.489,1,224,13.31
tf_efficientnet_cc_b0_8e,105.02,9.512,1,224,24.01
xcit_small_24_p8_384_dist,101.44,5047.074,512,384,47.63
efficientnet_b8,98.72,972.467,96,672,87.41
swin_v2_cr_large_384,98.1,1304.769,128,384,196.68
cait_s36_384,97.37,5258.401,512,384,68.37
resnetv2_152x2_bit_teacher_384,96.99,2639.495,256,384,236.34
tf_efficientnet_b8,96.21,997.82,96,672,87.41
tf_efficientnet_b8_ap,96.12,998.706,96,672,87.41
resnest269e,94.82,4049.739,384,416,110.93
vit_large_patch16_384,94.39,2712.048,256,384,304.72
resnetv2_50x3_bitm,92.92,1377.53,128,448,217.32
vit_giant_patch14_224,92.83,5515.228,512,224,1012.61
nfnet_f3,83.35,6142.681,512,416,254.92
convmixer_1024_20_ks9_p14,82.56,12403.804,1024,224,24.38
beit_large_patch16_384,82.25,3112.33,256,384,305.0
efficientnet_cc_b1_8e,77.14,12.953,1,240,39.72
dm_nfnet_f3,76.92,6655.946,512,416,254.92
volo_d3_448,75.82,2532.146,192,448,86.63
tf_efficientnet_cc_b1_8e,73.25,13.64,1,240,39.72
xcit_medium_24_p8_384_dist,70.98,3606.847,256,384,84.32
resnetv2_152x2_bitm,70.34,2729.533,192,448,236.34
resnetv2_101x3_bitm,57.82,2213.862,128,448,387.93
vit_gigantic_patch14_224,56.06,9133.289,512,224,1844.44
volo_d4_448,55.8,3441.115,192,448,193.41
swin_v2_cr_giant_224,49.27,2597.97,128,224,2598.76
nfnet_f4,44.93,8547.007,384,512,316.07
swin_v2_cr_huge_384,43.72,1463.762,64,384,657.94
dm_nfnet_f4,41.24,9312.061,384,512,316.07
xcit_large_24_p8_384_dist,40.33,4760.775,192,384,188.93
volo_d5_448,38.49,3325.731,128,448,295.91
beit_large_patch16_512,33.13,2897.67,96,512,305.67
nfnet_f5,33.1,11599.778,384,544,377.21
cait_m36_384,31.8,8050.244,256,384,271.22
dm_nfnet_f5,31.5,8127.503,256,544,377.21
volo_d5_512,26.9,4758.532,128,512,296.09
nfnet_f6,25.41,10073.116,256,576,438.36
dm_nfnet_f6,23.41,10936.755,256,576,438.36
nfnet_f7,20.63,9307.125,192,608,499.5
resnetv2_152x4_bitm,18.31,3496.111,64,480,936.53
swin_v2_cr_giant_384,13.76,2325.078,32,384,2598.76
cait_m48_448,13.51,9473.095,128,448,356.46
convmixer_1536_20,13.51,75823.541,1024,224,51.63
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt112-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,70939.06,14.424,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,53363.87,19.179,1024,224,0.03,0.92,1.59
lcnet_035,39908.29,25.648,1024,224,0.03,1.04,1.64
mobilenetv3_small_075,38048.72,26.902,1024,224,0.05,1.3,2.04
tinynet_d,35634.7,28.724,1024,152,0.05,1.42,2.34
lcnet_050,35231.0,29.055,1024,224,0.05,1.26,1.88
mobilenetv3_small_100,34913.55,29.319,1024,224,0.06,1.42,2.54
tf_mobilenetv3_small_minimal_100,31288.96,32.716,1024,224,0.06,1.41,2.04
tf_mobilenetv3_small_075,30676.85,33.368,1024,224,0.05,1.3,2.04
lcnet_075,30088.74,34.022,1024,224,0.1,1.99,2.36
tf_mobilenetv3_small_100,28547.5,35.858,1024,224,0.06,1.42,2.54
lcnet_100,23945.91,42.753,1024,224,0.16,2.52,2.95
mnasnet_small,22244.35,46.024,1024,224,0.07,2.16,2.03
levit_128s,22002.58,46.529,1024,224,0.31,1.88,7.78
mobilenetv2_035,20937.19,48.897,1024,224,0.07,2.86,1.68
mnasnet_050,18984.06,53.93,1024,224,0.11,3.07,2.22
ghostnet_050,18415.88,55.593,1024,224,0.05,1.77,2.59
tinynet_c,17846.73,57.365,1024,184,0.11,2.87,2.46
mobilenetv2_050,16928.19,60.48,1024,224,0.1,3.64,1.97
semnasnet_050,16394.61,62.449,1024,224,0.11,3.44,2.08
lcnet_150,15508.0,66.02,1024,224,0.34,3.79,4.5
gernet_s,15282.73,66.993,1024,224,0.75,2.65,8.17
levit_128,14929.05,68.581,1024,224,0.41,2.71,9.21
cs3darknet_focus_s,14654.05,69.868,1024,256,0.69,2.7,3.27
cs3darknet_s,14422.34,70.991,1024,256,0.72,2.97,3.28
mobilenetv3_large_075,14412.2,71.04,1024,224,0.16,4.0,3.99
mixer_s32_224,13576.58,75.414,1024,224,1.0,2.28,19.1
resnet10t,13509.21,75.789,1024,224,1.1,2.43,5.44
mobilenetv3_rw,13202.47,77.551,1024,224,0.23,4.41,5.48
levit_192,13174.02,77.717,1024,224,0.66,3.2,10.95
mobilenetv3_large_100,12955.77,79.027,1024,224,0.23,4.41,5.48
mobilenetv3_large_100_miil,12954.49,79.035,1024,224,0.23,4.41,5.48
vit_small_patch32_224,12913.76,79.284,1024,224,1.15,2.5,22.88
hardcorenas_a,12748.25,80.313,1024,224,0.23,4.38,5.26
mnasnet_075,12700.79,80.614,1024,224,0.23,4.77,3.17
tf_mobilenetv3_large_075,12296.64,83.262,1024,224,0.16,4.0,3.99
tinynet_b,12104.09,84.587,1024,188,0.21,4.44,3.73
tf_mobilenetv3_large_minimal_100,11915.92,85.923,1024,224,0.22,4.4,3.92
hardcorenas_b,11667.78,87.752,1024,224,0.26,5.09,5.18
hardcorenas_c,11602.35,88.247,1024,224,0.28,5.01,5.52
ese_vovnet19b_slim_dw,11450.49,89.417,1024,224,0.4,5.28,1.9
mnasnet_b1,11305.32,90.567,1024,224,0.33,5.46,4.38
mnasnet_100,11303.72,90.578,1024,224,0.33,5.46,4.38
tf_mobilenetv3_large_100,11165.87,91.695,1024,224,0.23,4.41,5.48
gluon_resnet18_v1b,11046.16,92.691,1024,224,1.82,2.48,11.69
ssl_resnet18,11027.01,92.852,1024,224,1.82,2.48,11.69
resnet18,11023.6,92.881,1024,224,1.82,2.48,11.69
swsl_resnet18,11003.86,93.048,1024,224,1.82,2.48,11.69
semnasnet_075,10953.15,93.479,1024,224,0.23,5.54,2.91
regnetx_004,10946.78,93.533,1024,224,0.4,3.14,5.16
hardcorenas_d,10898.13,93.95,1024,224,0.3,4.93,7.5
mobilenetv2_075,10779.36,94.985,1024,224,0.22,5.86,2.64
ghostnet_100,10498.38,97.527,1024,224,0.15,3.55,5.18
seresnet18,10333.85,99.081,1024,224,1.82,2.49,11.78
vit_tiny_r_s16_p8_224,10273.23,99.666,1024,224,0.44,2.06,6.34
spnasnet_100,10240.55,99.983,1024,224,0.35,6.03,4.42
legacy_seresnet18,10026.58,102.118,1024,224,1.82,2.49,11.78
mnasnet_a1,9730.7,105.223,1024,224,0.32,6.23,3.89
semnasnet_100,9728.29,105.249,1024,224,0.32,6.23,3.89
tf_efficientnetv2_b0,9714.68,105.397,1024,224,0.73,4.77,7.14
tinynet_a,9706.17,105.487,1024,192,0.35,5.41,6.19
hardcorenas_f,9654.05,106.058,1024,224,0.35,5.57,8.2
mobilenetv2_100,9591.46,106.751,1024,224,0.31,6.68,3.5
levit_256,9580.42,106.874,1024,224,1.13,4.23,18.89
regnetx_002,9551.99,107.191,1024,224,0.2,2.16,2.68
hardcorenas_e,9521.78,107.531,1024,224,0.35,5.65,8.07
efficientnet_lite0,9415.07,108.751,1024,224,0.4,6.74,4.65
regnety_002,9227.09,110.966,1024,224,0.2,2.17,3.16
fbnetc_100,9182.63,111.504,1024,224,0.4,6.51,5.57
resnet18d,9153.04,111.864,1024,224,2.06,3.29,11.71
regnety_006,9048.64,113.154,1024,224,0.61,4.33,6.06
ese_vovnet19b_slim,8822.42,116.057,1024,224,1.69,3.52,3.17
ghostnet_130,8402.81,121.852,1024,224,0.24,4.6,7.36
regnetx_006,8395.84,121.954,1024,224,0.61,3.98,6.2
levit_256d,8131.84,125.914,1024,224,1.4,4.93,26.21
tf_efficientnet_lite0,8115.07,126.174,1024,224,0.4,6.74,4.65
regnetz_005,8104.32,126.341,1024,224,0.52,5.86,7.12
efficientnet_b0,8015.36,127.743,1024,224,0.4,6.75,5.29
xcit_nano_12_p16_224_dist,7700.28,132.971,1024,224,0.56,4.17,3.05
xcit_nano_12_p16_224,7692.58,133.102,1024,224,0.56,4.17,3.05
mnasnet_140,7484.35,136.808,1024,224,0.6,7.71,7.12
rexnetr_100,7414.06,138.105,1024,224,0.43,7.72,4.88
resnet14t,7298.17,140.296,1024,224,1.69,5.8,10.08
mobilenetv2_110d,7233.3,141.556,1024,224,0.45,8.71,4.52
regnetx_008,7112.68,143.957,1024,224,0.81,5.15,7.26
tf_efficientnet_b0_ns,7058.22,145.067,1024,224,0.4,6.75,5.29
tf_efficientnet_b0_ap,7055.15,145.131,1024,224,0.4,6.75,5.29
tf_efficientnet_b0,7052.24,145.19,1024,224,0.4,6.75,5.29
edgenext_xx_small,6998.78,146.298,1024,256,0.33,4.21,1.33
dla46_c,6933.78,147.671,1024,224,0.58,4.5,1.3
deit_tiny_patch16_224,6855.38,149.36,1024,224,1.26,5.97,5.72
vit_tiny_patch16_224,6844.8,149.592,1024,224,1.26,5.97,5.72
regnety_008,6827.19,149.977,1024,224,0.81,5.25,6.26
gernet_m,6753.27,151.619,1024,224,3.02,5.24,21.14
deit_tiny_distilled_patch16_224,6720.97,152.347,1024,224,1.27,6.01,5.91
efficientnet_b1_pruned,6608.04,154.952,1024,240,0.4,6.21,6.33
hrnet_w18_small,6603.38,155.061,1024,224,1.61,5.72,13.19
gluon_resnet34_v1b,6434.28,159.136,1024,224,3.67,3.74,21.8
semnasnet_140,6428.21,159.287,1024,224,0.6,8.87,6.11
tv_resnet34,6406.04,159.838,1024,224,3.67,3.74,21.8
resnet34,6404.45,159.878,1024,224,3.67,3.74,21.8
ese_vovnet19b_dw,6353.89,161.15,1024,224,1.34,8.25,6.54
rexnet_100,6291.85,162.738,1024,224,0.41,7.44,4.8
mobilenetv2_140,6258.1,163.617,1024,224,0.6,9.57,6.11
mobilevitv2_050,6228.2,164.403,1024,256,0.48,8.04,1.37
efficientnet_lite1,6215.56,164.736,1024,240,0.62,10.14,5.42
tf_efficientnetv2_b1,6143.78,166.661,1024,240,1.21,7.34,8.14
visformer_tiny,6090.13,168.13,1024,224,1.27,5.72,10.32
fbnetv3_b,6012.29,170.307,1024,256,0.55,9.1,8.6
seresnet34,5988.65,170.978,1024,224,3.67,3.74,21.96
resnet26,5923.37,172.863,1024,224,2.36,7.35,16.0
efficientnet_es,5871.09,174.403,1024,224,1.81,8.73,5.44
efficientnet_es_pruned,5866.42,174.542,1024,224,1.81,8.73,5.44
selecsls42,5796.58,176.643,1024,224,2.94,4.62,30.35
pit_ti_distilled_224,5792.34,176.773,1024,224,0.71,6.23,5.1
legacy_seresnet34,5780.67,177.13,1024,224,3.67,3.74,21.96
selecsls42b,5766.92,177.544,1024,224,2.98,4.62,32.46
pit_ti_224,5764.0,177.643,1024,224,0.7,6.19,4.85
resnet34d,5748.0,178.138,1024,224,3.91,4.54,21.82
levit_384,5659.16,180.934,1024,224,2.36,6.26,39.13
tf_efficientnet_es,5608.15,182.58,1024,224,1.81,8.73,5.44
resnetblur18,5572.02,183.764,1024,224,2.34,3.39,11.69
tf_efficientnet_lite1,5541.93,184.761,1024,240,0.62,10.14,5.42
rexnetr_130,5487.71,186.587,1024,224,0.68,9.81,7.61
cs3darknet_m,5481.96,186.783,1024,288,2.63,6.69,9.31
mixnet_s,5402.43,189.533,1024,224,0.25,6.25,4.13
regnety_004,5382.71,190.227,1024,224,0.41,3.89,4.34
skresnet18,5371.9,190.61,1024,224,1.82,3.24,11.96
darknet17,5347.86,143.598,768,256,3.26,7.18,14.3
mobilevit_xxs,5306.36,192.964,1024,256,0.42,8.34,1.27
cs3darknet_focus_m,5289.88,193.566,1024,288,2.51,6.19,9.3
mobilenetv2_120d,5178.64,197.724,1024,224,0.69,11.97,5.83
repvgg_b0,5161.18,198.394,1024,224,3.41,6.15,15.82
xcit_tiny_12_p16_224_dist,5107.76,200.467,1024,224,1.24,6.29,6.72
xcit_tiny_12_p16_224,5104.48,200.597,1024,224,1.24,6.29,6.72
resnet26d,5093.48,201.03,1024,224,2.6,8.15,16.01
tf_mixnet_s,4981.9,205.528,1024,224,0.25,6.25,4.13
vit_base_patch32_224_sam,4925.79,207.875,1024,224,4.41,5.01,88.22
vit_base_patch32_224,4923.14,207.986,1024,224,4.41,5.01,88.22
selecsls60,4922.02,208.033,1024,224,3.59,5.52,30.67
mixer_b32_224,4909.46,208.566,1024,224,3.24,6.29,60.29
selecsls60b,4902.59,208.858,1024,224,3.63,5.52,32.77
rexnetr_150,4887.15,209.518,1024,224,0.89,11.13,9.78
nf_resnet26,4834.78,211.787,1024,224,2.41,7.35,16.0
darknet21,4804.05,159.854,768,256,3.93,7.47,20.86
resmlp_12_distilled_224,4801.62,213.251,1024,224,3.01,5.5,15.35
resmlp_12_224,4801.47,213.257,1024,224,3.01,5.5,15.35
efficientnet_lite2,4791.14,213.716,1024,260,0.89,12.9,6.09
pit_xs_224,4790.75,213.733,1024,224,1.4,7.71,10.62
fbnetv3_d,4788.83,213.819,1024,256,0.68,11.1,10.31
pit_xs_distilled_224,4740.73,215.989,1024,224,1.41,7.76,11.0
dla34,4712.5,217.283,1024,224,3.07,5.02,15.74
sedarknet21,4615.23,166.394,768,256,3.93,7.47,20.95
resnext26ts,4512.74,226.902,1024,256,2.43,10.52,10.3
tf_efficientnetv2_b2,4506.54,227.212,1024,260,1.72,9.84,10.1
mixer_s16_224,4471.0,229.021,1024,224,3.79,5.97,18.53
legacy_seresnext26_32x4d,4467.81,229.184,1024,224,2.49,9.39,16.79
edgenext_x_small,4458.42,229.664,1024,256,0.68,7.5,2.34
gernet_l,4450.89,230.055,1024,256,4.57,8.0,31.08
tf_efficientnet_b1,4403.29,232.542,1024,240,0.71,10.88,7.79
tf_efficientnet_b1_ns,4402.75,232.57,1024,240,0.71,10.88,7.79
tf_efficientnet_b1_ap,4402.24,232.597,1024,240,0.71,10.88,7.79
eca_resnext26ts,4354.84,235.13,1024,256,2.43,10.52,10.3
seresnext26ts,4350.24,235.378,1024,256,2.43,10.52,10.39
tf_efficientnet_lite2,4300.72,238.087,1024,260,0.89,12.9,6.09
gcresnext26ts,4295.78,238.361,1024,256,2.43,10.53,10.48
rexnet_130,4282.77,239.085,1024,224,0.68,9.71,7.56
efficientnet_b1,4273.32,239.615,1024,256,0.77,12.22,7.79
gmlp_ti16_224,4143.15,247.143,1024,224,1.34,7.55,5.87
efficientnet_b0_g16_evos,4127.77,248.064,1024,224,1.01,7.42,8.11
crossvit_tiny_240,4122.07,248.408,1024,240,1.57,9.08,7.01
nf_ecaresnet26,4104.24,249.486,1024,224,2.41,7.36,16.0
efficientnet_b2_pruned,4103.05,249.558,1024,260,0.73,9.13,8.31
nf_seresnet26,4102.39,249.599,1024,224,2.41,7.36,17.4
mobilevitv2_075,4066.07,251.829,1024,256,1.05,12.06,2.87
vit_small_patch32_384,4025.63,254.359,1024,384,3.45,8.25,22.92
ecaresnext50t_32x4d,4000.29,255.97,1024,224,2.7,10.09,15.41
ecaresnext26t_32x4d,3998.13,256.108,1024,224,2.7,10.09,15.41
seresnext26tn_32x4d,3995.56,256.274,1024,224,2.7,10.09,16.81
seresnext26t_32x4d,3993.92,256.378,1024,224,2.7,10.09,16.81
seresnext26d_32x4d,3982.98,257.083,1024,224,2.73,10.19,16.81
resnet26t,3922.56,261.042,1024,256,3.35,10.52,16.01
dla46x_c,3917.72,261.363,1024,224,0.54,5.66,1.07
rexnet_150,3870.49,264.554,1024,224,0.9,11.21,9.73
resnetv2_50,3868.33,264.701,1024,224,4.11,11.11,25.55
crossvit_9_240,3865.11,264.922,1024,240,1.85,9.52,8.55
convnext_nano_ols,3859.14,265.333,1024,224,2.5,8.37,15.6
nf_regnet_b0,3819.49,268.086,1024,256,0.64,5.58,8.76
ecaresnet50d_pruned,3812.92,268.549,1024,224,2.53,6.43,19.94
regnetx_016,3808.7,268.846,1024,224,1.62,7.93,9.19
crossvit_9_dagger_240,3792.18,270.018,1024,240,1.99,9.97,8.78
dla60x_c,3787.69,270.337,1024,224,0.59,6.01,1.32
convnext_nano_hnf,3786.83,270.399,1024,224,2.46,8.37,15.59
ecaresnetlight,3729.3,274.57,1024,224,4.11,8.42,30.16
poolformer_s12,3722.39,275.081,1024,224,1.82,5.53,11.92
gmixer_12_224,3686.02,277.795,1024,224,2.67,7.26,12.7
gluon_resnet50_v1b,3677.5,278.438,1024,224,4.11,11.11,25.56
resnet50,3676.35,278.526,1024,224,4.11,11.11,25.56
ssl_resnet50,3674.86,278.638,1024,224,4.11,11.11,25.56
tv_resnet50,3673.31,278.756,1024,224,4.11,11.11,25.56
swsl_resnet50,3672.81,278.794,1024,224,4.11,11.11,25.56
dpn68,3650.67,280.484,1024,224,2.35,10.47,12.61
dpn68b,3606.63,283.907,1024,224,2.35,10.47,12.61
botnet26t_256,3555.59,287.983,1024,256,3.32,11.98,12.49
regnety_016,3516.07,291.222,1024,224,1.63,8.04,11.2
repvgg_a2,3514.85,291.324,1024,224,5.7,6.26,28.21
resnetv2_50t,3513.08,291.47,1024,224,4.32,11.82,25.57
efficientnet_em,3504.92,292.149,1024,240,3.04,14.34,6.9
mixnet_m,3496.31,292.868,1024,224,0.36,8.19,5.01
resnetv2_50d,3496.21,292.876,1024,224,4.35,11.92,25.57
rexnetr_200,3492.0,219.921,768,224,1.59,15.11,16.52
halonet26t,3480.88,294.167,1024,256,3.19,11.69,12.48
gluon_resnet50_v1c,3476.97,294.498,1024,224,4.35,11.92,25.58
resnet32ts,3465.97,295.433,1024,256,4.63,11.58,17.96
bat_resnext26ts,3457.26,296.173,1024,256,2.53,12.51,10.73
resnet33ts,3415.09,299.834,1024,256,4.76,11.66,19.68
tf_efficientnet_b2_ns,3414.14,299.918,1024,260,1.02,13.83,9.11
tf_efficientnet_b2,3412.6,300.052,1024,260,1.02,13.83,9.11
tf_efficientnet_b2_ap,3412.15,300.092,1024,260,1.02,13.83,9.11
tf_efficientnet_em,3389.12,302.132,1024,240,3.04,14.34,6.9
resnet50t,3345.79,306.045,1024,224,4.32,11.82,25.57
gluon_resnet50_v1d,3337.44,306.809,1024,224,4.35,11.92,25.58
resnet50d,3336.65,306.882,1024,224,4.35,11.92,25.58
vit_tiny_r_s16_p8_384,3314.06,154.482,512,384,1.34,6.49,6.36
legacy_seresnet50,3311.17,309.245,1024,224,3.88,10.6,28.09
seresnet33ts,3304.6,309.859,1024,256,4.76,11.66,19.78
tf_mixnet_m,3303.11,309.997,1024,224,0.36,8.19,5.01
eca_resnet33ts,3297.96,310.484,1024,256,4.76,11.66,19.68
convit_tiny,3289.83,311.251,1024,224,1.26,7.94,5.71
gcresnet33ts,3263.32,313.778,1024,256,4.76,11.68,19.88
vit_small_resnet26d_224,3252.42,314.83,1024,224,5.07,11.12,63.61
vovnet39a,3229.96,317.018,1024,224,7.09,6.73,22.6
efficientnet_b2a,3221.33,317.868,1024,288,1.12,16.2,9.11
efficientnet_b2,3221.08,317.894,1024,288,1.12,16.2,9.11
efficientnet_b3_pruned,3195.08,320.48,1024,300,1.04,11.86,9.86
seresnet50,3183.22,321.675,1024,224,4.11,11.13,28.09
cs3darknet_l,3171.55,322.859,1024,288,6.16,10.83,21.16
cs3darknet_focus_l,3146.07,325.473,1024,288,5.9,10.16,21.15
res2net50_48w_2s,3141.04,325.995,1024,224,4.18,11.72,25.29
eca_vovnet39b,3123.78,327.796,1024,224,7.09,6.74,22.6
ese_vovnet39b,3117.85,328.419,1024,224,7.09,6.74,24.57
mobilevit_xs,3095.24,248.113,768,256,1.05,16.33,2.32
resnext50_32x4d,3092.67,331.094,1024,224,4.26,14.4,25.03
ssl_resnext50_32x4d,3089.61,331.422,1024,224,4.26,14.4,25.03
gluon_resnext50_32x4d,3087.43,331.656,1024,224,4.26,14.4,25.03
swsl_resnext50_32x4d,3086.28,331.779,1024,224,4.26,14.4,25.03
tv_resnext50_32x4d,3085.42,331.872,1024,224,4.26,14.4,25.03
hrnet_w18_small_v2,3075.57,332.934,1024,224,2.62,9.65,15.6
eca_botnext26ts_256,3069.01,333.646,1024,256,2.46,11.6,10.59
dla60,3049.62,335.767,1024,224,4.26,10.16,22.04
vgg11,3048.59,167.933,512,224,7.61,7.44,132.86
skresnet34,3035.0,337.386,1024,224,3.67,5.13,22.28
mobilevitv2_100,3028.13,253.611,768,256,1.84,16.08,4.9
vit_small_patch16_224,3005.65,340.679,1024,224,4.61,11.95,22.05
deit_small_patch16_224,3005.16,340.735,1024,224,4.61,11.95,22.05
eca_halonext26ts,2980.4,343.567,1024,256,2.44,11.46,10.76
resnetaa50d,2972.16,344.518,1024,224,5.39,12.44,25.58
ecaresnet101d_pruned,2963.47,345.529,1024,224,3.48,7.69,24.88
coat_lite_tiny,2957.59,346.216,1024,224,1.6,11.65,5.72
cs3sedarknet_l,2955.87,346.418,1024,288,6.16,10.83,21.91
deit_small_distilled_patch16_224,2950.21,347.082,1024,224,4.63,12.02,22.44
gluon_resnet50_v1s,2947.99,347.343,1024,224,5.47,13.52,25.68
resnetrs50,2945.06,347.688,1024,224,4.48,12.14,35.69
seresnet50t,2939.07,348.397,1024,224,4.32,11.83,28.1
densenet121,2936.15,348.744,1024,224,2.87,6.9,7.98
pit_s_224,2935.87,348.777,1024,224,2.88,11.56,23.46
ecaresnet50d,2934.67,348.92,1024,224,4.35,11.93,25.58
tv_densenet121,2927.3,349.799,1024,224,2.87,6.9,7.98
selecsls84,2927.1,349.822,1024,224,5.9,7.57,50.95
pit_s_distilled_224,2909.88,351.892,1024,224,2.9,11.64,24.04
vit_relpos_base_patch32_plus_rpn_256,2858.63,358.203,1024,256,7.68,8.01,119.42
deit3_small_patch16_224_in21ft1k,2858.2,358.255,1024,224,4.61,11.95,22.06
deit3_small_patch16_224,2853.97,358.786,1024,224,4.61,11.95,22.06
resnext50d_32x4d,2849.78,359.313,1024,224,4.5,15.2,25.05
vit_relpos_small_patch16_rpn_224,2814.18,363.86,1024,224,4.59,13.05,21.97
densenet121d,2808.28,364.624,1024,224,3.11,7.7,8.0
cspresnet50,2805.19,365.024,1024,256,4.54,11.5,21.62
vit_relpos_small_patch16_224,2800.45,365.643,1024,224,4.59,13.05,21.98
gcresnext50ts,2798.56,365.89,1024,256,3.75,15.46,15.67
vit_srelpos_small_patch16_224,2795.09,366.343,1024,224,4.59,12.16,21.97
coat_lite_mini,2774.65,369.044,1024,224,2.0,12.25,11.01
haloregnetz_b,2774.49,369.064,1024,224,1.97,11.94,11.68
vit_base_patch32_plus_256,2772.64,369.31,1024,256,7.79,7.76,119.48
rexnet_200,2762.14,278.034,768,224,1.56,14.91,16.37
res2net50_26w_4s,2757.69,371.313,1024,224,4.28,12.61,25.7
seresnext50_32x4d,2741.98,373.44,1024,224,4.26,14.42,27.56
gluon_seresnext50_32x4d,2737.7,374.024,1024,224,4.26,14.42,27.56
legacy_seresnext50_32x4d,2737.27,374.083,1024,224,4.26,14.42,27.56
xcit_tiny_24_p16_224_dist,2733.14,374.648,1024,224,2.34,11.82,12.12
xcit_tiny_24_p16_224,2731.46,374.879,1024,224,2.34,11.82,12.12
xcit_nano_12_p16_384_dist,2727.34,375.445,1024,384,1.64,12.15,3.05
dla60x,2724.01,375.903,1024,224,3.54,13.8,17.35
gcresnet50t,2718.77,376.628,1024,256,5.42,14.67,25.9
vgg11_bn,2701.91,189.486,512,224,7.62,7.44,132.87
visformer_small,2695.9,379.825,1024,224,4.88,11.43,40.22
lambda_resnet26rpt_256,2689.85,380.679,1024,256,3.16,11.87,10.99
mixnet_l,2682.98,286.237,768,224,0.58,10.84,7.33
resnetblur50,2682.32,381.746,1024,224,5.16,12.02,25.56
vovnet57a,2674.97,382.797,1024,224,8.95,7.52,36.64
efficientnet_lite3,2659.04,192.54,512,300,1.65,21.85,8.2
cspresnet50d,2649.78,386.434,1024,256,4.86,12.55,21.64
seresnetaa50d,2644.86,387.154,1024,224,5.4,12.46,28.11
efficientnetv2_rw_t,2633.28,388.856,1024,288,3.19,16.42,13.65
cspresnet50w,2624.25,390.195,1024,256,5.04,12.19,28.12
twins_svt_small,2599.22,393.951,1024,224,2.94,13.75,24.06
tf_efficientnetv2_b3,2587.08,395.8,1024,300,3.04,15.74,14.36
nf_regnet_b2,2583.08,396.413,1024,272,1.22,9.27,14.31
vit_base_resnet26d_224,2575.01,397.656,1024,224,6.97,13.16,101.4
ese_vovnet57b,2572.63,398.024,1024,224,8.95,7.52,38.61
fbnetv3_g,2570.51,398.353,1024,288,1.77,21.09,16.62
gc_efficientnetv2_rw_t,2557.73,400.342,1024,288,3.2,16.45,13.68
tf_mixnet_l,2550.98,301.047,768,224,0.58,10.84,7.33
nf_regnet_b1,2527.71,405.098,1024,288,1.02,9.2,10.22
res2net50_14w_8s,2514.56,407.217,1024,224,4.21,13.28,25.06
inception_v3,2512.32,407.579,1024,299,5.73,8.97,23.83
densenetblur121d,2509.93,407.967,1024,224,3.11,7.9,8.0
adv_inception_v3,2509.35,408.057,1024,299,5.73,8.97,23.83
tf_inception_v3,2505.31,408.714,1024,299,5.73,8.97,23.83
gluon_inception_v3,2501.93,409.271,1024,299,5.73,8.97,23.83
resnetblur50d,2498.32,409.863,1024,224,5.4,12.82,25.58
nf_ecaresnet50,2492.25,410.862,1024,224,4.21,11.13,25.56
nf_seresnet50,2488.35,411.506,1024,224,4.21,11.13,28.09
resmlp_24_224,2465.19,415.371,1024,224,5.96,10.91,30.02
resmlp_24_distilled_224,2463.93,415.584,1024,224,5.96,10.91,30.02
mobilevit_s,2450.02,313.456,768,256,2.03,19.94,5.58
regnetx_032,2449.76,417.988,1024,224,3.2,11.37,15.3
cspresnext50,2440.84,419.515,1024,256,4.05,15.86,20.57
dla60_res2net,2430.52,421.296,1024,224,4.15,12.34,20.85
resnest14d,2424.63,422.32,1024,224,2.76,7.33,10.61
densenet169,2421.68,422.835,1024,224,3.4,7.3,14.15
convnext_tiny_hnfd,2418.1,423.46,1024,224,4.47,13.44,28.59
convnext_tiny_hnf,2414.45,424.101,1024,224,4.47,13.44,28.59
tf_efficientnet_lite3,2396.12,213.668,512,300,1.65,21.85,8.2
sehalonet33ts,2392.73,427.951,1024,256,3.55,14.7,13.69
efficientnet_cc_b0_4e,2389.83,428.47,1024,224,0.41,9.42,13.31
efficientnet_cc_b0_8e,2386.88,429.0,1024,224,0.42,9.42,24.01
convnext_tiny_in22ft1k,2380.94,430.068,1024,224,4.47,13.44,28.59
convnext_tiny,2379.5,430.329,1024,224,4.47,13.44,28.59
regnetz_b16,2348.93,435.929,1024,288,2.39,16.43,9.72
resnetv2_101,2321.74,441.036,1024,224,7.83,16.23,44.54
convnext_nano,2304.31,444.37,1024,288,4.06,13.84,15.59
tf_efficientnet_cc_b0_4e,2293.39,446.488,1024,224,0.41,9.42,13.31
semobilevit_s,2279.96,336.836,768,256,2.03,19.95,5.74
gluon_resnet101_v1b,2249.95,455.108,1024,224,7.83,16.23,44.55
tv_resnet101,2246.24,455.861,1024,224,7.83,16.23,44.55
resnet101,2246.09,455.891,1024,224,7.83,16.23,44.55
mobilevitv2_125,2233.52,343.842,768,256,2.86,20.1,7.48
skresnet50,2232.97,458.569,1024,224,4.11,12.5,25.8
ecaresnet26t,2203.93,464.611,1024,320,5.24,16.44,16.01
resnetv2_101d,2180.36,469.635,1024,224,8.07,17.04,44.56
gluon_resnet101_v1c,2174.26,470.952,1024,224,8.08,17.04,44.57
twins_pcpvt_small,2160.75,473.897,1024,224,3.83,18.08,24.11
xcit_small_12_p16_224_dist,2141.07,478.253,1024,224,4.82,12.58,26.25
xcit_small_12_p16_224,2140.23,478.441,1024,224,4.82,12.58,26.25
cs3darknet_focus_x,2138.85,478.75,1024,256,8.03,10.69,35.02
edgenext_small,2120.03,482.998,1024,320,1.97,14.16,5.59
gluon_resnet101_v1d,2116.52,483.8,1024,224,8.08,17.04,44.57
tf_efficientnet_cc_b0_8e,2114.86,484.181,1024,224,0.42,9.42,24.01
vgg13,2106.04,486.207,1024,224,11.31,12.25,133.05
skresnet50d,2104.19,486.637,1024,224,4.36,13.31,25.82
xcit_nano_12_p8_224_dist,2091.47,489.594,1024,224,2.16,15.71,3.05
xcit_nano_12_p8_224,2088.8,490.222,1024,224,2.16,15.71,3.05
sebotnet33ts_256,2061.15,248.392,512,256,3.89,17.46,13.7
efficientnet_b0_gn,2058.74,373.032,768,224,0.42,6.75,5.29
wide_resnet50_2,2057.11,497.774,1024,224,11.43,14.4,68.88
dla102,2034.05,503.416,1024,224,7.19,14.18,33.27
vit_base_resnet50d_224,2032.15,503.887,1024,224,8.73,16.92,110.97
resnet51q,2003.15,511.181,1024,288,8.07,20.94,35.7
legacy_seresnet101,1997.44,512.643,1024,224,7.61,15.74,49.33
regnetx_040,1987.99,515.08,1024,224,3.99,12.2,22.12
gmlp_s16_224,1983.97,516.124,1024,224,4.42,15.1,19.42
res2net50_26w_6s,1970.9,519.546,1024,224,6.33,15.28,37.05
resnetaa101d,1964.05,521.361,1024,224,9.12,17.56,44.57
gluon_resnet101_v1s,1954.76,523.835,1024,224,9.19,18.64,44.67
seresnet101,1951.77,524.639,1024,224,7.84,16.27,49.33
repvgg_b1,1949.45,525.265,1024,224,13.16,10.64,57.42
crossvit_small_240,1948.12,525.623,1024,240,5.63,18.17,26.86
cs3sedarknet_xdw,1942.13,527.244,1024,256,5.97,17.18,21.6
resnetaa50,1934.76,529.254,1024,288,8.52,19.24,25.56
swin_tiny_patch4_window7_224,1924.88,531.966,1024,224,4.51,17.06,28.29
resnext101_32x4d,1924.17,532.166,1024,224,8.01,21.23,44.18
ssl_resnext101_32x4d,1923.99,532.215,1024,224,8.01,21.23,44.18
poolformer_s24,1923.48,532.355,1024,224,3.41,10.68,21.39
gluon_resnext101_32x4d,1922.64,532.587,1024,224,8.01,21.23,44.18
swsl_resnext101_32x4d,1922.44,532.644,1024,224,8.01,21.23,44.18
vit_relpos_medium_patch16_cls_224,1918.8,533.655,1024,224,8.03,18.24,38.76
vit_relpos_medium_patch16_rpn_224,1917.82,533.927,1024,224,7.97,17.02,38.73
vit_relpos_medium_patch16_224,1911.97,535.56,1024,224,7.97,17.02,38.75
vit_srelpos_medium_patch16_224,1908.42,536.559,1024,224,7.96,16.21,38.74
resnest50d_1s4x24d,1891.51,541.355,1024,224,4.43,13.57,25.68
darknet53,1883.2,407.805,768,288,11.78,15.68,41.61
gmixer_24_224,1881.95,544.104,1024,224,5.28,14.45,24.72
darknetaa53,1875.8,409.414,768,288,10.08,15.68,36.02
densenet201,1868.67,547.971,1024,224,4.34,7.85,20.01
halonet50ts,1867.02,548.456,1024,256,5.3,19.2,22.73
mobilevitv2_150,1865.71,274.416,512,256,4.09,24.11,10.59
mobilevitv2_150_in22ft1k,1864.94,274.529,512,256,4.09,24.11,10.59
tf_efficientnet_b3_ns,1862.24,274.927,512,300,1.87,23.83,12.23
tf_efficientnet_b3,1861.19,275.081,512,300,1.87,23.83,12.23
tf_efficientnet_b3_ap,1860.71,275.153,512,300,1.87,23.83,12.23
nf_resnet101,1854.11,552.273,1024,224,8.01,16.23,44.55
dla102x,1853.94,552.322,1024,224,5.89,19.42,26.31
ecaresnet101d,1853.67,552.405,1024,224,8.08,17.07,44.57
vgg13_bn,1850.18,276.718,512,224,11.33,12.25,133.05
cspdarknet53,1837.13,418.032,768,256,6.57,16.81,27.64
efficientnet_b3a,1829.01,279.921,512,320,2.01,26.52,12.23
efficientnet_b3,1828.85,279.946,512,320,2.01,26.52,12.23
mixnet_xl,1821.62,281.057,512,224,0.93,14.57,11.9
resnet61q,1810.56,565.559,1024,288,9.87,21.52,36.85
vit_small_r26_s32_224,1806.33,566.883,1024,224,3.56,9.85,36.43
xcit_tiny_12_p16_384_dist,1805.03,567.29,1024,384,3.64,18.26,6.72
edgenext_small_rw,1803.36,567.813,1024,320,2.46,14.85,7.83
crossvit_15_240,1792.63,571.217,1024,240,5.81,19.77,27.53
resnest26d,1781.59,574.753,1024,224,3.64,9.97,17.07
nf_resnet50,1773.35,577.425,1024,288,6.88,18.37,25.56
hrnet_w18,1766.17,579.773,1024,224,4.32,16.31,21.3
crossvit_15_dagger_240,1755.64,583.25,1024,240,6.13,20.43,28.21
swin_s3_tiny_224,1746.31,586.364,1024,224,4.64,19.13,28.33
resnetblur101d,1744.07,587.12,1024,224,9.12,17.94,44.57
res2net101_26w_4s,1715.56,596.875,1024,224,8.1,18.45,45.21
cait_xxs24_224,1707.34,599.75,1024,224,2.53,20.29,11.96
nf_regnet_b3,1702.33,601.516,1024,320,2.05,14.61,18.59
seresnext101_32x4d,1701.46,601.825,1024,224,8.02,21.26,48.96
gluon_seresnext101_32x4d,1701.17,601.927,1024,224,8.02,21.26,48.96
legacy_seresnext101_32x4d,1697.89,603.09,1024,224,8.02,21.26,48.96
resnetv2_50d_frn,1691.2,605.475,1024,224,4.33,11.92,25.59
vgg16,1690.51,605.724,1024,224,15.47,13.56,138.36
repvgg_b1g4,1670.27,613.064,1024,224,8.15,10.64,39.97
res2net50_26w_8s,1662.2,616.038,1024,224,8.37,17.95,48.4
resmlp_36_224,1656.29,618.237,1024,224,8.91,16.33,44.69
resmlp_36_distilled_224,1655.44,618.553,1024,224,8.91,16.33,44.69
regnetz_c16,1654.15,309.514,512,320,3.92,25.88,13.46
sequencer2d_s,1646.91,621.756,1024,224,4.96,11.31,27.65
efficientnet_b0_g8_gn,1636.03,469.418,768,224,0.66,6.75,6.56
botnet50ts_256,1633.5,313.424,512,256,5.54,22.23,22.74
vit_large_patch32_224,1629.91,628.244,1024,224,15.39,13.3,306.54
ese_vovnet39b_evos,1627.7,629.096,1024,224,7.07,6.74,24.58
cs3darknet_x,1627.54,629.156,1024,288,10.6,14.36,35.05
resnetv2_50d_evob,1620.95,631.713,1024,224,4.33,11.92,25.59
efficientnet_cc_b1_8e,1619.01,632.471,1024,240,0.75,15.44,39.72
resnetv2_152,1614.21,634.351,1024,224,11.55,22.56,60.19
xception41p,1587.33,322.543,512,299,9.25,39.86,26.91
regnetx_064,1586.15,484.18,768,224,6.49,16.37,26.21
coat_lite_small,1583.04,646.84,1024,224,3.96,22.09,19.84
swinv2_cr_tiny_224,1581.48,647.481,1024,224,4.66,28.45,28.33
xception,1578.2,486.619,768,299,8.4,35.83,22.86
gluon_resnet152_v1b,1573.68,650.689,1024,224,11.56,22.56,60.19
tv_resnet152,1573.22,650.883,1024,224,11.56,22.56,60.19
resnetv2_50x1_bit_distilled,1572.94,650.997,1024,224,4.23,11.11,25.55
resnet152,1571.71,651.505,1024,224,11.56,22.56,60.19
tf_efficientnet_cc_b1_8e,1564.9,654.344,1024,240,0.75,15.44,39.72
halo2botnet50ts_256,1559.2,656.732,1024,256,5.02,21.78,22.64
mixer_l32_224,1558.59,656.992,1024,224,11.27,19.86,206.94
vit_tiny_patch16_384,1557.53,657.441,1024,384,4.7,25.39,5.79
mobilevitv2_175,1554.07,329.447,512,256,5.54,28.13,14.25
swinv2_cr_tiny_ns_224,1551.84,659.847,1024,224,4.66,28.45,28.33
mobilevitv2_175_in22ft1k,1551.58,329.973,512,256,5.54,28.13,14.25
vit_base_patch32_384,1550.87,660.263,1024,384,13.06,16.5,88.3
cs3sedarknet_x,1549.02,661.048,1024,288,10.6,14.37,35.4
resnetv2_152d,1545.41,662.596,1024,224,11.8,23.36,60.2
nf_ecaresnet101,1540.66,664.639,1024,224,8.01,16.27,44.55
nf_seresnet101,1538.7,665.483,1024,224,8.02,16.27,49.33
gluon_resnet152_v1c,1536.27,666.538,1024,224,11.8,23.36,60.21
efficientnet_el,1524.59,335.818,512,300,8.0,30.7,10.59
efficientnet_el_pruned,1523.29,336.105,512,300,8.0,30.7,10.59
gluon_resnet152_v1d,1508.62,678.753,1024,224,11.8,23.36,60.21
vgg16_bn,1504.44,340.315,512,224,15.5,13.56,138.37
twins_pcpvt_base,1494.56,685.139,1024,224,6.68,25.25,43.83
tf_efficientnet_el,1481.51,345.582,512,300,8.0,30.7,10.59
cs3edgenet_x,1479.52,692.101,1024,288,14.59,16.36,47.82
vit_base_r26_s32_224,1479.31,692.202,1024,224,6.81,12.36,101.38
skresnext50_32x4d,1465.64,698.657,1024,224,4.5,17.18,27.48
convnext_small,1452.43,705.012,1024,224,8.71,21.56,50.22
convnext_small_in22ft1k,1450.42,705.987,1024,224,8.71,21.56,50.22
hrnet_w32,1444.27,708.991,1024,224,8.97,22.02,41.23
ese_vovnet99b,1442.7,709.767,1024,224,16.51,11.27,63.2
mixer_b16_224,1424.87,718.648,1024,224,12.62,14.53,59.88
gluon_resnet152_v1s,1424.84,718.665,1024,224,12.92,24.96,60.32
ecaresnet50t,1422.0,720.1,1024,320,8.82,24.13,25.57
mixer_b16_224_miil,1421.45,720.381,1024,224,12.62,14.53,59.88
vgg19,1411.57,725.42,1024,224,19.63,14.86,143.67
regnety_032,1398.62,732.137,1024,288,5.29,18.61,19.44
convit_small,1398.4,732.254,1024,224,5.76,17.87,27.78
nest_tiny,1387.46,553.516,768,224,5.83,25.48,17.06
legacy_seresnet152,1382.46,740.694,1024,224,11.33,22.08,66.82
dla169,1382.4,740.729,1024,224,11.6,20.2,53.39
xcit_tiny_12_p8_224,1374.74,744.858,1024,224,4.81,23.6,6.71
xcit_tiny_12_p8_224_dist,1374.04,745.236,1024,224,4.81,23.6,6.71
densenet161,1366.11,749.563,1024,224,7.79,11.06,28.68
jx_nest_tiny,1362.5,563.656,768,224,5.83,25.48,17.06
seresnet152,1361.36,752.174,1024,224,11.57,22.61,66.82
mobilevitv2_200_in22ft1k,1354.63,283.461,384,256,7.22,32.15,18.45
mobilevitv2_200,1354.25,283.54,384,256,7.22,32.15,18.45
xception41,1347.67,379.903,512,299,9.28,39.86,26.97
inception_v4,1323.37,773.767,1024,299,12.28,15.09,42.68
twins_svt_base,1316.07,778.059,1024,224,8.59,26.33,56.07
vit_small_resnet50d_s16_224,1305.38,784.435,1024,224,13.48,24.82,57.53
dpn92,1303.44,785.601,1024,224,6.54,18.21,37.67
tresnet_m,1297.91,788.947,1024,224,5.74,7.31,31.39
poolformer_s36,1296.72,789.674,1024,224,5.0,15.82,30.86
sequencer2d_m,1285.21,796.745,1024,224,6.55,14.26,38.31
crossvit_18_240,1273.5,804.072,1024,240,9.05,26.26,43.27
regnetx_080,1271.94,805.056,1024,224,8.02,14.06,39.57
dla102x2,1271.93,402.524,512,224,9.34,29.91,41.28
vgg19_bn,1265.83,404.467,512,224,19.66,14.86,143.68
efficientnet_lite4,1263.67,303.867,384,380,4.04,45.66,13.01
crossvit_18_dagger_240,1245.15,822.375,1024,240,9.5,27.03,44.27
res2next50,1241.53,824.775,1024,224,4.2,13.71,24.67
volo_d1_224,1235.2,829.0,1024,224,6.94,24.43,26.63
efficientnetv2_s,1221.73,838.141,1024,384,8.44,35.77,21.46
resnest50d,1214.35,843.233,1024,224,5.4,14.36,27.48
tf_efficientnetv2_s_in21ft1k,1191.03,859.741,1024,384,8.44,35.77,21.46
tf_efficientnetv2_s,1191.03,859.748,1024,384,8.44,35.77,21.46
dpn98,1188.11,861.858,1024,224,11.73,25.2,61.57
mixnet_xxl,1187.83,323.267,384,224,2.04,23.43,23.96
swin_small_patch4_window7_224,1183.48,865.227,1024,224,8.77,27.47,49.61
regnetz_d8,1180.44,867.458,1024,320,6.19,37.08,23.37
hrnet_w30,1180.28,867.576,1024,224,8.15,21.21,37.71
gluon_resnext101_64x4d,1176.11,870.653,1024,224,15.52,31.21,83.46
efficientnetv2_rw_s,1166.72,877.658,1024,384,8.72,38.03,23.94
tf_efficientnet_lite4,1164.88,329.636,384,380,4.04,45.66,13.01
swinv2_tiny_window8_256,1158.39,883.971,1024,256,5.96,24.57,28.35
wide_resnet101_2,1155.6,886.11,1024,224,22.8,21.23,126.89
repvgg_b2,1154.84,886.691,1024,224,20.45,12.9,89.02
vit_base_patch16_224_miil,1153.59,887.648,1024,224,17.58,23.9,86.54
resnet50_gn,1150.43,890.083,1024,224,4.14,11.11,25.56
resnet200,1149.46,890.84,1024,224,15.07,32.19,64.67
cait_xxs36_224,1148.13,891.873,1024,224,3.77,30.34,17.3
xception65p,1140.81,448.791,512,299,13.91,52.48,39.82
regnetz_040,1140.64,336.641,384,320,6.35,37.78,27.12
xcit_small_24_p16_224_dist,1137.7,900.049,1024,224,9.1,23.64,47.67
xcit_small_24_p16_224,1136.58,900.934,1024,224,9.1,23.64,47.67
regnetz_040h,1135.68,338.111,384,320,6.43,37.94,28.94
deit_base_patch16_224,1133.84,903.113,1024,224,17.58,23.9,86.57
vit_base_patch16_224,1133.45,903.419,1024,224,17.58,23.9,86.57
vit_base_patch16_224_sam,1132.11,904.493,1024,224,17.58,23.9,86.57
regnetz_d32,1128.13,907.679,1024,320,9.33,37.08,27.58
dla60_res2next,1127.83,907.922,1024,224,3.49,13.17,17.03
eca_nfnet_l0,1126.64,908.88,1024,288,7.12,17.29,24.14
resnetrs101,1123.2,911.667,1024,288,13.56,28.53,63.62
nfnet_l0,1123.1,911.747,1024,288,7.13,17.29,35.07
cs3se_edgenet_x,1120.46,913.898,1024,320,18.01,20.21,50.72
deit_base_distilled_patch16_224,1119.36,914.798,1024,224,17.68,24.05,87.34
vit_base_patch16_rpn_224,1111.53,921.235,1024,224,17.49,23.75,86.54
inception_resnet_v2,1108.79,923.511,1024,299,13.18,25.06,55.84
ens_adv_inception_resnet_v2,1107.31,924.747,1024,299,13.18,25.06,55.84
deit3_base_patch16_224,1093.88,936.101,1024,224,17.58,23.9,86.59
deit3_base_patch16_224_in21ft1k,1092.45,937.33,1024,224,17.58,23.9,86.59
gluon_seresnext101_64x4d,1088.94,940.353,1024,224,15.53,31.25,88.23
vit_relpos_base_patch16_clsgap_224,1081.95,946.422,1024,224,17.6,25.12,86.43
vit_relpos_base_patch16_cls_224,1081.77,946.586,1024,224,17.6,25.12,86.43
vit_relpos_base_patch16_rpn_224,1080.35,947.826,1024,224,17.51,24.97,86.41
vit_relpos_base_patch16_224,1079.52,948.56,1024,224,17.51,24.97,86.43
tnt_s_patch16_224,1078.48,949.47,1024,224,5.24,24.37,23.76
twins_pcpvt_large,1066.87,959.801,1024,224,9.84,35.82,60.99
ssl_resnext101_32x8d,1054.92,970.677,1024,224,16.48,31.21,88.79
resnext101_32x8d,1054.71,970.869,1024,224,16.48,31.21,88.79
ig_resnext101_32x8d,1054.19,971.349,1024,224,16.48,31.21,88.79
swsl_resnext101_32x8d,1053.69,971.812,1024,224,16.48,31.21,88.79
beit_base_patch16_224,1049.2,975.962,1024,224,17.58,23.9,86.53
resnest50d_4s2x40d,1042.05,982.666,1024,224,4.4,17.94,30.42
coat_tiny,1040.04,984.566,1024,224,4.35,27.2,5.5
resnet101d,1029.19,994.942,1024,320,16.48,34.77,44.57
convnext_base,1012.72,1011.124,1024,224,15.38,28.75,88.59
convnext_base_in22ft1k,1011.75,1012.088,1024,224,15.38,28.75,88.59
efficientnet_b4,993.87,386.356,384,384,4.51,50.04,19.34
pit_b_224,992.43,515.895,512,224,12.42,32.94,73.76
pit_b_distilled_224,988.79,517.791,512,224,12.5,33.07,74.79
gluon_xception65,981.26,521.764,512,299,13.96,52.48,39.92
xception65,975.37,524.918,512,299,13.96,52.48,39.92
vit_small_patch16_36x1_224,973.92,1051.404,1024,224,13.71,35.69,64.67
repvgg_b3,972.06,1053.416,1024,224,29.16,15.1,123.09
repvgg_b2g4,969.39,1056.316,1024,224,12.63,12.9,61.76
xcit_tiny_24_p16_384_dist,969.37,1056.345,1024,384,6.87,34.29,12.12
swinv2_cr_small_224,967.97,1057.869,1024,224,9.07,50.27,49.7
swinv2_cr_small_ns_224,957.86,1069.034,1024,224,9.08,50.27,49.7
vit_small_patch16_18x2_224,951.03,1076.715,1024,224,13.71,35.69,64.67
twins_svt_large,923.66,1108.616,1024,224,15.15,35.1,99.27
tf_efficientnet_b4,922.69,416.164,384,380,4.49,49.49,19.34
tf_efficientnet_b4_ap,922.5,416.247,384,380,4.49,49.49,19.34
tf_efficientnet_b4_ns,922.41,416.289,384,380,4.49,49.49,19.34
hrnet_w40,910.46,1124.691,1024,224,12.75,25.29,57.56
regnetz_b16_evos,903.54,849.98,768,288,2.36,16.43,9.74
cait_s24_224,902.24,1134.941,1024,224,9.35,40.58,46.92
nfnet_f0,901.6,1135.748,1024,256,12.62,18.05,71.49
nf_regnet_b4,900.78,1136.78,1024,384,4.7,28.61,30.21
poolformer_m36,896.53,1142.174,1024,224,8.8,22.02,56.17
nest_small,884.77,868.006,768,224,10.35,40.04,38.35
hrnet_w48,875.56,1169.527,1024,224,17.34,28.56,77.47
jx_nest_small,874.29,878.41,768,224,10.35,40.04,38.35
dpn131,866.66,1181.531,1024,224,16.09,32.97,79.25
swin_s3_small_224,854.8,898.443,768,224,9.43,37.84,49.74
regnety_040,854.48,898.782,768,288,6.61,20.3,20.65
regnetv_040,854.18,899.093,768,288,6.6,20.3,20.64
regnety_080,846.16,605.071,512,288,13.22,29.69,39.18
resnetv2_50d_evos,844.23,1212.929,1024,288,7.15,19.7,25.59
coat_mini,836.76,1223.748,1024,224,6.82,33.68,10.34
swin_base_patch4_window7_224,836.19,1224.59,1024,224,15.47,36.63,87.77
repvgg_b3g4,835.5,1225.597,1024,224,17.89,15.1,83.83
sequencer2d_l,832.91,1229.407,1024,224,9.74,22.12,54.3
dm_nfnet_f0,831.16,1232.004,1024,256,12.62,18.05,71.49
mobilevitv2_150_384_in22ft1k,826.94,309.566,256,384,9.2,54.25,10.59
gmlp_b16_224,825.63,1240.256,1024,224,15.78,30.21,73.08
convnext_tiny_384_in22ft1k,814.55,628.553,512,384,13.14,39.48,28.59
xcit_medium_24_p16_224_dist,812.18,1260.787,1024,224,16.13,31.71,84.4
xcit_medium_24_p16_224,812.16,1260.822,1024,224,16.13,31.71,84.4
regnetx_120,810.53,631.674,512,224,12.13,21.37,46.11
xcit_small_12_p16_384_dist,800.29,1279.521,1024,384,14.14,36.51,26.25
densenet264,798.84,1281.845,1024,224,12.95,12.8,72.69
hrnet_w44,790.45,1295.441,1024,224,14.94,26.92,67.06
crossvit_base_240,787.5,975.226,768,240,21.22,36.33,105.03
regnety_120,783.87,653.154,512,224,12.14,21.38,51.82
swinv2_tiny_window16_256,765.77,668.599,512,256,6.68,39.02,28.35
resnetv2_50d_gn,751.14,1022.427,768,288,7.24,19.7,25.57
xception71,749.93,682.721,512,299,18.09,69.92,42.34
vit_large_r50_s32_224,739.74,1384.252,1024,224,19.58,24.41,328.99
vit_base_patch16_plus_240,737.95,1387.618,1024,240,27.41,33.08,117.56
dpn107,736.42,1390.497,1024,224,18.38,33.46,86.92
resnet152d,736.09,1391.121,1024,320,24.08,47.67,60.21
ecaresnet200d,730.76,1401.271,1024,256,20.0,43.15,64.69
seresnet200d,728.63,1405.363,1024,256,20.01,43.15,71.86
vit_relpos_base_patch16_plus_240,727.74,1407.074,1024,240,27.3,34.33,117.38
xcit_tiny_24_p8_224,725.69,1411.06,1024,224,9.21,45.39,12.11
xcit_tiny_24_p8_224_dist,725.66,1411.108,1024,224,9.21,45.39,12.11
hrnet_w64,719.99,1422.231,1024,224,28.97,35.09,128.06
regnety_040s_gn,719.08,1068.018,768,224,4.03,12.29,20.65
xcit_nano_12_p8_384_dist,713.49,1435.191,1024,384,6.34,46.08,3.05
swinv2_small_window8_256,712.51,1437.16,1024,256,11.58,40.14,49.73
convit_base,706.35,1449.693,1024,224,17.52,31.77,86.54
resnext101_64x4d,704.57,1090.007,768,288,25.66,51.59,83.46
swin_s3_base_224,695.14,1473.064,1024,224,13.69,48.26,71.13
vit_small_patch16_384,693.62,1107.217,768,384,15.52,50.78,22.2
tnt_b_patch16_224,691.83,1480.107,1024,224,14.09,39.01,65.41
swinv2_cr_base_224,689.84,1484.394,1024,224,15.86,59.66,87.88
regnety_064,684.37,748.122,512,288,10.56,27.11,30.58
swinv2_cr_base_ns_224,683.96,1497.137,1024,224,15.86,59.66,87.88
volo_d2_224,679.94,1506.002,1024,224,14.34,41.34,58.68
mobilevitv2_175_384_in22ft1k,679.77,376.586,256,384,12.47,63.29,14.25
regnetv_064,678.0,755.149,512,288,10.55,27.11,30.58
poolformer_m48,676.06,1514.652,1024,224,11.59,29.17,73.47
deit3_small_patch16_384,669.71,1146.752,768,384,15.52,50.78,22.21
deit3_small_patch16_384_in21ft1k,669.36,1147.345,768,384,15.52,50.78,22.21
legacy_senet154,660.41,1550.529,1024,224,20.77,38.69,115.09
senet154,659.33,1553.081,1024,224,20.77,38.69,115.09
gluon_senet154,659.08,1553.657,1024,224,20.77,38.69,115.09
regnetx_160,650.99,786.486,512,224,15.99,25.52,54.28
resnetrs152,646.22,1584.595,1024,320,24.34,48.14,86.62
seresnet152d,642.19,1594.525,1024,320,24.09,47.72,66.84
regnetz_e8,633.28,1212.715,768,320,15.46,63.94,57.7
tresnet_l,630.57,1623.908,1024,224,10.88,11.9,55.99
nest_base,629.43,813.42,512,224,17.96,53.39,67.72
ese_vovnet99b_iabn,628.09,1630.325,1024,224,16.49,11.27,63.2
jx_nest_base,622.33,822.697,512,224,17.96,53.39,67.72
vit_small_r26_s32_384,613.78,834.168,512,384,10.43,29.85,36.47
vit_base_r50_s16_224,610.7,1676.739,1024,224,21.66,35.29,98.66
xcit_small_12_p8_224,609.86,1679.054,1024,224,18.69,47.21,26.21
xcit_small_12_p8_224_dist,609.57,1679.853,1024,224,18.69,47.21,26.21
efficientnetv2_m,603.09,1697.911,1024,416,18.6,67.5,54.14
mobilevitv2_200_384_in22ft1k,594.06,323.186,192,384,16.24,72.34,18.45
seresnext101_32x8d,590.8,1299.927,768,288,27.24,51.63,93.57
resnest101e,588.3,1305.448,768,256,13.38,28.66,48.28
regnetz_c16_evos,588.05,870.656,512,320,3.86,25.88,13.49
convmixer_768_32,578.91,1768.818,1024,224,19.55,25.95,21.11
seresnext101d_32x8d,575.36,1334.812,768,288,27.64,52.95,93.59
seresnet269d,570.63,1794.5,1024,256,26.59,53.6,113.67
convnext_large,561.47,1823.764,1024,224,34.4,43.13,197.77
convnext_large_in22ft1k,560.92,1825.557,1024,224,34.4,43.13,197.77
resnet200d,544.65,1880.08,1024,320,31.25,67.33,64.69
efficientnetv2_rw_m,534.87,1435.852,768,416,21.49,79.62,53.24
seresnextaa101d_32x8d,521.71,1472.057,768,288,28.51,56.44,93.59
vit_large_patch32_384,517.33,1979.373,1024,384,45.31,43.86,306.63
swinv2_base_window8_256,509.01,2011.746,1024,256,20.37,52.59,87.92
eca_nfnet_l1,497.06,2060.113,1024,320,14.92,34.42,41.41
convnext_small_384_in22ft1k,496.5,1031.206,512,384,25.58,63.37,50.22
mixer_l16_224,495.85,2065.122,1024,224,44.6,41.69,208.2
efficientnet_b5,493.19,519.054,256,456,10.46,98.86,30.39
halonet_h1,492.19,520.115,256,256,3.0,51.17,8.1
regnety_320,483.53,1058.87,512,224,32.34,30.26,145.05
swin_large_patch4_window7_224,480.21,1599.271,768,224,34.53,54.94,196.53
swinv2_small_window16_256,477.23,1072.842,512,256,12.82,66.29,49.73
volo_d3_224,474.32,2158.852,1024,224,20.78,60.09,86.33
resnetrs200,471.22,2173.072,1024,320,31.51,67.81,93.21
tf_efficientnet_b5_ns,469.35,545.419,256,456,10.46,98.86,30.39
tf_efficientnet_b5_ap,469.08,545.738,256,456,10.46,98.86,30.39
tf_efficientnet_b5,468.97,545.864,256,456,10.46,98.86,30.39
xcit_tiny_12_p8_384_dist,468.35,2186.374,1024,384,14.13,69.14,6.71
tresnet_xl,466.16,2196.648,1024,224,15.17,15.34,78.44
efficientnet_b3_gn,464.67,550.914,256,320,2.14,28.83,11.73
xcit_large_24_p16_224_dist,454.83,2251.393,1024,224,35.86,47.27,189.1
xcit_large_24_p16_224,454.8,2251.543,1024,224,35.86,47.27,189.1
tf_efficientnetv2_m_in21ft1k,444.81,1726.544,768,480,24.76,89.84,54.14
tf_efficientnetv2_m,444.79,1726.633,768,480,24.76,89.84,54.14
xcit_small_24_p16_384_dist,426.37,2401.669,1024,384,26.72,68.58,47.67
regnety_160,422.88,908.045,384,288,26.37,38.07,83.59
nf_regnet_b5,413.3,1238.797,512,456,11.7,61.95,49.74
swinv2_cr_tiny_384,408.94,625.992,256,384,15.34,161.01,28.33
swinv2_cr_large_224,406.2,1890.673,768,224,35.1,78.42,196.68
resnetv2_50x1_bitm,400.73,958.233,384,448,16.62,44.46,25.55
regnetz_d8_evos,392.85,1954.947,768,320,7.03,38.92,23.46
convmixer_1024_20_ks9_p14,390.62,2621.469,1024,224,5.55,5.51,24.38
efficientnet_b3_g8_gn,373.24,685.874,256,320,3.2,28.83,14.25
vit_large_patch16_224,370.72,2762.206,1024,224,61.6,63.52,304.33
convnext_xlarge_in22ft1k,368.8,1388.275,512,224,60.98,57.5,350.2
crossvit_15_dagger_408,368.65,694.421,256,408,21.45,95.05,28.5
vit_base_patch16_18x2_224,361.92,2829.305,1024,224,52.51,71.38,256.73
deit3_large_patch16_224_in21ft1k,358.3,2857.929,1024,224,61.6,63.52,304.37
deit3_large_patch16_224,357.82,2861.791,1024,224,61.6,63.52,304.37
swinv2_base_window16_256,346.22,1109.109,384,256,22.02,84.71,87.92
swinv2_base_window12to16_192to256_22kft1k,345.97,1109.898,384,256,22.02,84.71,87.92
nasnetalarge,345.75,1110.628,384,331,23.89,90.56,88.75
convnext_base_384_in22ft1k,345.74,1110.63,384,384,45.21,84.49,88.59
beit_large_patch16_224,343.01,2985.299,1024,224,61.6,63.52,304.43
ssl_resnext101_32x16d,338.92,1510.65,512,224,36.27,51.18,194.03
ig_resnext101_32x16d,338.75,1511.419,512,224,36.27,51.18,194.03
swsl_resnext101_32x16d,338.52,1512.441,512,224,36.27,51.18,194.03
tresnet_m_448,325.14,3149.347,1024,448,22.94,29.21,31.39
pnasnet5large,323.98,1185.25,384,331,25.04,92.89,86.06
regnetx_320,320.95,1196.437,384,224,31.81,36.3,107.81
xcit_small_24_p8_224,319.26,3207.434,1024,224,35.81,90.78,47.63
xcit_small_24_p8_224_dist,319.16,3208.44,1024,224,35.81,90.78,47.63
volo_d1_384,315.83,1621.098,512,384,22.75,108.55,26.78
nfnet_f1,306.71,3338.679,1024,320,35.97,46.77,132.63
ecaresnet269d,304.87,3358.754,1024,352,50.25,101.25,102.09
volo_d4_224,303.38,3375.331,1024,224,44.34,80.22,192.96
xcit_medium_24_p16_384_dist,297.67,2580.013,768,384,47.39,91.64,84.4
resnetrs270,296.93,3448.599,1024,352,51.13,105.48,129.86
resnetv2_152x2_bit_teacher,290.64,2642.472,768,224,46.95,45.11,236.34
vit_base_patch16_384,289.22,1327.696,384,384,55.54,101.56,86.86
deit_base_patch16_384,289.04,1328.502,384,384,55.54,101.56,86.86
deit_base_distilled_patch16_384,285.12,1346.806,384,384,55.65,101.82,87.63
dm_nfnet_f1,282.58,3623.721,1024,320,35.97,46.77,132.63
deit3_base_patch16_384,281.04,1366.341,384,384,55.54,101.56,86.88
deit3_base_patch16_384_in21ft1k,280.92,1366.918,384,384,55.54,101.56,86.88
efficientnet_b6,279.71,457.611,128,528,19.4,167.39,43.04
cait_xxs24_384,275.0,3723.573,1024,384,9.63,122.66,12.03
vit_large_patch14_224,271.58,3770.566,1024,224,81.08,88.79,304.2
crossvit_18_dagger_408,269.56,712.259,192,408,32.47,124.87,44.61
tf_efficientnet_b6_ap,267.5,478.495,128,528,19.4,167.39,43.04
tf_efficientnet_b6,267.48,478.534,128,528,19.4,167.39,43.04
tf_efficientnet_b6_ns,267.38,478.715,128,528,19.4,167.39,43.04
efficientnetv2_l,254.55,2011.402,512,480,56.4,157.99,118.52
resnetv2_101x1_bitm,252.46,1521.025,384,448,31.65,64.93,44.54
tf_efficientnetv2_l,251.41,2036.496,512,480,56.4,157.99,118.52
tf_efficientnetv2_l_in21ft1k,251.09,2039.122,512,480,56.4,157.99,118.52
swinv2_cr_small_384,250.5,1021.951,256,384,29.7,298.03,49.7
beit_base_patch16_384,248.36,1546.143,384,384,55.54,101.56,86.74
xcit_tiny_24_p8_384_dist,246.6,4152.437,1024,384,27.05,132.95,12.11
vit_large_r50_s32_384,246.12,2080.308,512,384,57.43,76.52,329.09
eca_nfnet_l2,237.09,3239.214,768,384,30.05,68.28,56.72
resmlp_big_24_224,228.25,4486.35,1024,224,100.23,87.31,129.14
resmlp_big_24_224_in22ft1k,227.96,4491.908,1024,224,100.23,87.31,129.14
resmlp_big_24_distilled_224,227.94,4492.471,1024,224,100.23,87.31,129.14
xcit_medium_24_p8_224_dist,222.27,3455.29,768,224,63.53,121.23,84.32
xcit_medium_24_p8_224,222.27,3455.239,768,224,63.53,121.23,84.32
swin_base_patch4_window12_384,221.01,868.732,192,384,47.19,134.78,87.9
swinv2_large_window12to16_192to256_22kft1k,212.32,1205.699,256,256,47.81,121.53,196.74
xcit_small_12_p8_384_dist,207.32,1852.22,384,384,54.92,138.29,26.21
resnest200e,201.89,2536.056,512,320,35.69,82.78,70.2
volo_d5_224,200.39,5110.009,1024,224,72.4,118.11,295.46
resnetrs350,194.78,3942.989,768,384,77.59,154.74,163.96
convnext_large_384_in22ft1k,191.43,1337.313,256,384,101.1,126.74,197.77
cait_xs24_384,190.0,4042.088,768,384,19.28,183.98,26.67
vit_base_patch8_224,188.23,1360.04,256,224,78.22,161.69,86.58
cait_xxs36_384,183.87,5569.221,1024,384,14.35,183.7,17.37
swinv2_cr_base_384,178.69,1432.608,256,384,50.57,333.68,87.88
vit_base_r50_s16_384,176.73,1448.509,256,384,67.43,135.03,98.95
vit_base_resnet50_384,176.72,1448.639,256,384,67.43,135.03,98.95
volo_d2_384,176.17,2179.696,384,384,46.17,184.51,58.87
swinv2_cr_huge_224,175.72,2185.293,384,224,115.97,121.08,657.83
nfnet_f2,172.68,5929.942,1024,352,63.22,79.06,193.78
xcit_large_24_p16_384_dist,168.33,3041.628,512,384,105.35,137.17,189.1
densenet264d_iabn,166.37,6155.083,1024,224,13.47,14.0,72.74
efficientnet_b7,161.46,594.574,96,600,38.33,289.94,66.35
efficientnetv2_xl,159.28,2410.894,384,512,93.85,247.32,208.12
dm_nfnet_f2,159.16,4825.445,768,352,63.22,79.06,193.78
tf_efficientnetv2_xl_in21ft1k,158.05,2429.572,384,512,93.85,247.32,208.12
tf_efficientnet_b7,155.86,615.938,96,600,38.33,289.94,66.35
tf_efficientnet_b7_ap,155.83,616.029,96,600,38.33,289.94,66.35
tf_efficientnet_b7_ns,155.78,616.248,96,600,38.33,289.94,66.35
tresnet_l_448,151.99,6737.079,1024,448,43.5,47.56,55.99
cait_s24_384,148.32,3451.871,512,384,32.17,245.31,47.06
vit_huge_patch14_224,146.38,6995.606,1024,224,167.4,139.41,632.05
ig_resnext101_32x32d,143.03,1789.868,256,224,87.29,91.12,468.53
resnetrs420,142.89,5374.778,768,416,108.45,213.79,191.89
deit3_huge_patch14_224,142.28,7197.12,1024,224,167.4,139.41,632.13
deit3_huge_patch14_224_in21ft1k,142.06,7208.264,1024,224,167.4,139.41,632.13
eca_nfnet_l3,132.47,3864.977,512,448,52.55,118.4,72.04
swin_large_patch4_window12_384,130.79,978.633,128,384,104.08,202.16,196.74
convnext_xlarge_384_in22ft1k,126.14,2029.4,256,384,179.2,168.99,350.2
xcit_large_24_p8_224,125.6,4076.305,512,224,141.23,181.56,188.93
xcit_large_24_p8_224_dist,125.51,4079.48,512,224,141.23,181.56,188.93
tresnet_xl_448,111.87,9153.659,1024,448,60.65,61.31,78.44
xcit_small_24_p8_384_dist,108.62,3535.115,384,384,105.24,265.91,47.63
swinv2_cr_large_384,108.25,1182.395,128,384,108.95,404.96,196.68
efficientnet_b8,101.79,943.075,96,672,63.48,442.89,87.41
resnetv2_50x3_bitm,101.1,1266.075,128,448,145.7,133.37,217.32
cait_s36_384,99.17,5162.791,512,384,47.99,367.4,68.37
tf_efficientnet_b8,98.82,971.416,96,672,63.48,442.89,87.41
tf_efficientnet_b8_ap,98.81,971.518,96,672,63.48,442.89,87.41
resnetv2_152x2_bit_teacher_384,98.49,2599.208,256,384,136.16,132.56,236.34
vit_large_patch16_384,97.65,2621.481,256,384,191.21,270.24,304.72
vit_giant_patch14_224,95.7,8025.142,768,224,267.18,192.64,1012.61
deit3_large_patch16_384,94.92,2697.101,256,384,191.21,270.24,304.76
deit3_large_patch16_384_in21ft1k,94.92,2697.083,256,384,191.21,270.24,304.76
swinv2_base_window12to24_192to384_22kft1k,94.52,677.087,64,384,55.25,280.36,87.92
nfnet_f3,93.84,8184.276,768,416,115.58,141.78,254.92
resnest269e,93.31,4115.183,384,416,77.69,171.98,110.93
dm_nfnet_f3,86.56,5914.801,512,416,115.58,141.78,254.92
beit_large_patch16_384,84.77,3019.757,256,384,191.21,270.24,305.0
volo_d3_448,76.33,2515.269,192,448,96.33,446.83,86.63
xcit_medium_24_p8_384_dist,75.97,3369.835,256,384,186.67,354.73,84.32
ig_resnext101_32x48d,74.47,2578.247,192,224,153.57,131.06,828.41
resnetv2_152x2_bitm,72.95,2631.902,192,448,184.99,180.43,236.34
tf_efficientnet_l2_ns_475,63.13,1013.713,64,475,172.11,609.89,480.31
resnetv2_101x3_bitm,60.06,2131.166,128,448,280.33,194.78,387.93
swinv2_large_window12to24_192to384_22kft1k,59.82,802.459,48,384,116.15,407.83,196.74
vit_gigantic_patch14_224,57.64,8883.342,512,224,483.95,275.37,1844.44
volo_d4_448,56.09,3423.322,192,448,197.13,527.35,193.41
convmixer_1536_20,53.95,18979.912,1024,224,48.68,33.03,51.63
swinv2_cr_giant_224,50.56,2531.46,128,224,483.85,309.15,2598.76
nfnet_f4,49.93,10254.329,512,512,216.26,262.26,316.07
swinv2_cr_huge_384,47.13,1357.909,64,384,352.04,583.18,657.94
dm_nfnet_f4,45.97,8353.673,384,512,216.26,262.26,316.07
xcit_large_24_p8_384_dist,42.84,4481.402,192,384,415.0,531.82,188.93
volo_d5_448,38.62,3314.317,128,448,315.06,737.92,295.91
nfnet_f5,37.03,10370.994,384,544,290.97,349.71,377.21
dm_nfnet_f5,33.93,11318.026,384,544,290.97,349.71,377.21
beit_large_patch16_512,33.87,2834.401,96,512,362.24,656.39,305.67
cait_m36_384,32.22,7945.944,256,384,173.11,734.81,271.22
nfnet_f6,28.33,13554.319,384,576,378.69,452.2,438.36
volo_d5_512,26.99,4742.789,128,512,425.09,1105.37,296.09
dm_nfnet_f6,26.14,9792.719,256,576,378.69,452.2,438.36
resnetv2_152x4_bitm,24.34,2629.892,64,480,844.84,414.26,936.53
efficientnet_l2,23.12,1037.889,24,800,479.12,1707.39,480.31
tf_efficientnet_l2_ns,22.7,1057.422,24,800,479.12,1707.39,480.31
nfnet_f7,22.34,11460.175,256,608,480.39,570.85,499.5
swinv2_cr_giant_384,14.63,2187.208,32,384,1450.71,1394.86,2598.76
cait_m48_448,13.64,9385.159,128,448,329.41,1708.23,356.46
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt113-cu117-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,72737.62,14.068,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,54822.3,18.668,1024,224,0.03,0.92,1.59
lcnet_035,53629.35,19.084,1024,224,0.03,1.04,1.64
lcnet_050,45492.41,22.499,1024,224,0.05,1.26,1.88
mobilenetv3_small_075,39215.51,26.102,1024,224,0.05,1.3,2.04
tinynet_d,37346.61,27.409,1024,152,0.05,1.42,2.34
mobilenetv3_small_100,36280.34,28.214,1024,224,0.06,1.42,2.54
tf_mobilenetv3_small_minimal_100,31726.33,32.265,1024,224,0.06,1.41,2.04
tf_mobilenetv3_small_075,31503.43,32.494,1024,224,0.05,1.3,2.04
lcnet_075,29817.69,34.332,1024,224,0.1,1.99,2.36
tf_mobilenetv3_small_100,29444.91,34.767,1024,224,0.06,1.42,2.54
mnasnet_small,25354.86,40.376,1024,224,0.07,2.16,2.03
lcnet_100,24134.76,42.417,1024,224,0.16,2.52,2.95
regnetx_002,23983.4,42.686,1024,224,0.2,2.16,2.68
levit_128s,22675.73,45.148,1024,224,0.31,1.88,7.78
regnety_002,21709.37,47.158,1024,224,0.2,2.17,3.16
mobilenetv2_035,21673.44,47.236,1024,224,0.07,2.86,1.68
mnasnet_050,20010.27,51.163,1024,224,0.11,3.07,2.22
ghostnet_050,18932.82,54.075,1024,224,0.05,1.77,2.59
tinynet_c,18428.42,55.556,1024,184,0.11,2.87,2.46
semnasnet_050,17215.18,59.471,1024,224,0.11,3.44,2.08
mobilenetv2_050,17194.94,59.542,1024,224,0.1,3.64,1.97
cs3darknet_focus_s,16189.76,63.24,1024,256,0.69,2.7,3.27
lcnet_150,15557.15,65.811,1024,224,0.34,3.79,4.5
cs3darknet_s,15369.47,66.615,1024,256,0.72,2.97,3.28
levit_128,15337.67,66.754,1024,224,0.41,2.71,9.21
gernet_s,15288.68,66.966,1024,224,0.75,2.65,8.17
mobilenetv3_large_075,14216.3,72.019,1024,224,0.16,4.0,3.99
mixer_s32_224,14182.92,72.188,1024,224,1.0,2.28,19.1
vit_tiny_r_s16_p8_224,14125.39,72.482,1024,224,0.44,2.06,6.34
resnet10t,14112.07,72.551,1024,224,1.1,2.43,5.44
vit_small_patch32_224,13799.47,74.195,1024,224,1.15,2.5,22.88
regnetx_004,13610.2,75.225,1024,224,0.4,3.14,5.16
levit_192,13524.14,75.706,1024,224,0.66,3.2,10.95
mobilenetv3_rw,12956.58,79.021,1024,224,0.23,4.41,5.48
hardcorenas_a,12803.61,79.966,1024,224,0.23,4.38,5.26
mobilenetv3_large_100,12749.93,80.304,1024,224,0.23,4.41,5.48
mnasnet_075,12532.36,81.697,1024,224,0.23,4.77,3.17
tf_mobilenetv3_large_075,12186.51,84.017,1024,224,0.16,4.0,3.99
tinynet_b,12083.18,84.735,1024,188,0.21,4.44,3.73
regnety_004,11918.36,85.906,1024,224,0.41,3.89,4.34
tf_mobilenetv3_large_minimal_100,11715.94,87.392,1024,224,0.22,4.4,3.92
hardcorenas_c,11548.05,88.662,1024,224,0.28,5.01,5.52
hardcorenas_b,11510.71,88.949,1024,224,0.26,5.09,5.18
ese_vovnet19b_slim_dw,11501.95,89.018,1024,224,0.4,5.28,1.9
ghostnet_100,11332.61,90.348,1024,224,0.15,3.55,5.18
mnasnet_100,11138.43,91.923,1024,224,0.33,5.46,4.38
gluon_resnet18_v1b,11098.78,92.252,1024,224,1.82,2.48,11.69
resnet18,11083.1,92.383,1024,224,1.82,2.48,11.69
swsl_resnet18,11062.48,92.555,1024,224,1.82,2.48,11.69
ssl_resnet18,11061.11,92.565,1024,224,1.82,2.48,11.69
tf_mobilenetv3_large_100,11018.56,92.922,1024,224,0.23,4.41,5.48
mnasnet_b1,10993.58,93.135,1024,224,0.33,5.46,4.38
hardcorenas_d,10910.47,93.843,1024,224,0.3,4.93,7.5
semnasnet_075,10898.09,93.951,1024,224,0.23,5.54,2.91
mobilenetv2_075,10893.76,93.988,1024,224,0.22,5.86,2.64
seresnet18,10385.56,98.588,1024,224,1.82,2.49,11.78
legacy_seresnet18,10064.41,101.734,1024,224,1.82,2.49,11.78
spnasnet_100,10009.21,102.296,1024,224,0.35,6.03,4.42
tf_efficientnetv2_b0,9930.95,103.1,1024,224,0.73,4.77,7.14
levit_256,9858.1,103.863,1024,224,1.13,4.23,18.89
tinynet_a,9720.11,105.337,1024,192,0.35,5.41,6.19
hardcorenas_f,9714.91,105.393,1024,224,0.35,5.57,8.2
semnasnet_100,9623.78,106.393,1024,224,0.32,6.23,3.89
mnasnet_a1,9623.77,106.393,1024,224,0.32,6.23,3.89
mobilenetv2_100,9598.91,106.667,1024,224,0.31,6.68,3.5
hardcorenas_e,9571.87,106.966,1024,224,0.35,5.65,8.07
dla46_c,9568.4,107.007,1024,224,0.58,4.5,1.3
efficientnet_lite0,9361.14,109.377,1024,224,0.4,6.74,4.65
fbnetc_100,9352.03,109.484,1024,224,0.4,6.51,5.57
resnet18d,9334.83,109.687,1024,224,2.06,3.29,11.71
ese_vovnet19b_slim,9109.47,112.4,1024,224,1.69,3.52,3.17
regnety_006,9097.63,112.542,1024,224,0.61,4.33,6.06
regnetz_005,8607.49,118.955,1024,224,0.52,5.86,7.12
xcit_nano_12_p16_224_dist,8577.2,119.375,1024,224,0.56,4.17,3.05
xcit_nano_12_p16_224,8554.61,119.689,1024,224,0.56,4.17,3.05
levit_256d,8382.88,122.143,1024,224,1.4,4.93,26.21
regnetx_006,8379.52,122.192,1024,224,0.61,3.98,6.2
ghostnet_130,8278.59,123.681,1024,224,0.24,4.6,7.36
tf_efficientnet_lite0,8080.51,126.714,1024,224,0.4,6.74,4.65
efficientnet_b0,7965.17,128.548,1024,224,0.4,6.75,5.29
mnasnet_140,7779.42,131.618,1024,224,0.6,7.71,7.12
deit_tiny_distilled_patch16_224,7467.68,137.113,1024,224,1.27,6.01,5.91
rexnetr_100,7464.12,137.179,1024,224,0.43,7.72,4.88
deit_tiny_patch16_224,7430.15,137.806,1024,224,1.26,5.97,5.72
resnet14t,7429.68,137.815,1024,224,1.69,5.8,10.08
vit_tiny_patch16_224,7424.93,137.902,1024,224,1.26,5.97,5.72
regnetx_008,7394.88,138.463,1024,224,0.81,5.15,7.26
mobilenetv2_110d,7247.12,141.287,1024,224,0.45,8.71,4.52
hrnet_w18_small,7232.93,141.561,1024,224,1.61,5.72,13.19
tf_efficientnet_b0,7016.18,145.938,1024,224,0.4,6.75,5.29
regnety_008,6938.46,147.571,1024,224,0.81,5.25,6.26
mobilevitv2_050,6848.87,149.503,1024,256,0.48,8.04,1.37
pit_ti_distilled_224,6811.68,150.317,1024,224,0.71,6.23,5.1
pit_ti_224,6784.24,150.927,1024,224,0.7,6.19,4.85
gernet_m,6679.85,153.286,1024,224,3.02,5.24,21.14
efficientnet_b1_pruned,6642.37,154.15,1024,240,0.4,6.21,6.33
resnet34,6496.42,157.614,1024,224,3.67,3.74,21.8
gluon_resnet34_v1b,6494.61,157.658,1024,224,3.67,3.74,21.8
tv_resnet34,6481.01,157.989,1024,224,3.67,3.74,21.8
tf_efficientnetv2_b1,6476.52,158.098,1024,240,1.21,7.34,8.14
semnasnet_140,6454.5,158.637,1024,224,0.6,8.87,6.11
nf_regnet_b0,6452.24,158.693,1024,256,0.64,5.58,8.76
ese_vovnet19b_dw,6335.13,161.627,1024,224,1.34,8.25,6.54
mobilenetv2_140,6271.56,163.266,1024,224,0.6,9.57,6.11
rexnet_100,6226.48,164.447,1024,224,0.41,7.44,4.8
efficientnet_lite1,6187.91,165.472,1024,240,0.62,10.14,5.42
efficientnet_es_pruned,6115.4,167.434,1024,224,1.81,8.73,5.44
efficientnet_es,6115.12,167.443,1024,224,1.81,8.73,5.44
visformer_tiny,6103.09,167.772,1024,224,1.27,5.72,10.32
seresnet34,6058.13,169.019,1024,224,3.67,3.74,21.96
fbnetv3_b,6018.76,170.124,1024,256,0.55,9.1,8.6
selecsls42,5953.76,171.98,1024,224,2.94,4.62,30.35
selecsls42b,5921.2,172.924,1024,224,2.98,4.62,32.46
resnet26,5895.21,173.69,1024,224,2.36,7.35,16.0
edgenext_xx_small,5893.72,173.732,1024,288,0.33,4.21,1.33
levit_384,5880.4,174.126,1024,224,2.36,6.26,39.13
resnet34d,5865.98,174.555,1024,224,3.91,4.54,21.82
legacy_seresnet34,5850.24,175.025,1024,224,3.67,3.74,21.96
dla34,5827.3,175.712,1024,224,3.07,5.02,15.74
tf_efficientnet_es,5781.29,177.112,1024,224,1.81,8.73,5.44
cs3darknet_focus_m,5721.39,178.967,1024,288,2.51,6.19,9.3
resnetblur18,5636.65,181.657,1024,224,2.34,3.39,11.69
rexnetr_130,5590.0,183.173,1024,224,0.68,9.81,7.61
mobilevit_xxs,5524.87,185.333,1024,256,0.42,8.34,1.27
tf_efficientnet_lite1,5524.68,185.339,1024,240,0.62,10.14,5.42
cs3darknet_m,5478.07,186.916,1024,288,2.63,6.69,9.31
convnext_atto,5460.54,187.516,1024,288,0.91,6.3,3.7
xcit_tiny_12_p16_224_dist,5457.72,187.611,1024,224,1.24,6.29,6.72
xcit_tiny_12_p16_224,5456.63,187.649,1024,224,1.24,6.29,6.72
skresnet18,5413.1,189.159,1024,224,1.82,3.24,11.96
darknet17,5401.37,189.571,1024,256,3.26,7.18,14.3
mixnet_s,5392.58,189.878,1024,224,0.25,6.25,4.13
resmlp_12_224,5366.15,190.814,1024,224,3.01,5.5,15.35
resmlp_12_distilled_224,5364.91,190.857,1024,224,3.01,5.5,15.35
convnext_atto_ols,5288.94,193.6,1024,288,0.96,6.8,3.7
vit_base_patch32_clip_224,5280.68,193.903,1024,224,4.41,5.01,88.22
vit_base_patch32_224,5280.52,193.908,1024,224,4.41,5.01,88.22
pit_xs_distilled_224,5272.13,194.218,1024,224,1.41,7.76,11.0
pit_xs_224,5271.0,194.259,1024,224,1.4,7.71,10.62
repvgg_b0,5252.66,194.939,1024,224,3.41,6.15,15.82
mixer_b32_224,5221.71,196.094,1024,224,3.24,6.29,60.29
pvt_v2_b0,5210.31,196.521,1024,224,0.57,7.99,3.67
resnetaa34d,5171.78,197.986,1024,224,4.43,5.07,21.82
selecsls60,5160.83,198.407,1024,224,3.59,5.52,30.67
selecsls60b,5119.51,200.008,1024,224,3.63,5.52,32.77
mobilenetv2_120d,5111.95,200.304,1024,224,0.69,11.97,5.83
resnet26d,5108.26,200.449,1024,224,2.6,8.15,16.01
gmixer_12_224,5064.97,202.162,1024,224,2.67,7.26,12.7
gmlp_ti16_224,5007.93,204.464,1024,224,1.34,7.55,5.87
mixer_s16_224,4998.69,204.842,1024,224,3.79,5.97,18.53
tf_mixnet_s,4989.18,205.231,1024,224,0.25,6.25,4.13
efficientnet_b0_g16_evos,4930.67,207.667,1024,224,1.01,7.42,8.11
rexnetr_150,4900.22,208.959,1024,224,0.89,11.13,9.78
fbnetv3_d,4881.14,209.776,1024,256,0.68,11.1,10.31
darknet21,4850.41,211.105,1024,256,3.93,7.47,20.86
nf_resnet26,4816.48,212.591,1024,224,2.41,7.35,16.0
efficientnet_lite2,4781.65,214.14,1024,260,0.89,12.9,6.09
convnext_femto,4749.12,215.607,1024,288,1.3,7.56,5.22
tf_efficientnetv2_b2,4718.26,217.018,1024,260,1.72,9.84,10.1
sedarknet21,4656.51,219.895,1024,256,3.93,7.47,20.95
dla46x_c,4636.77,220.831,1024,224,0.54,5.66,1.07
convnext_femto_ols,4618.33,221.714,1024,288,1.35,8.06,5.23
resnext26ts,4603.25,222.441,1024,256,2.43,10.52,10.3
efficientformer_l1,4566.14,224.248,1024,224,1.3,5.53,12.29
dpn48b,4506.78,227.201,1024,224,1.69,8.92,9.13
crossvit_tiny_240,4481.69,228.473,1024,240,1.57,9.08,7.01
dla60x_c,4459.27,229.622,1024,224,0.59,6.01,1.32
eca_resnext26ts,4456.63,229.759,1024,256,2.43,10.52,10.3
seresnext26ts,4453.99,229.896,1024,256,2.43,10.52,10.39
legacy_seresnext26_32x4d,4441.15,230.558,1024,224,2.49,9.39,16.79
gernet_l,4396.56,232.898,1024,256,4.57,8.0,31.08
mobilevitv2_075,4393.87,233.041,1024,256,1.05,12.06,2.87
gcresnext26ts,4384.92,233.516,1024,256,2.43,10.53,10.48
tf_efficientnet_b1,4370.6,234.282,1024,240,0.71,10.88,7.79
tf_efficientnet_lite2,4293.9,238.467,1024,260,0.89,12.9,6.09
rexnet_130,4262.16,240.243,1024,224,0.68,9.71,7.56
efficientnet_b1,4239.44,241.53,1024,256,0.77,12.22,7.79
vit_small_patch32_384,4239.1,241.55,1024,384,3.45,8.25,22.92
crossvit_9_240,4212.37,243.082,1024,240,1.85,9.52,8.55
crossvit_9_dagger_240,4095.03,250.049,1024,240,1.99,9.97,8.78
nf_ecaresnet26,4091.86,250.24,1024,224,2.41,7.36,16.0
nf_seresnet26,4088.47,250.449,1024,224,2.41,7.36,17.4
efficientnet_cc_b0_8e,4076.51,251.183,1024,224,0.42,9.42,24.01
efficientnet_cc_b0_4e,4073.3,251.382,1024,224,0.41,9.42,13.31
ecaresnet50d_pruned,4055.39,252.492,1024,224,2.53,6.43,19.94
efficientnet_b2_pruned,4030.92,254.025,1024,260,0.73,9.13,8.31
ecaresnext50t_32x4d,4018.73,254.796,1024,224,2.7,10.09,15.41
ecaresnext26t_32x4d,4017.09,254.9,1024,224,2.7,10.09,15.41
seresnext26t_32x4d,4014.43,255.069,1024,224,2.7,10.09,16.81
seresnext26tn_32x4d,4014.36,255.074,1024,224,2.7,10.09,16.81
repvgg_a2,3987.84,256.77,1024,224,5.7,6.26,28.21
poolformer_s12,3982.67,257.103,1024,224,1.82,5.53,11.92
seresnext26d_32x4d,3979.57,257.303,1024,224,2.73,10.19,16.81
vit_tiny_r_s16_p8_384,3963.05,258.374,1024,384,1.34,6.49,6.36
resnet26t,3939.46,259.923,1024,256,3.35,10.52,16.01
nf_regnet_b1,3911.64,261.772,1024,288,1.02,9.2,10.22
rexnet_150,3881.93,263.775,1024,224,0.9,11.21,9.73
nf_regnet_b2,3879.78,263.921,1024,272,1.22,9.27,14.31
resnetv2_50,3865.49,264.896,1024,224,4.11,11.11,25.55
regnetx_016,3852.41,265.794,1024,224,1.62,7.93,9.19
tf_efficientnet_cc_b0_4e,3812.08,268.608,1024,224,0.41,9.42,13.31
tf_efficientnet_cc_b0_8e,3803.67,269.202,1024,224,0.42,9.42,24.01
convnext_pico,3747.49,273.239,1024,288,2.27,10.08,9.05
ecaresnetlight,3744.45,273.459,1024,224,4.11,8.42,30.16
dpn68,3724.59,274.917,1024,224,2.35,10.47,12.61
edgenext_x_small,3714.71,275.646,1024,288,0.68,7.5,2.34
gluon_resnet50_v1b,3672.76,278.798,1024,224,4.11,11.11,25.56
ssl_resnet50,3671.85,278.866,1024,224,4.11,11.11,25.56
efficientnet_em,3671.25,278.913,1024,240,3.04,14.34,6.9
resnet50,3668.58,279.116,1024,224,4.11,11.11,25.56
swsl_resnet50,3668.32,279.136,1024,224,4.11,11.11,25.56
tv_resnet50,3667.14,279.225,1024,224,4.11,11.11,25.56
dpn68b,3667.07,279.229,1024,224,2.35,10.47,12.61
rexnetr_200,3659.45,279.811,1024,224,1.59,15.11,16.52
convnext_pico_ols,3651.34,280.434,1024,288,2.37,10.74,9.06
botnet26t_256,3594.28,284.883,1024,256,3.32,11.98,12.49
bat_resnext26ts,3569.91,286.828,1024,256,2.53,12.51,10.73
resnetv2_50t,3547.32,288.657,1024,224,4.32,11.82,25.57
mixnet_m,3537.26,289.477,1024,224,0.36,8.19,5.01
regnety_016,3531.88,289.919,1024,224,1.63,8.04,11.2
tf_efficientnet_em,3529.62,290.106,1024,240,3.04,14.34,6.9
resnetv2_50d,3525.02,290.482,1024,224,4.35,11.92,25.57
halonet26t,3515.15,291.299,1024,256,3.19,11.69,12.48
resnet32ts,3492.62,293.179,1024,256,4.63,11.58,17.96
hrnet_w18_small_v2,3482.81,294.001,1024,224,2.62,9.65,15.6
gluon_resnet50_v1c,3481.59,294.107,1024,224,4.35,11.92,25.58
dla60,3466.91,295.351,1024,224,4.26,10.16,22.04
resnet33ts,3460.78,295.875,1024,256,4.76,11.66,19.68
tf_efficientnet_b2,3402.3,300.962,1024,260,1.02,13.83,9.11
convit_tiny,3399.61,301.199,1024,224,1.26,7.94,5.71
resnet50t,3373.72,303.51,1024,224,4.32,11.82,25.57
tf_mixnet_m,3366.38,304.167,1024,224,0.36,8.19,5.01
efficientnet_b3_pruned,3360.1,304.74,1024,300,1.04,11.86,9.86
seresnet33ts,3354.27,305.27,1024,256,4.76,11.66,19.78
resnet50d,3351.47,305.527,1024,224,4.35,11.92,25.58
eca_resnet33ts,3350.95,305.574,1024,256,4.76,11.66,19.68
vit_small_resnet26d_224,3346.77,305.954,1024,224,5.07,11.12,63.61
cs3darknet_focus_l,3335.18,307.018,1024,288,5.9,10.16,21.15
gluon_resnet50_v1d,3334.65,307.068,1024,224,4.35,11.92,25.58
mobilevitv2_100,3324.63,307.994,1024,256,1.84,16.08,4.9
vovnet39a,3320.12,308.408,1024,224,7.09,6.73,22.6
legacy_seresnet50,3312.33,309.135,1024,224,3.88,10.6,28.09
efficientnet_b0_gn,3307.86,309.554,1024,224,0.42,6.75,5.29
gcresnet33ts,3307.01,309.633,1024,256,4.76,11.68,19.88
pit_s_distilled_224,3301.25,310.173,1024,224,2.9,11.64,24.04
pit_s_224,3299.97,310.295,1024,224,2.88,11.56,23.46
mobilevit_xs,3252.28,314.844,1024,256,1.05,16.33,2.32
deit_small_distilled_patch16_224,3233.6,316.663,1024,224,4.63,12.02,22.44
efficientnet_b2a,3223.97,317.608,1024,288,1.12,16.2,9.11
efficientnet_b2,3223.9,317.615,1024,288,1.12,16.2,9.11
deit_small_patch16_224,3218.99,318.1,1024,224,4.61,11.95,22.05
vit_small_patch16_224,3218.38,318.16,1024,224,4.61,11.95,22.05
cs3darknet_l,3210.26,318.965,1024,288,6.16,10.83,21.16
ese_vovnet39b,3206.21,319.369,1024,224,7.09,6.74,24.57
eca_vovnet39b,3203.77,319.612,1024,224,7.09,6.74,22.6
convnextv2_atto,3196.73,320.315,1024,288,0.91,6.3,3.71
coatnet_pico_rw_224,3189.82,321.008,1024,224,2.05,14.62,10.85
seresnet50,3181.57,321.841,1024,224,4.11,11.13,28.09
pvt_v2_b1,3147.37,325.339,1024,224,2.12,15.39,14.01
coat_lite_tiny,3146.41,325.439,1024,224,1.6,11.65,5.72
res2net50_48w_2s,3127.52,327.404,1024,224,4.18,11.72,25.29
eca_botnext26ts_256,3112.32,329.003,1024,256,2.46,11.6,10.59
ecaresnet101d_pruned,3103.16,329.973,1024,224,3.48,7.69,24.88
efficientnet_b0_g8_gn,3073.2,333.192,1024,224,0.66,6.75,6.56
ssl_resnext50_32x4d,3071.68,333.356,1024,224,4.26,14.4,25.03
dla60x,3071.64,333.359,1024,224,3.54,13.8,17.35
swsl_resnext50_32x4d,3070.7,333.464,1024,224,4.26,14.4,25.03
tv_resnext50_32x4d,3069.81,333.56,1024,224,4.26,14.4,25.03
resnext50_32x4d,3069.72,333.57,1024,224,4.26,14.4,25.03
gluon_resnext50_32x4d,3068.47,333.704,1024,224,4.26,14.4,25.03
vit_small_r26_s32_224,3061.92,334.417,1024,224,3.56,9.85,36.43
skresnet34,3055.95,335.073,1024,224,3.67,5.13,22.28
deit3_small_patch16_224_in21ft1k,3048.82,335.855,1024,224,4.61,11.95,22.06
deit3_small_patch16_224,3047.23,336.031,1024,224,4.61,11.95,22.06
eca_halonext26ts,3035.71,337.305,1024,256,2.44,11.46,10.76
haloregnetz_b,3032.47,337.665,1024,224,1.97,11.94,11.68
vit_relpos_base_patch32_plus_rpn_256,3026.45,338.338,1024,256,7.68,8.01,119.42
vit_relpos_small_patch16_rpn_224,3019.95,339.067,1024,224,4.59,13.05,21.97
vit_relpos_small_patch16_224,3008.26,340.383,1024,224,4.59,13.05,21.98
vit_srelpos_small_patch16_224,3000.96,341.213,1024,224,4.59,12.16,21.97
xcit_nano_12_p16_384_dist,3000.48,341.266,1024,384,1.64,12.15,3.05
cs3sedarknet_l,2995.41,341.845,1024,288,6.16,10.83,21.91
resnetaa50d,2993.03,342.116,1024,224,5.39,12.44,25.58
vgg11,2983.47,85.796,256,224,7.61,7.44,132.86
selecsls84,2973.16,344.402,1024,224,5.9,7.57,50.95
resnetrs50,2963.42,345.535,1024,224,4.48,12.14,35.69
seresnet50t,2957.12,346.271,1024,224,4.32,11.83,28.1
resnest14d,2954.69,346.556,1024,224,2.76,7.33,10.61
gluon_resnet50_v1s,2953.65,346.677,1024,224,5.47,13.52,25.68
coat_lite_mini,2952.61,346.799,1024,224,2.0,12.25,11.01
ecaresnet50d,2945.96,347.583,1024,224,4.35,11.93,25.58
densenet121,2933.45,349.064,1024,224,2.87,6.9,7.98
tv_densenet121,2929.69,349.514,1024,224,2.87,6.9,7.98
vit_base_patch32_plus_256,2929.65,349.519,1024,256,7.79,7.76,119.48
rexnet_200,2927.94,349.723,1024,224,1.56,14.91,16.37
xcit_tiny_24_p16_224_dist,2927.0,349.834,1024,224,2.34,11.82,12.12
xcit_tiny_24_p16_224,2921.97,350.436,1024,224,2.34,11.82,12.12
coatnet_nano_cc_224,2867.38,357.108,1024,224,2.24,15.02,13.76
gcresnext50ts,2857.34,358.363,1024,256,3.75,15.46,15.67
lambda_resnet26rpt_256,2853.55,358.839,1024,256,3.16,11.87,10.99
resnext50d_32x4d,2845.08,359.908,1024,224,4.5,15.2,25.05
mixnet_l,2828.6,361.996,1024,224,0.58,10.84,7.33
densenet121d,2824.08,362.584,1024,224,3.11,7.7,8.0
efficientnet_lite3,2821.84,362.87,1024,300,1.65,21.85,8.2
cspresnet50,2793.65,366.534,1024,256,4.54,11.5,21.62
coatnet_nano_rw_224,2781.93,368.077,1024,224,2.41,15.41,15.14
vgg11_bn,2760.38,370.949,1024,224,7.62,7.44,132.87
vovnet57a,2755.77,371.572,1024,224,8.95,7.52,36.64
resmlp_24_224,2750.33,372.306,1024,224,5.96,10.91,30.02
resmlp_24_distilled_224,2740.33,373.665,1024,224,5.96,10.91,30.02
convnextv2_femto,2735.91,374.269,1024,288,1.3,7.56,5.23
flexivit_small,2735.78,374.287,1024,240,5.35,14.18,22.06
gcresnet50t,2732.04,374.8,1024,256,5.42,14.67,25.9
legacy_seresnext50_32x4d,2722.84,376.065,1024,224,4.26,14.42,27.56
seresnext50_32x4d,2721.47,376.256,1024,224,4.26,14.42,27.56
gluon_seresnext50_32x4d,2720.58,376.379,1024,224,4.26,14.42,27.56
visformer_small,2719.93,376.468,1024,224,4.88,11.43,40.22
twins_svt_small,2713.39,377.374,1024,224,2.94,13.75,24.06
resnetv2_50x1_bit_distilled,2708.81,378.014,1024,224,4.23,11.11,25.55
res2net50_14w_8s,2692.9,380.248,1024,224,4.21,13.28,25.06
resnetblur50,2685.97,381.228,1024,224,5.16,12.02,25.56
vit_base_resnet26d_224,2684.6,381.421,1024,224,6.97,13.16,101.4
tf_mixnet_l,2680.8,381.958,1024,224,0.58,10.84,7.33
seresnetaa50d,2658.93,385.106,1024,224,5.4,12.46,28.11
dla60_res2net,2656.16,385.506,1024,224,4.15,12.34,20.85
cspresnet50d,2655.05,385.668,1024,256,4.86,12.55,21.64
coatnext_nano_rw_224,2655.0,385.674,1024,224,2.47,12.8,14.7
ese_vovnet57b,2654.33,385.773,1024,224,8.95,7.52,38.61
tf_efficientnetv2_b3,2654.14,385.8,1024,300,3.04,15.74,14.36
cspresnet50w,2641.68,387.621,1024,256,5.04,12.19,28.12
res2net50_26w_4s,2629.64,389.395,1024,224,4.28,12.61,25.7
regnetz_b16,2626.71,389.828,1024,288,2.39,16.43,9.72
convnext_nano,2611.78,392.059,1024,288,4.06,13.84,15.59
efficientnetv2_rw_t,2601.49,393.609,1024,288,3.19,16.42,13.65
fbnetv3_g,2595.29,394.549,1024,288,1.77,21.09,16.62
gmixer_24_224,2595.15,394.571,1024,224,5.28,14.45,24.72
mobilevit_s,2586.09,395.952,1024,256,2.03,19.94,5.58
coatnet_rmlp_nano_rw_224,2569.7,398.478,1024,224,2.62,20.34,15.15
gcvit_xxtiny,2561.41,399.768,1024,224,2.14,15.36,12.0
tf_efficientnet_lite3,2530.94,404.582,1024,300,1.65,21.85,8.2
efficientnet_cc_b1_8e,2530.65,404.628,1024,240,0.75,15.44,39.72
densenetblur121d,2522.66,405.908,1024,224,3.11,7.9,8.0
resnetblur50d,2509.45,408.045,1024,224,5.4,12.82,25.58
nf_ecaresnet50,2490.39,411.168,1024,224,4.21,11.13,25.56
inception_v3,2485.21,412.025,1024,299,5.73,8.97,23.83
nf_seresnet50,2482.66,412.449,1024,224,4.21,11.13,28.09
tf_inception_v3,2481.38,412.658,1024,299,5.73,8.97,23.83
gc_efficientnetv2_rw_t,2480.59,412.793,1024,288,3.2,16.45,13.68
adv_inception_v3,2479.41,412.983,1024,299,5.73,8.97,23.83
repvgg_b1g4,2473.34,414.003,1024,224,8.15,10.64,39.97
mobilevitv2_125,2472.28,414.18,1024,256,2.86,20.1,7.48
gluon_inception_v3,2468.42,414.827,1024,299,5.73,8.97,23.83
nf_regnet_b3,2461.52,415.991,1024,320,2.05,14.61,18.59
xcit_small_12_p16_224_dist,2446.89,418.478,1024,224,4.82,12.58,26.25
xcit_small_12_p16_224,2446.42,418.558,1024,224,4.82,12.58,26.25
cspresnext50,2438.96,419.836,1024,256,4.05,15.86,20.57
convnext_nano_ols,2435.0,420.521,1024,288,4.38,15.5,15.65
regnetx_032,2429.42,421.489,1024,224,3.2,11.37,15.3
densenet169,2426.29,422.031,1024,224,3.4,7.3,14.15
sehalonet33ts,2419.4,423.234,1024,256,3.55,14.7,13.69
tf_efficientnet_cc_b1_8e,2406.19,425.557,1024,240,0.75,15.44,39.72
semobilevit_s,2402.02,426.294,1024,256,2.03,19.95,5.74
resnetv2_101,2330.6,439.36,1024,224,7.83,16.23,44.54
twins_pcpvt_small,2312.72,442.754,1024,224,3.83,18.08,24.11
xcit_nano_12_p8_224_dist,2295.5,446.077,1024,224,2.16,15.71,3.05
xcit_nano_12_p8_224,2292.87,446.587,1024,224,2.16,15.71,3.05
gmlp_s16_224,2290.73,447.007,1024,224,4.42,15.1,19.42
cs3darknet_focus_x,2287.2,447.697,1024,256,8.03,10.69,35.02
vit_base_r26_s32_224,2275.25,450.047,1024,224,6.81,12.36,101.38
gluon_resnet101_v1b,2260.37,453.01,1024,224,7.83,16.23,44.55
tv_resnet101,2258.59,453.368,1024,224,7.83,16.23,44.55
resnet101,2258.28,453.43,1024,224,7.83,16.23,44.55
skresnet50,2234.62,458.23,1024,224,4.11,12.5,25.8
ecaresnet26t,2232.29,458.709,1024,320,5.24,16.44,16.01
edgenext_small,2226.69,459.86,1024,320,1.97,14.16,5.59
dla102,2219.96,461.255,1024,224,7.19,14.18,33.27
res2next50,2214.71,462.347,1024,224,4.2,13.71,24.67
dla60_res2next,2210.67,463.194,1024,224,3.49,13.17,17.03
resnetv2_101d,2203.82,464.633,1024,224,8.07,17.04,44.56
gluon_resnet101_v1c,2194.65,466.578,1024,224,8.08,17.04,44.57
resnest26d,2170.04,471.869,1024,224,3.64,9.97,17.07
vgg13,2149.71,476.331,1024,224,11.31,12.25,133.05
gluon_resnet101_v1d,2137.49,479.053,1024,224,8.08,17.04,44.57
skresnet50d,2115.22,484.098,1024,224,4.36,13.31,25.82
convnextv2_pico,2108.5,485.64,1024,288,2.27,10.08,9.07
vit_base_resnet50d_224,2101.17,487.333,1024,224,8.73,16.92,110.97
coatnet_0_rw_224,2082.49,491.706,1024,224,4.43,18.73,27.44
crossvit_small_240,2081.5,491.94,1024,240,5.63,18.17,26.86
deit3_medium_patch16_224_in21ft1k,2076.53,493.118,1024,224,8.0,15.93,38.85
deit3_medium_patch16_224,2072.34,494.116,1024,224,8.0,15.93,38.85
mobilevitv2_150,2071.36,494.349,1024,256,4.09,24.11,10.59
mobilevitv2_150_in22ft1k,2070.3,494.603,1024,256,4.09,24.11,10.59
sebotnet33ts_256,2067.91,247.581,512,256,3.89,17.46,13.7
wide_resnet50_2,2057.08,497.78,1024,224,11.43,14.4,68.88
vit_relpos_medium_patch16_rpn_224,2044.85,500.757,1024,224,7.97,17.02,38.73
efficientformer_l3,2041.79,501.507,1024,224,3.93,12.01,31.41
poolformer_s24,2040.35,501.863,1024,224,3.41,10.68,21.39
vit_relpos_medium_patch16_224,2037.47,502.572,1024,224,7.97,17.02,38.75
cspdarknet53,2035.94,502.949,1024,256,6.57,16.81,27.64
resnet51q,2034.41,503.329,1024,288,8.07,20.94,35.7
vit_srelpos_medium_patch16_224,2033.15,503.638,1024,224,7.96,16.21,38.74
maxvit_rmlp_pico_rw_256,2008.78,509.748,1024,256,1.85,24.86,7.52
vit_relpos_medium_patch16_cls_224,2007.24,510.141,1024,224,8.03,18.24,38.76
dla102x,2006.55,510.315,1024,224,5.89,19.42,26.31
legacy_seresnet101,2003.12,511.188,1024,224,7.61,15.74,49.33
swin_tiny_patch4_window7_224,1995.14,513.235,1024,224,4.51,17.06,28.29
repvgg_b1,1985.42,515.747,1024,224,13.16,10.64,57.42
resnetaa101d,1982.98,516.381,1024,224,9.12,17.56,44.57
coatnet_rmlp_0_rw_224,1981.75,516.703,1024,224,4.72,24.89,27.45
tf_efficientnet_b3,1975.92,518.226,1024,300,1.87,23.83,12.23
gcvit_xtiny,1969.68,519.869,1024,224,2.93,20.26,19.98
hrnet_w18,1967.17,520.531,1024,224,4.32,16.31,21.3
gluon_resnet101_v1s,1965.68,520.926,1024,224,9.19,18.64,44.67
maxvit_pico_rw_256,1965.38,521.006,1024,256,1.83,22.3,7.46
resnetaa50,1958.15,522.93,1024,288,8.52,19.24,25.56
seresnet101,1954.63,523.871,1024,224,7.84,16.27,49.33
efficientnet_b3,1949.54,525.239,1024,320,2.01,26.52,12.23
efficientnet_b3a,1949.11,525.356,1024,320,2.01,26.52,12.23
edgenext_small_rw,1932.68,529.816,1024,320,2.46,14.85,7.83
regnetx_040,1932.62,529.839,1024,224,3.99,12.2,22.12
cs3sedarknet_xdw,1925.4,531.825,1024,256,5.97,17.18,21.6
coatnet_bn_0_rw_224,1920.71,533.123,1024,224,4.67,22.04,27.44
xcit_tiny_12_p16_384_dist,1911.65,535.652,1024,384,3.64,18.26,6.72
ssl_resnext101_32x4d,1910.73,535.909,1024,224,8.01,21.23,44.18
swsl_resnext101_32x4d,1910.43,535.993,1024,224,8.01,21.23,44.18
resnext101_32x4d,1909.99,536.115,1024,224,8.01,21.23,44.18
gluon_resnext101_32x4d,1909.34,536.298,1024,224,8.01,21.23,44.18
darknet53,1903.77,537.866,1024,288,11.78,15.68,41.61
darknetaa53,1898.12,539.468,1024,288,10.08,15.68,36.02
crossvit_15_240,1892.46,541.083,1024,240,5.81,19.77,27.53
halonet50ts,1881.53,544.226,1024,256,5.3,19.2,22.73
vgg13_bn,1879.72,544.749,1024,224,11.33,12.25,133.05
mixnet_xl,1872.46,546.86,1024,224,0.93,14.57,11.9
res2net50_26w_6s,1870.88,547.321,1024,224,6.33,15.28,37.05
ecaresnet101d,1869.88,547.616,1024,224,8.08,17.07,44.57
densenet201,1869.57,547.706,1024,224,4.34,7.85,20.01
nf_resnet101,1858.48,550.976,1024,224,8.01,16.23,44.55
coatnet_0_224,1857.28,275.661,512,224,4.58,24.01,25.04
pvt_v2_b2,1854.85,552.053,1024,224,4.05,27.53,25.36
crossvit_15_dagger_240,1850.69,553.295,1024,240,6.13,20.43,28.21
resmlp_36_224,1846.41,554.574,1024,224,8.91,16.33,44.69
resmlp_36_distilled_224,1845.04,554.99,1024,224,8.91,16.33,44.69
resnet61q,1841.84,555.954,1024,288,9.87,21.52,36.85
swin_s3_tiny_224,1817.5,563.398,1024,224,4.64,19.13,28.33
cait_xxs24_224,1796.55,569.968,1024,224,2.53,20.29,11.96
cs3darknet_x,1789.33,572.268,1024,288,10.6,14.36,35.05
vit_medium_patch16_gap_240,1785.54,573.481,1024,240,9.22,18.81,44.4
nf_resnet50,1784.84,573.708,1024,288,6.88,18.37,25.56
resnet50_gn,1764.31,580.385,1024,224,4.14,11.11,25.56
mixer_b16_224_miil,1761.45,581.327,1024,224,12.62,14.53,59.88
mixer_b16_224,1759.76,581.885,1024,224,12.62,14.53,59.88
resnetblur101d,1757.96,582.482,1024,224,9.12,17.94,44.57
eca_nfnet_l0,1726.58,593.068,1024,288,7.12,17.29,24.14
nfnet_l0,1721.83,594.705,1024,288,7.13,17.29,35.07
vit_large_patch32_224,1717.59,596.169,1024,224,15.41,13.32,327.9
vgg16,1717.44,596.224,1024,224,15.47,13.56,138.36
regnetz_c16,1710.89,598.505,1024,320,3.92,25.88,13.46
pvt_v2_b2_li,1709.89,598.855,1024,224,3.91,27.6,22.55
resnest50d_1s4x24d,1705.52,600.391,1024,224,4.43,13.57,25.68
coat_lite_small,1704.55,600.733,1024,224,3.96,22.09,19.84
resnetv2_50d_frn,1697.1,603.368,1024,224,4.33,11.92,25.59
cs3sedarknet_x,1689.8,605.975,1024,288,10.6,14.37,35.4
seresnext101_32x4d,1687.65,606.747,1024,224,8.02,21.26,48.96
gluon_seresnext101_32x4d,1687.1,606.945,1024,224,8.02,21.26,48.96
legacy_seresnext101_32x4d,1684.69,607.813,1024,224,8.02,21.26,48.96
regnetv_040,1682.92,608.454,1024,288,6.6,20.3,20.64
mobilevitv2_175,1677.66,457.769,768,256,5.54,28.13,14.25
regnety_040,1677.03,610.59,1024,288,6.61,20.3,20.65
mobilevitv2_175_in22ft1k,1677.0,457.949,768,256,5.54,28.13,14.25
convnext_tiny_hnf,1676.16,610.908,1024,288,7.39,22.21,28.59
res2net101_26w_4s,1675.37,611.195,1024,224,8.1,18.45,45.21
vit_tiny_patch16_384,1665.76,614.72,1024,384,4.7,25.39,5.79
sequencer2d_s,1661.32,616.362,1024,224,4.96,11.31,27.65
ese_vovnet39b_evos,1661.21,616.404,1024,224,7.07,6.74,24.58
vit_base_patch32_384,1649.27,620.868,1024,384,13.06,16.5,88.3
vit_base_patch32_clip_384,1648.64,621.105,1024,384,13.06,16.5,88.3
mixer_l32_224,1645.23,622.393,1024,224,11.27,19.86,206.94
convnext_tiny,1642.14,623.562,1024,288,7.39,22.21,28.59
botnet50ts_256,1639.64,312.25,512,256,5.54,22.23,22.74
swinv2_cr_tiny_224,1630.02,628.199,1024,224,4.66,28.45,28.33
resnetv2_50d_evob,1627.44,629.196,1024,224,4.33,11.92,25.59
twins_pcpvt_base,1615.12,633.996,1024,224,6.68,25.25,43.83
resnetv2_152,1614.43,634.268,1024,224,11.55,22.56,60.19
hrnet_w32,1605.06,637.96,1024,224,8.97,22.02,41.23
swinv2_cr_tiny_ns_224,1600.43,639.811,1024,224,4.66,28.45,28.33
xception41p,1598.79,480.351,768,299,9.25,39.86,26.91
tv_resnet152,1582.54,647.049,1024,224,11.56,22.56,60.19
gluon_resnet152_v1b,1581.57,647.444,1024,224,11.56,22.56,60.19
resnet152,1581.02,647.671,1024,224,11.56,22.56,60.19
xception,1579.88,648.138,1024,299,8.4,35.83,22.86
halo2botnet50ts_256,1572.75,651.076,1024,256,5.02,21.78,22.64
res2net50_26w_8s,1568.85,652.695,1024,224,8.37,17.95,48.4
vit_medium_patch16_gap_256,1564.22,654.626,1024,256,10.59,22.15,38.86
resnetv2_152d,1557.03,657.648,1024,224,11.8,23.36,60.2
efficientnet_el_pruned,1555.14,658.449,1024,300,8.0,30.7,10.59
maxvit_rmlp_nano_rw_256,1551.85,659.845,1024,256,4.47,31.92,15.5
regnetx_064,1550.52,660.413,1024,224,6.49,16.37,26.21
efficientnet_el,1549.97,660.646,1024,300,8.0,30.7,10.59
gluon_resnet152_v1c,1548.96,661.078,1024,224,11.8,23.36,60.21
nf_ecaresnet101,1546.58,662.091,1024,224,8.01,16.27,44.55
nf_seresnet101,1539.38,665.191,1024,224,8.02,16.27,49.33
mvitv2_tiny,1537.54,665.985,1024,224,4.7,21.16,24.17
nfnet_f0,1525.01,671.456,1024,256,12.62,18.05,71.49
vgg16_bn,1523.86,671.963,1024,224,15.5,13.56,138.37
cs3edgenet_x,1521.21,673.136,1024,288,14.59,16.36,47.82
gluon_resnet152_v1d,1520.11,673.621,1024,224,11.8,23.36,60.21
maxvit_nano_rw_256,1517.43,674.812,1024,256,4.46,30.28,15.45
tf_efficientnet_el,1506.16,679.862,1024,300,8.0,30.7,10.59
convnextv2_nano,1500.71,511.746,768,288,4.06,13.84,15.62
resnest50d,1492.63,686.022,1024,224,5.4,14.36,27.48
ese_vovnet99b,1489.17,687.617,1024,224,16.51,11.27,63.2
dla169,1471.11,696.059,1024,224,11.6,20.2,53.39
regnety_032,1467.85,697.604,1024,288,5.29,18.61,19.44
skresnext50_32x4d,1463.28,699.785,1024,224,4.5,17.18,27.48
xcit_tiny_12_p8_224_dist,1458.7,701.981,1024,224,4.81,23.6,6.71
xcit_tiny_12_p8_224,1458.23,702.211,1024,224,4.81,23.6,6.71
convit_small,1457.54,702.541,1024,224,5.76,17.87,27.78
mobilevitv2_200_in22ft1k,1456.59,527.247,768,256,7.22,32.15,18.45
mobilevitv2_200,1456.02,527.451,768,256,7.22,32.15,18.45
ecaresnet50t,1438.32,711.929,1024,320,8.82,24.13,25.57
gluon_resnet152_v1s,1432.22,714.961,1024,224,12.92,24.96,60.32
nest_tiny,1415.33,542.618,768,224,5.83,25.48,17.06
regnety_040s_gn,1412.65,724.867,1024,224,4.03,12.29,20.65
vgg19,1393.71,183.67,256,224,19.63,14.86,143.67
jx_nest_tiny,1389.62,552.657,768,224,5.83,25.48,17.06
legacy_seresnet152,1383.83,739.96,1024,224,11.33,22.08,66.82
densenet161,1376.52,743.891,1024,224,7.79,11.06,28.68
poolformer_s36,1370.67,747.069,1024,224,5.0,15.82,30.86
vit_small_resnet50d_s16_224,1367.59,748.748,1024,224,13.48,24.82,57.53
twins_svt_base,1362.65,751.463,1024,224,8.59,26.33,56.07
seresnet152,1361.7,751.99,1024,224,11.57,22.61,66.82
xception41,1356.44,566.173,768,299,9.28,39.86,26.97
maxvit_tiny_rw_224,1350.45,758.254,1024,224,5.11,33.11,29.06
crossvit_18_240,1348.85,759.154,1024,240,9.05,26.26,43.27
maxxvit_rmlp_nano_rw_256,1347.73,759.767,1024,256,4.37,26.05,16.78
efficientnet_lite4,1343.74,571.528,768,380,4.04,45.66,13.01
gcvit_tiny,1339.65,764.364,1024,224,4.79,29.82,28.22
pvt_v2_b3,1325.92,772.282,1024,224,6.92,37.7,45.24
crossvit_18_dagger_240,1313.78,779.419,1024,240,9.5,27.03,44.27
volo_d1_224,1312.37,780.255,1024,224,6.94,24.43,26.63
xcit_small_24_p16_224_dist,1307.3,783.278,1024,224,9.1,23.64,47.67
tresnet_m,1305.71,784.234,1024,224,5.74,7.31,31.39
inception_v4,1305.41,784.412,1024,299,12.28,15.09,42.68
repvgg_b2,1305.22,784.529,1024,224,20.45,12.9,89.02
xcit_small_24_p16_224,1303.71,785.433,1024,224,9.1,23.64,47.67
sequencer2d_m,1295.72,790.281,1024,224,6.55,14.26,38.31
edgenext_base,1283.77,797.633,1024,320,6.01,24.32,18.51
hrnet_w30,1280.53,799.653,1024,224,8.15,21.21,37.71
dm_nfnet_f0,1275.46,802.834,1024,256,12.62,18.05,71.49
coatnet_rmlp_1_rw_224,1268.37,807.322,1024,224,7.85,35.47,41.69
maxxvitv2_nano_rw_256,1259.7,812.877,1024,256,6.26,23.05,23.7
efficientnetv2_s,1254.49,816.255,1024,384,8.44,35.77,21.46
vgg19_bn,1246.52,205.36,256,224,19.66,14.86,143.68
nf_regnet_b4,1235.79,828.604,1024,384,4.7,28.61,30.21
swin_small_patch4_window7_224,1235.74,828.641,1024,224,8.77,27.47,49.61
tf_efficientnet_lite4,1232.22,623.25,768,380,4.04,45.66,13.01
regnetz_d32,1223.51,836.919,1024,320,9.33,37.08,27.58
mixnet_xxl,1219.27,629.871,768,224,2.04,23.43,23.96
tf_efficientnetv2_s,1219.16,839.906,1024,384,8.44,35.77,21.46
deit_base_patch16_224,1213.08,844.121,1024,224,17.58,23.9,86.57
deit_base_distilled_patch16_224,1212.98,844.19,1024,224,17.68,24.05,87.34
vit_base_patch16_clip_224,1211.82,844.996,1024,224,17.58,23.9,86.57
vit_base_patch16_224_miil,1211.26,845.389,1024,224,17.59,23.91,94.4
dpn92,1210.45,845.948,1024,224,6.54,18.21,37.67
vit_base_patch16_224,1210.28,846.074,1024,224,17.58,23.9,86.57
coatnet_rmlp_1_rw2_224,1208.65,847.215,1024,224,8.11,40.13,41.72
cait_xxs36_224,1205.51,849.419,1024,224,3.77,30.34,17.3
maxvit_tiny_tf_224,1200.3,639.828,768,224,5.6,35.78,30.92
swinv2_tiny_window8_256,1200.06,853.274,1024,256,5.96,24.57,28.35
efficientnetv2_rw_s,1199.87,853.413,1024,384,8.72,38.03,23.94
dla102x2,1198.52,854.374,1024,224,9.34,29.91,41.28
regnetx_160,1195.08,856.833,1024,224,15.99,25.52,54.28
dpn98,1183.92,864.908,1024,224,11.73,25.2,61.57
vit_base_patch16_rpn_224,1180.39,867.498,1024,224,17.49,23.75,86.54
twins_pcpvt_large,1168.64,876.22,1024,224,9.84,35.82,60.99
deit3_base_patch16_224,1164.77,879.134,1024,224,17.58,23.9,86.59
deit3_base_patch16_224_in21ft1k,1164.5,879.334,1024,224,17.58,23.9,86.59
regnetz_d8,1163.64,879.982,1024,320,6.19,37.08,23.37
swsl_resnext101_32x8d,1158.15,884.156,1024,224,16.48,31.21,88.79
resnext101_32x8d,1158.05,884.232,1024,224,16.48,31.21,88.79
ssl_resnext101_32x8d,1158.02,884.255,1024,224,16.48,31.21,88.79
wide_resnet101_2,1157.66,884.531,1024,224,22.8,21.23,126.89
ig_resnext101_32x8d,1157.3,884.8,1024,224,16.48,31.21,88.79
coatnet_1_rw_224,1155.72,886.014,1024,224,8.04,34.6,41.72
vit_base_patch16_gap_224,1154.73,886.777,1024,224,17.49,25.59,86.57
vit_base_patch32_clip_448,1154.21,887.173,1024,448,17.93,23.9,88.34
resnet200,1149.71,890.646,1024,224,15.07,32.19,64.67
mvitv2_small,1146.92,892.812,1024,224,7.0,28.08,34.87
xception65p,1145.07,670.686,768,299,13.91,52.48,39.82
cs3se_edgenet_x,1143.17,895.738,1024,320,18.01,20.21,50.72
vit_relpos_base_patch16_rpn_224,1143.15,895.76,1024,224,17.51,24.97,86.41
vit_relpos_base_patch16_224,1141.31,897.204,1024,224,17.51,24.97,86.43
tnt_s_patch16_224,1135.32,901.935,1024,224,5.24,24.37,23.76
resnetrs101,1134.67,902.454,1024,288,13.56,28.53,63.62
vit_relpos_base_patch16_clsgap_224,1128.94,907.03,1024,224,17.6,25.12,86.43
vit_relpos_base_patch16_cls_224,1126.78,908.771,1024,224,17.6,25.12,86.43
inception_resnet_v2,1126.73,908.809,1024,299,13.18,25.06,55.84
ens_adv_inception_resnet_v2,1125.41,909.877,1024,299,13.18,25.06,55.84
beit_base_patch16_224,1112.26,920.631,1024,224,17.58,23.9,86.53
coat_tiny,1108.72,923.572,1024,224,4.35,27.2,5.5
beitv2_base_patch16_224,1108.55,923.711,1024,224,17.58,23.9,86.53
mvitv2_small_cls,1101.66,929.491,1024,224,7.04,28.17,34.87
resnetv2_50d_gn,1092.35,937.413,1024,288,7.24,19.7,25.57
pit_b_distilled_224,1078.48,474.731,512,224,12.5,33.07,74.79
pit_b_224,1075.34,476.117,512,224,12.42,32.94,73.76
hrnet_w40,1059.78,966.217,1024,224,12.75,25.29,57.56
coatnet_1_224,1045.17,489.859,512,224,8.7,39.0,42.23
resnet101d,1039.88,984.712,1024,320,16.48,34.77,44.57
flexivit_base,1037.21,987.248,1024,240,20.29,28.36,86.59
gluon_resnext101_64x4d,1034.86,989.491,1024,224,15.52,31.21,83.46
vit_small_patch16_36x1_224,1033.13,991.146,1024,224,13.71,35.69,64.67
vit_large_r50_s32_224,1030.67,993.517,1024,224,19.58,24.41,328.99
maxvit_rmlp_tiny_rw_256,1029.25,746.162,768,256,6.77,46.92,29.15
xcit_tiny_24_p16_384_dist,1027.64,996.444,1024,384,6.87,34.29,12.12
efficientnet_b4,1014.08,504.879,512,384,4.51,50.04,19.34
maxvit_tiny_rw_256,1008.0,1015.861,1024,256,6.74,44.35,29.07
vit_small_patch16_18x2_224,1006.7,1017.169,1024,224,13.71,35.69,64.67
swinv2_cr_small_224,1005.28,1018.603,1024,224,9.07,50.27,49.7
regnetx_080,1004.51,1019.384,1024,224,8.02,14.06,39.57
repvgg_b3,994.23,1029.925,1024,224,29.16,15.1,123.09
swinv2_cr_small_ns_224,993.75,1030.424,1024,224,9.08,50.27,49.7
repvgg_b2g4,988.97,1035.405,1024,224,12.63,12.9,61.76
convnext_small,988.3,1036.113,1024,288,14.39,35.65,50.22
gluon_xception65,987.82,777.458,768,299,13.96,52.48,39.92
vit_small_r26_s32_384,982.68,1042.031,1024,384,10.43,29.85,36.47
xception65,978.83,784.597,768,299,13.96,52.48,39.92
regnetz_040,975.77,787.056,768,320,6.35,37.78,27.12
regnetz_040h,971.51,790.512,768,320,6.43,37.94,28.94
gluon_seresnext101_64x4d,965.3,1060.794,1024,224,15.53,31.25,88.23
maxvit_tiny_pm_256,964.03,1062.189,1024,256,6.61,47.9,30.09
efficientformer_l7,962.55,1063.825,1024,224,10.17,24.45,82.23
twins_svt_large,962.19,1064.229,1024,224,15.15,35.1,99.27
tf_efficientnet_b4,957.62,534.646,512,380,4.49,49.49,19.34
pvt_v2_b4,957.38,1069.569,1024,224,10.14,53.74,62.56
poolformer_m36,954.91,1072.334,1024,224,8.8,22.02,56.17
cait_s24_224,954.44,1072.866,1024,224,9.35,40.58,46.92
regnetz_b16_evos,950.47,808.013,768,288,2.36,16.43,9.74
resnest50d_4s2x40d,938.07,1091.586,1024,224,4.4,17.94,30.42
hrnet_w48,936.07,1093.917,1024,224,17.34,28.56,77.47
gmlp_b16_224,930.95,1099.935,1024,224,15.78,30.21,73.08
convnextv2_tiny,930.82,550.041,512,288,7.39,22.21,28.64
convnextv2_small,928.68,1102.629,1024,224,8.71,21.56,50.32
maxxvit_rmlp_tiny_rw_256,918.72,1114.583,1024,256,6.66,39.76,29.64
mobilevitv2_150_384_in22ft1k,915.49,419.435,384,384,9.2,54.25,10.59
pvt_v2_b5,909.79,1125.516,1024,224,11.76,50.92,81.96
nest_small,903.21,850.284,768,224,10.35,40.04,38.35
swin_s3_small_224,899.98,853.339,768,224,9.43,37.84,49.74
xcit_medium_24_p16_224_dist,898.61,1139.525,1024,224,16.13,31.71,84.4
xcit_medium_24_p16_224,898.6,1139.542,1024,224,16.13,31.71,84.4
jx_nest_small,892.03,860.939,768,224,10.35,40.04,38.35
coat_mini,880.8,1162.569,1024,224,6.82,33.68,10.34
swin_base_patch4_window7_224,875.38,1169.764,1024,224,15.47,36.63,87.77
dpn131,865.2,1183.527,1024,224,16.09,32.97,79.25
resnetv2_50d_evos,854.82,1197.895,1024,288,7.15,19.7,25.59
xcit_small_12_p16_384_dist,853.54,1199.694,1024,384,14.14,36.51,26.25
sequencer2d_l,839.78,1219.347,1024,224,9.74,22.12,54.3
crossvit_base_240,839.43,914.892,768,240,21.22,36.33,105.03
hrnet_w44,821.37,1246.671,1024,224,14.94,26.92,67.06
eca_nfnet_l1,818.87,1250.489,1024,320,14.92,34.42,41.41
vit_base_r50_s16_224,817.55,1252.502,1024,224,21.67,35.31,114.69
maxvit_rmlp_small_rw_224,816.34,1254.368,1024,224,10.75,49.3,64.9
gcvit_small,815.24,1256.055,1024,224,8.57,41.61,51.09
regnety_080,811.28,1262.191,1024,288,13.22,29.69,39.18
densenet264,804.85,1272.268,1024,224,12.95,12.8,72.69
mvitv2_base,804.14,1273.395,1024,224,10.16,40.5,51.47
repvgg_b3g4,802.85,1275.443,1024,224,17.89,15.1,83.83
vit_base_patch16_plus_240,782.25,1309.022,1024,240,27.41,33.08,117.56
swinv2_tiny_window16_256,781.61,655.045,512,256,6.68,39.02,28.35
maxvit_small_tf_224,777.04,658.899,512,224,11.66,53.17,68.93
xcit_tiny_24_p8_224,771.1,1327.958,1024,224,9.21,45.39,12.11
xcit_tiny_24_p8_224_dist,770.21,1329.496,1024,224,9.21,45.39,12.11
coatnet_2_rw_224,763.52,670.562,512,224,15.09,49.22,73.87
vit_relpos_base_patch16_plus_240,763.4,1341.361,1024,240,27.3,34.33,117.38
efficientnet_b3_gn,763.0,671.023,512,320,2.14,28.83,11.73
coatnet_rmlp_2_rw_224,759.73,673.906,512,224,15.18,54.78,73.88
vit_small_patch16_384,753.82,1018.79,768,384,15.52,50.78,22.2
hrnet_w64,750.36,1364.663,1024,224,28.97,35.09,128.06
xception71,749.7,1024.396,768,299,18.09,69.92,42.34
resnet152d,742.37,1379.356,1024,320,24.08,47.67,60.21
swinv2_small_window8_256,741.95,1380.134,1024,256,11.58,40.14,49.73
mobilevitv2_175_384_in22ft1k,739.09,519.544,384,384,12.47,63.29,14.25
ecaresnet200d,736.17,1390.959,1024,256,20.0,43.15,64.69
seresnet200d,733.28,1396.444,1024,256,20.01,43.15,71.86
swin_s3_base_224,733.27,1396.459,1024,224,13.69,48.26,71.13
convit_base,731.09,1400.636,1024,224,17.52,31.77,86.54
resnest101e,726.65,1409.184,1024,256,13.38,28.66,48.28
deit3_small_patch16_384,726.49,1057.125,768,384,15.52,50.78,22.21
deit3_small_patch16_384_in21ft1k,726.32,1057.368,768,384,15.52,50.78,22.21
volo_d2_224,722.61,1417.079,1024,224,14.34,41.34,58.68
tnt_b_patch16_224,721.24,1419.762,1024,224,14.09,39.01,65.41
xcit_nano_12_p8_384_dist,720.41,1421.4,1024,384,6.34,46.08,3.05
swinv2_cr_base_224,719.23,1423.721,1024,224,15.86,59.66,87.88
poolformer_m48,719.07,1424.046,1024,224,11.59,29.17,73.47
coatnet_2_224,715.36,715.711,512,224,16.5,52.67,74.68
swinv2_cr_base_ns_224,712.96,1436.239,1024,224,15.86,59.66,87.88
dpn107,691.0,1481.897,1024,224,18.38,33.46,86.92
convnext_base,687.14,1490.219,1024,288,25.43,47.53,88.59
resnetv2_50x1_bitm,684.31,374.087,256,448,16.62,44.46,25.55
efficientnet_b3_g8_gn,664.63,770.341,512,320,3.2,28.83,14.25
regnety_064,657.71,1556.911,1024,288,10.56,27.11,30.58
regnetv_064,652.6,1569.096,1024,288,10.55,27.11,30.58
xcit_small_12_p8_224,651.3,1572.214,1024,224,18.69,47.21,26.21
xcit_small_12_p8_224_dist,651.08,1572.755,1024,224,18.69,47.21,26.21
resnetrs152,649.95,1575.501,1024,320,24.34,48.14,86.62
mobilevitv2_200_384_in22ft1k,647.42,395.4,256,384,16.24,72.34,18.45
seresnet152d,645.69,1585.88,1024,320,24.09,47.72,66.84
tresnet_l,644.38,1589.105,1024,224,10.88,11.9,55.99
tresnet_v2_l,642.3,1594.246,1024,224,8.81,16.34,46.17
nest_base,640.98,798.76,512,224,17.96,53.39,67.72
regnetx_120,640.37,1599.07,1024,224,12.13,21.37,46.11
seresnext101_32x8d,639.53,1601.159,1024,288,27.24,51.63,93.57
regnetz_e8,639.43,1601.423,1024,320,15.46,63.94,57.7
ese_vovnet99b_iabn,636.1,1609.798,1024,224,16.49,11.27,63.2
jx_nest_base,634.61,806.787,512,224,17.96,53.39,67.72
regnety_120,625.75,1636.422,1024,224,12.14,21.38,51.82
efficientnetv2_m,624.53,1639.618,1024,416,18.6,67.5,54.14
seresnext101d_32x8d,621.55,1647.466,1024,288,27.64,52.95,93.59
resnext101_64x4d,619.77,1652.21,1024,288,25.66,51.59,83.46
swsl_resnext101_32x16d,612.21,1672.624,1024,224,36.27,51.18,194.03
ig_resnext101_32x16d,611.98,1673.243,1024,224,36.27,51.18,194.03
maxvit_rmlp_small_rw_256,611.67,1255.571,768,256,14.15,66.09,64.9
ssl_resnext101_32x16d,611.31,1675.063,1024,224,36.27,51.18,194.03
regnety_320,605.31,1691.684,1024,224,32.34,30.26,145.05
gcvit_base,602.42,1699.782,1024,224,14.87,55.48,90.32
regnetz_c16_evos,596.93,857.706,512,320,3.86,25.88,13.49
maxxvit_rmlp_small_rw_256,590.18,1735.046,1024,256,14.67,58.38,66.01
legacy_senet154,585.86,1747.854,1024,224,20.77,38.69,115.09
senet154,585.53,1748.836,1024,224,20.77,38.69,115.09
seresnextaa101d_32x8d,585.08,1750.175,1024,288,28.51,56.44,93.59
gluon_senet154,584.86,1750.843,1024,224,20.77,38.69,115.09
convmixer_768_32,581.95,1759.577,1024,224,19.55,25.95,21.11
seresnet269d,574.5,1782.4,1024,256,26.59,53.6,113.67
nf_regnet_b5,565.36,905.602,512,456,11.7,61.95,49.74
mixer_l16_224,553.66,1849.49,1024,224,44.6,41.69,208.2
resnet200d,545.14,1878.401,1024,320,31.25,67.33,64.69
nfnet_f1,544.28,1881.353,1024,320,35.97,46.77,132.63
vit_large_patch32_384,543.45,1884.237,1024,384,45.31,43.86,306.63
efficientnetv2_rw_m,543.37,1884.512,1024,416,21.49,79.62,53.24
vit_medium_patch16_gap_384,539.24,949.475,512,384,26.08,67.54,39.03
efficientnet_b5,533.21,960.212,512,448,9.59,93.56,30.39
swinv2_base_window8_256,531.81,1925.495,1024,256,20.37,52.59,87.92
maxxvitv2_rmlp_base_rw_224,525.72,1947.791,1024,224,24.2,62.77,116.09
xcit_large_24_p16_224_dist,509.19,2011.039,1024,224,35.86,47.27,189.1
xcit_large_24_p16_224,509.15,2011.169,1024,224,35.86,47.27,189.1
swin_large_patch4_window7_224,504.4,1522.593,768,224,34.53,54.94,196.53
halonet_h1,503.39,508.543,256,256,3.0,51.17,8.1
volo_d3_224,502.58,2037.467,1024,224,20.78,60.09,86.33
swinv2_small_window16_256,488.97,1047.084,512,256,12.82,66.29,49.73
tresnet_xl,481.58,2126.301,1024,224,15.17,15.34,78.44
vit_small_patch8_224,479.11,1068.641,512,224,22.44,80.84,21.67
tf_efficientnet_b5,476.47,805.919,384,456,10.46,98.86,30.39
maxvit_rmlp_base_rw_224,472.06,2169.196,1024,224,23.15,92.64,116.14
resnetrs200,471.68,2170.964,1024,320,31.51,67.81,93.21
xcit_tiny_12_p8_384_dist,471.45,2172.002,1024,384,14.13,69.14,6.71
dm_nfnet_f1,461.24,2220.087,1024,320,35.97,46.77,132.63
tf_efficientnetv2_m,458.93,1673.426,768,480,24.76,89.84,54.14
xcit_small_24_p16_384_dist,457.16,2239.891,1024,384,26.72,68.58,47.67
coatnet_rmlp_3_rw_224,439.5,582.463,256,224,33.56,79.47,165.15
maxvit_base_tf_224,430.05,1190.542,512,224,24.04,95.01,119.47
swinv2_cr_large_224,423.86,1811.887,768,224,35.1,78.42,196.68
resnetv2_152x2_bit_teacher,423.36,2418.743,1024,224,46.95,45.11,236.34
swinv2_cr_tiny_384,423.1,907.565,384,384,15.34,161.01,28.33
coatnet_3_rw_224,421.95,606.701,256,224,33.44,73.83,181.81
resnetv2_101x1_bitm,419.35,610.453,256,448,31.65,64.93,44.54
coatnet_3_224,405.07,631.982,256,224,36.56,79.01,166.97
convnextv2_base,403.59,1268.593,512,288,25.43,47.53,88.72
eca_nfnet_l2,401.73,2548.946,1024,384,30.05,68.28,56.72
regnetz_d8_evos,394.39,1947.294,768,320,7.03,38.92,23.46
convmixer_1024_20_ks9_p14,393.5,2602.254,1024,224,5.55,5.51,24.38
eva_large_patch14_196,392.3,2610.234,1024,196,61.57,63.52,304.14
crossvit_15_dagger_408,390.72,655.182,256,408,21.45,95.05,28.5
vit_large_patch16_224,390.66,2621.182,1024,224,61.6,63.52,304.33
vit_base_patch16_18x2_224,384.38,2663.987,1024,224,52.51,71.38,256.73
deit3_large_patch16_224_in21ft1k,377.58,2711.976,1024,224,61.6,63.52,304.37
deit3_large_patch16_224,377.53,2712.348,1024,224,61.6,63.52,304.37
convnext_large,373.02,2058.836,768,288,56.87,71.29,197.77
beit_large_patch16_224,360.62,2839.572,1024,224,61.6,63.52,304.43
beitv2_large_patch16_224,360.58,2839.86,1024,224,61.6,63.52,304.43
swinv2_base_window12to16_192to256_22kft1k,360.56,1065.006,384,256,22.02,84.71,87.92
swinv2_base_window16_256,360.23,1065.959,384,256,22.02,84.71,87.92
regnety_160,353.5,2172.566,768,288,26.37,38.07,83.59
nasnetalarge,345.63,1111.004,384,331,23.89,90.56,88.75
maxvit_tiny_tf_384,344.01,744.157,256,384,17.53,123.42,30.98
xcit_small_24_p8_224,342.37,2990.915,1024,224,35.81,90.78,47.63
xcit_small_24_p8_224_dist,342.26,2991.817,1024,224,35.81,90.78,47.63
flexivit_large,335.35,3053.52,1024,240,70.99,75.39,304.36
maxxvitv2_rmlp_large_rw_224,332.33,3081.271,1024,224,44.14,87.15,215.42
vit_large_r50_s32_384,329.8,3104.921,1024,384,57.43,76.52,329.09
pnasnet5large,328.89,1167.534,384,331,25.04,92.89,86.06
tresnet_m_448,325.8,3143.01,1024,448,22.94,29.21,31.39
volo_d1_384,323.04,1584.906,512,384,22.75,108.55,26.78
volo_d4_224,318.96,3210.439,1024,224,44.34,80.22,192.96
xcit_medium_24_p16_384_dist,312.74,3274.268,1024,384,47.39,91.64,84.4
nfnet_f2,310.6,3296.869,1024,352,63.22,79.06,193.78
vit_base_patch16_384,307.09,1250.42,384,384,55.54,101.56,86.86
deit_base_patch16_384,306.8,1251.599,384,384,55.54,101.56,86.86
vit_base_patch16_clip_384,306.29,1253.685,384,384,55.54,101.56,86.86
deit_base_distilled_patch16_384,305.48,1257.017,384,384,55.65,101.82,87.63
ecaresnet269d,305.06,3356.684,1024,352,50.25,101.25,102.09
maxvit_large_tf_224,301.43,1273.908,384,224,43.68,127.35,211.79
deit3_base_patch16_384_in21ft1k,298.01,1288.526,384,384,55.54,101.56,86.88
deit3_base_patch16_384,297.88,1289.093,384,384,55.54,101.56,86.88
resnetrs270,296.97,3448.186,1024,352,51.13,105.48,129.86
regnetx_320,289.44,2653.413,768,224,31.81,36.3,107.81
efficientnet_b6,287.31,890.997,256,528,19.4,167.39,43.04
vit_large_patch14_224,286.23,3577.501,1024,224,81.08,88.79,304.2
vit_large_patch14_clip_224,285.99,3580.5,1024,224,81.08,88.79,304.2
crossvit_18_dagger_408,285.18,673.248,192,408,32.47,124.87,44.61
cait_xxs24_384,281.48,3637.936,1024,384,9.63,122.66,12.03
ig_resnext101_32x32d,275.12,1860.956,512,224,87.29,91.12,468.53
tf_efficientnet_b6,274.07,700.545,192,528,19.4,167.39,43.04
dm_nfnet_f2,264.79,2900.408,768,352,63.22,79.06,193.78
beit_base_patch16_384,261.27,1469.733,384,384,55.54,101.56,86.74
efficientnetv2_l,260.33,1966.694,512,480,56.4,157.99,118.52
swinv2_cr_small_384,259.75,985.56,256,384,29.7,298.03,49.7
tf_efficientnetv2_l,257.29,1989.923,512,480,56.4,157.99,118.52
resnest200e,254.36,1006.453,256,320,35.69,82.78,70.2
mvitv2_large,249.99,2048.061,512,224,43.87,112.02,217.99
xcit_tiny_24_p8_384_dist,248.25,4124.916,1024,384,27.05,132.95,12.11
convnext_xlarge,242.63,2110.182,512,288,100.8,95.05,350.2
resmlp_big_24_224_in22ft1k,241.9,4233.056,1024,224,100.23,87.31,129.14
resmlp_big_24_224,241.74,4235.988,1024,224,100.23,87.31,129.14
resmlp_big_24_distilled_224,241.44,4241.249,1024,224,100.23,87.31,129.14
convnextv2_large,239.52,1068.782,256,288,56.87,71.29,197.96
coatnet_4_224,238.62,1072.827,256,224,62.48,129.26,275.43
swin_base_patch4_window12_384,236.12,813.144,192,384,47.19,134.78,87.9
xcit_medium_24_p8_224_dist,233.5,3289.007,768,224,63.53,121.23,84.32
xcit_medium_24_p8_224,233.5,3289.104,768,224,63.53,121.23,84.32
eca_nfnet_l3,229.87,2227.284,512,448,52.55,118.4,72.04
vit_base_r50_s16_384,226.32,1696.687,384,384,67.43,135.03,98.95
maxvit_small_tf_384,224.01,857.105,192,384,35.87,183.65,69.02
xcit_small_12_p8_384_dist,221.54,1733.28,384,384,54.92,138.29,26.21
swinv2_large_window12to16_192to256_22kft1k,220.1,1163.101,256,256,47.81,121.53,196.74
volo_d5_224,210.88,4855.76,1024,224,72.4,118.11,295.46
vit_base_patch8_224,199.67,1282.079,256,224,78.22,161.69,86.58
cait_xs24_384,197.64,3885.811,768,384,19.28,183.98,26.67
resnetrs350,196.19,5219.377,1024,384,77.59,154.74,163.96
cait_xxs36_384,188.27,5439.03,1024,384,14.35,183.7,17.37
swinv2_cr_base_384,185.68,1378.725,256,384,50.57,333.68,87.88
coatnet_rmlp_2_rw_384,184.84,1038.746,192,384,47.69,209.43,73.88
swinv2_cr_huge_224,184.09,2085.934,384,224,115.97,121.08,657.83
convnext_xxlarge,183.68,2787.486,512,224,151.66,95.29,846.47
volo_d2_384,180.56,2126.753,384,384,46.17,184.51,58.87
xcit_large_24_p16_384_dist,176.39,5805.281,1024,384,105.35,137.17,189.1
regnety_640,174.81,4393.396,768,224,64.16,42.5,281.38
maxvit_xlarge_tf_224,171.63,1491.6,256,224,97.49,191.02,474.95
nfnet_f3,170.11,4514.791,768,416,115.58,141.78,254.92
densenet264d_iabn,167.13,6126.84,1024,224,13.47,14.0,72.74
efficientnet_b7,166.38,1153.975,192,600,38.33,289.94,66.35
maxvit_tiny_tf_512,163.72,781.809,128,512,33.49,257.59,31.05
efficientnetv2_xl,162.7,3146.865,512,512,93.85,247.32,208.12
tf_efficientnetv2_xl,161.32,3173.821,512,512,93.85,247.32,208.12
tf_efficientnet_b7,160.43,1196.798,192,600,38.33,289.94,66.35
resnetv2_152x2_bit_teacher_384,159.54,1604.579,256,384,136.16,132.56,236.34
tresnet_l_448,154.66,6620.743,1024,448,43.5,47.56,55.99
vit_huge_patch14_224,154.27,6637.58,1024,224,167.43,139.43,658.75
vit_huge_patch14_clip_224,154.17,6642.017,1024,224,167.4,139.41,632.05
maxxvitv2_rmlp_base_rw_384,153.9,1663.429,256,384,72.98,213.74,116.09
cait_s24_384,152.41,3359.254,512,384,32.17,245.31,47.06
deit3_huge_patch14_224_in21ft1k,150.05,6824.53,1024,224,167.4,139.41,632.13
deit3_huge_patch14_224,149.59,6845.356,1024,224,167.4,139.41,632.13
dm_nfnet_f3,145.48,3519.403,512,416,115.58,141.78,254.92
resnetrs420,142.37,5394.528,768,416,108.45,213.79,191.89
swin_large_patch4_window12_384,138.37,925.016,128,384,104.08,202.16,196.74
resnetv2_50x3_bitm,133.5,1438.189,192,448,145.7,133.37,217.32
maxvit_rmlp_base_rw_384,131.6,1945.285,256,384,70.97,318.95,116.14
xcit_large_24_p8_224_dist,131.32,3898.808,512,224,141.23,181.56,188.93
xcit_large_24_p8_224,131.27,3900.391,512,224,141.23,181.56,188.93
coatnet_5_224,130.48,1471.508,192,224,145.49,194.24,687.47
maxvit_base_tf_384,122.48,1567.652,192,384,73.8,332.9,119.65
resnest269e,119.17,2148.198,256,416,77.69,171.98,110.93
resnetv2_152x2_bitm,117.29,2182.534,256,448,184.99,180.43,236.34
xcit_small_24_p8_384_dist,116.59,3293.649,384,384,105.24,265.91,47.63
tresnet_xl_448,115.63,8855.938,1024,448,60.65,61.31,78.44
swinv2_cr_large_384,113.43,1128.479,128,384,108.95,404.96,196.68
maxvit_small_tf_512,106.82,1198.298,128,512,67.26,383.77,69.13
efficientnet_b8,106.21,1205.18,128,672,63.48,442.89,87.41
tf_efficientnet_b8,102.86,1244.358,128,672,63.48,442.89,87.41
eva_large_patch14_336,102.71,2492.371,256,336,191.1,270.24,304.53
vit_large_patch14_clip_336,102.52,2496.99,256,336,191.11,270.24,304.53
vit_large_patch16_384,102.5,2497.593,256,384,191.21,270.24,304.72
cait_s36_384,101.88,5025.316,512,384,47.99,367.4,68.37
eva_giant_patch14_224,101.84,10055.112,1024,224,267.18,192.64,1012.56
vit_giant_patch14_224,100.71,7625.752,768,224,267.18,192.64,1012.61
vit_giant_patch14_clip_224,100.43,7646.856,768,224,267.18,192.64,1012.65
deit3_large_patch16_384_in21ft1k,99.81,2564.809,256,384,191.21,270.24,304.76
deit3_large_patch16_384,99.8,2564.994,256,384,191.21,270.24,304.76
swinv2_base_window12to24_192to384_22kft1k,96.12,665.832,64,384,55.25,280.36,87.92
nfnet_f4,89.33,5731.574,512,512,216.26,262.26,316.07
beit_large_patch16_384,88.56,2890.58,256,384,191.21,270.24,305.0
maxvit_large_tf_384,86.44,1480.84,128,384,132.55,445.84,212.03
regnety_1280,82.49,4654.845,384,224,127.66,71.58,644.81
xcit_medium_24_p8_384_dist,79.96,3201.705,256,384,186.67,354.73,84.32
resnetv2_101x3_bitm,79.41,2417.67,192,448,280.33,194.78,387.93
volo_d3_448,77.64,2473.021,192,448,96.33,446.83,86.63
dm_nfnet_f4,77.54,4952.036,384,512,216.26,262.26,316.07
nfnet_f5,67.46,5691.915,384,544,290.97,349.71,377.21
tf_efficientnet_l2,63.66,1507.989,96,475,172.11,609.89,480.31
swinv2_large_window12to24_192to384_22kft1k,60.94,787.651,48,384,116.15,407.83,196.74
vit_gigantic_patch14_224,60.18,8507.121,512,224,483.95,275.37,1844.44
vit_gigantic_patch14_clip_224,60.11,8517.85,512,224,483.96,275.37,1844.91
volo_d4_448,57.87,3317.675,192,448,197.13,527.35,193.41
maxvit_base_tf_512,57.86,2212.256,128,512,138.02,703.99,119.88
dm_nfnet_f5,57.78,6645.368,384,544,290.97,349.71,377.21
vit_huge_patch14_clip_336,57.4,4460.085,256,336,390.97,407.54,632.46
ig_resnext101_32x48d,56.43,6804.709,384,224,153.57,131.06,828.41
convnextv2_huge,56.31,1704.92,96,384,337.96,232.35,660.29
convmixer_1536_20,55.47,18461.426,1024,224,48.68,33.03,51.63
swinv2_cr_giant_224,52.39,3665.046,192,224,483.85,309.15,2598.76
nfnet_f6,51.81,7411.574,384,576,378.69,452.2,438.36
maxvit_xlarge_tf_384,50.76,1891.335,96,384,292.78,668.76,475.32
swinv2_cr_huge_384,49.01,1305.73,64,384,352.04,583.18,657.94
regnety_2560,47.69,8051.463,384,224,257.07,87.48,826.14
xcit_large_24_p8_384_dist,44.91,4275.004,192,384,415.0,531.82,188.93
dm_nfnet_f6,44.62,5737.462,256,576,378.69,452.2,438.36
nfnet_f7,41.13,6224.782,256,608,480.39,570.85,499.5
maxvit_large_tf_512,41.04,1559.597,64,512,244.75,942.15,212.33
eva_giant_patch14_336,39.89,6418.269,256,336,620.64,550.67,1013.01
volo_d5_448,39.88,3209.812,128,448,315.06,737.92,295.91
beit_large_patch16_512,35.33,2716.953,96,512,362.24,656.39,305.67
cait_m36_384,32.89,7783.487,256,384,173.11,734.81,271.22
resnetv2_152x4_bitm,30.46,3151.929,96,480,844.84,414.26,936.53
volo_d5_512,27.89,4590.0,128,512,425.09,1105.37,296.09
maxvit_xlarge_tf_512,24.38,1968.424,48,512,534.14,1413.22,475.77
efficientnet_l2,23.13,1383.428,32,800,479.12,1707.39,480.31
swinv2_cr_giant_384,15.06,2124.735,32,384,1450.71,1394.86,2598.76
cait_m48_448,13.86,9235.876,128,448,329.41,1708.23,356.46
eva_giant_patch14_560,10.52,3043.009,32,560,1906.76,2577.17,1014.45
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nchw-pt111-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,9380.97,53.881,512,106,2.04
mobilenetv3_small_050,7276.68,69.643,512,224,1.59
tf_mobilenetv3_small_minimal_100,6334.14,80.291,512,224,2.04
mobilenetv3_small_075,5920.21,85.765,512,224,2.04
lcnet_035,5760.61,88.397,512,224,1.64
mobilenetv3_small_100,5583.48,90.99,512,224,2.54
tf_mobilenetv3_small_075,5569.37,91.204,512,224,2.04
levit_128s,5426.95,93.402,512,224,7.78
lcnet_050,5425.23,93.895,512,224,1.88
tf_mobilenetv3_small_100,5275.47,96.328,512,224,2.54
tinynet_d,4879.29,104.179,512,152,2.34
mixer_s32_224,4491.84,113.42,512,224,19.1
vit_small_patch32_224,4321.32,117.658,512,224,22.88
lcnet_075,4147.31,122.971,512,224,2.36
levit_128,3971.6,127.764,512,224,9.21
vit_tiny_r_s16_p8_224,3957.53,128.516,512,224,6.34
lcnet_100,3524.02,144.807,512,224,2.95
mnasnet_small,3488.23,145.878,512,224,2.03
levit_192,3436.74,147.842,512,224,10.95
regnetx_002,3423.6,148.87,512,224,2.68
regnety_002,3178.35,160.128,512,224,3.16
mobilenetv2_035,3069.55,166.033,512,224,1.68
gernet_s,2949.06,172.893,512,224,8.17
mnasnet_050,2821.34,180.673,512,224,2.22
ssl_resnet18,2720.94,187.823,512,224,11.69
swsl_resnet18,2720.55,187.842,512,224,11.69
gluon_resnet18_v1b,2718.42,187.998,512,224,11.69
resnet18,2711.83,188.459,512,224,11.69
semnasnet_050,2700.91,188.67,512,224,2.08
tinynet_c,2654.89,191.851,512,184,2.46
mobilenetv2_050,2650.81,192.34,512,224,1.97
levit_256,2620.65,194.218,512,224,18.89
lcnet_150,2613.69,195.4,512,224,4.5
regnetx_004,2532.79,201.109,512,224,5.16
seresnet18,2519.64,202.716,512,224,11.78
legacy_seresnet18,2461.74,207.479,512,224,11.78
ese_vovnet19b_slim_dw,2456.97,207.908,512,224,1.9
mobilenetv3_large_075,2342.92,217.677,512,224,3.99
tf_mobilenetv3_large_minimal_100,2311.18,220.824,512,224,3.92
vit_tiny_patch16_224,2249.49,226.757,512,224,5.72
levit_256d,2248.95,226.128,512,224,26.21
deit_tiny_patch16_224,2244.74,227.248,512,224,5.72
tf_mobilenetv3_large_075,2222.25,229.522,512,224,3.99
deit_tiny_distilled_patch16_224,2207.23,231.107,512,224,5.91
ghostnet_050,2193.43,232.046,512,224,2.59
mnasnet_075,2163.05,235.897,512,224,3.17
mobilenetv3_rw,2147.03,237.6,512,224,5.48
mobilenetv3_large_100_miil,2135.44,238.882,512,224,5.48
mobilenetv3_large_100,2134.66,238.979,512,224,5.48
resnet18d,2086.64,244.99,512,224,11.71
pit_ti_distilled_224,2085.77,244.59,512,224,5.1
pit_ti_224,2083.57,244.847,512,224,4.85
hardcorenas_a,2050.08,249.015,512,224,5.26
regnetx_006,2048.56,249.103,512,224,6.2
tf_mobilenetv3_large_100,2046.24,249.335,512,224,5.48
ese_vovnet19b_slim,1997.73,255.915,512,224,3.17
mnasnet_100,1996.94,255.609,512,224,4.38
mnasnet_b1,1993.53,256.025,512,224,4.38
xcit_nano_12_p16_224_dist,1946.62,261.255,512,224,3.05
xcit_nano_12_p16_224,1946.1,261.308,512,224,3.05
semnasnet_075,1927.77,264.67,512,224,2.91
hardcorenas_b,1912.19,266.758,512,224,5.18
mobilenetv2_075,1888.29,270.368,512,224,2.64
hardcorenas_c,1885.12,270.645,512,224,5.52
tf_efficientnetv2_b0,1880.38,271.08,512,224,7.14
tinynet_b,1857.15,274.614,512,188,3.73
regnety_004,1855.83,274.756,512,224,4.34
resnetblur18,1846.1,276.978,512,224,11.69
regnety_006,1808.76,282.043,512,224,6.06
hardcorenas_d,1808.52,281.873,512,224,7.5
mnasnet_a1,1796.84,284.044,512,224,3.89
semnasnet_100,1784.41,286.019,512,224,3.89
spnasnet_100,1782.41,286.288,512,224,4.42
skresnet18,1776.65,287.57,512,224,11.96
mobilenetv2_100,1761.25,289.902,512,224,3.5
mixer_b32_224,1711.23,298.43,512,224,60.29
regnetx_008,1669.43,305.887,512,224,7.26
levit_384,1662.81,306.81,512,224,39.13
vit_base_patch32_224,1658.09,307.969,512,224,88.22
vit_base_patch32_224_sam,1657.82,308.015,512,224,88.22
efficientnet_lite0,1636.56,312.086,512,224,4.65
visformer_tiny,1629.51,313.524,512,224,10.32
gluon_resnet34_v1b,1626.25,314.261,512,224,21.8
resnet34,1624.32,314.625,512,224,21.8
tv_resnet34,1622.42,314.985,512,224,21.8
ghostnet_100,1610.26,316.618,512,224,5.18
hardcorenas_f,1606.37,317.568,512,224,8.2
pit_xs_distilled_224,1596.23,319.885,512,224,11.0
pit_xs_224,1595.39,320.023,512,224,10.62
hardcorenas_e,1595.11,319.841,512,224,8.07
tinynet_a,1592.8,320.187,512,192,6.19
resmlp_12_distilled_224,1562.43,326.888,512,224,15.35
resmlp_12_224,1562.2,326.933,512,224,15.35
regnety_008,1550.03,329.315,512,224,6.26
tf_efficientnet_lite0,1547.08,330.18,512,224,4.65
mixer_s16_224,1538.34,332.27,512,224,18.53
fbnetc_100,1532.35,333.142,512,224,5.57
seresnet34,1494.24,341.776,512,224,21.96
mnasnet_140,1493.96,341.916,512,224,7.12
nf_regnet_b0,1481.15,344.453,512,256,8.76
legacy_seresnet34,1458.06,350.271,512,224,21.96
gmixer_12_224,1449.53,352.403,512,224,12.7
gernet_m,1442.79,354.129,512,224,21.14
ese_vovnet19b_dw,1440.24,354.984,512,224,6.54
vit_small_patch32_384,1423.05,358.931,512,384,22.92
nf_resnet26,1422.6,359.386,512,224,16.0
efficientnet_b0,1413.79,270.572,384,224,5.29
dla46_c,1408.04,362.893,512,224,1.3
rexnetr_100,1385.71,275.935,384,224,4.88
mobilenetv2_110d,1384.57,276.313,384,224,4.52
rexnet_100,1381.06,276.903,384,224,4.8
vit_tiny_r_s16_p8_384,1371.38,279.143,384,384,6.36
ghostnet_130,1367.64,372.982,512,224,7.36
resnet34d,1367.17,373.876,512,224,21.82
tf_efficientnet_b0,1353.9,282.522,384,224,5.29
tf_efficientnet_b0_ap,1353.18,282.683,384,224,5.29
tf_efficientnet_b0_ns,1352.63,282.744,384,224,5.29
selecsls42,1349.71,378.706,512,224,30.35
selecsls42b,1347.34,379.337,512,224,32.46
gmlp_ti16_224,1340.35,284.915,384,224,5.87
regnetz_005,1326.54,384.587,512,224,7.12
crossvit_tiny_240,1322.74,385.447,512,240,7.01
resnet26,1321.4,386.974,512,224,16.0
semnasnet_140,1315.96,388.179,512,224,6.11
xcit_tiny_12_p16_224,1309.91,389.1,512,224,6.72
xcit_tiny_12_p16_224_dist,1303.81,390.894,512,224,6.72
hrnet_w18_small,1302.26,391.765,512,224,13.19
efficientnet_b1_pruned,1277.21,399.368,512,240,6.33
mobilevit_xxs,1270.89,301.046,384,256,1.27
mobilenetv2_140,1250.7,306.236,384,224,6.11
poolformer_s12,1245.33,410.466,512,224,11.92
crossvit_9_240,1236.49,309.127,384,240,8.55
nf_seresnet26,1207.14,423.49,512,224,17.4
tf_efficientnetv2_b1,1198.02,319.027,384,240,8.14
repvgg_b0,1184.29,431.28,512,224,15.82
crossvit_9_dagger_240,1177.9,324.572,384,240,8.78
selecsls60,1152.93,443.193,512,224,30.67
mixnet_s,1150.64,443.717,512,224,4.13
selecsls60b,1149.12,444.678,512,224,32.77
efficientnet_es,1142.77,447.293,512,224,5.44
efficientnet_es_pruned,1141.22,447.892,512,224,5.44
nf_ecaresnet26,1137.4,449.596,512,224,16.0
resnet26d,1125.5,454.383,512,224,16.01
tf_efficientnet_es,1120.47,456.18,512,224,5.44
efficientnet_lite1,1109.69,229.723,256,240,5.42
rexnetr_130,1097.15,232.191,256,224,7.61
tf_mixnet_s,1090.03,468.412,512,224,4.13
convit_tiny,1087.94,469.614,512,224,5.71
convnext_nano_hnf,1075.51,356.223,384,224,15.59
dla34,1072.51,476.787,512,224,15.74
tf_efficientnet_lite1,1061.0,240.288,256,240,5.42
dla46x_c,1058.5,482.972,512,224,1.07
rexnet_130,1051.04,242.43,256,224,7.56
regnetx_016,1042.11,490.415,512,224,9.19
mobilenetv2_120d,1036.53,245.787,256,224,5.83
skresnet34,1033.13,494.425,512,224,22.28
vit_small_patch16_224,1032.86,370.943,384,224,22.05
deit_small_patch16_224,1031.61,371.386,384,224,22.05
deit_small_distilled_patch16_224,1011.7,378.721,384,224,22.44
dla60x_c,1011.12,505.401,512,224,1.32
ecaresnet50d_pruned,1009.57,506.231,512,224,19.94
gernet_l,1007.43,507.303,512,256,31.08
efficientnet_b0_g16_evos,992.51,385.814,384,224,8.11
rexnetr_150,973.93,261.684,256,224,9.78
vit_base2_patch32_256,970.35,526.818,512,256,119.46
pit_s_distilled_224,963.9,264.693,256,224,24.04
pit_s_224,963.25,264.885,256,224,23.46
fbnetv3_b,936.5,408.4,384,256,8.6
rexnet_150,934.54,272.774,256,224,9.73
repvgg_a2,931.84,548.616,512,224,28.21
legacy_seresnext26_32x4d,930.69,411.955,384,224,16.79
regnety_016,921.99,553.558,512,224,11.2
resnest14d,909.12,562.716,512,224,10.61
efficientnet_cc_b0_4e,892.75,428.931,384,224,13.31
efficientnet_cc_b0_8e,889.3,430.599,384,224,24.01
coat_lite_tiny,885.61,432.738,384,224,5.72
nf_regnet_b1,883.44,578.138,512,288,10.22
resnetv2_50,883.05,579.001,512,224,25.55
resnext26ts,882.89,434.405,384,256,10.3
resnet26t,880.13,581.227,512,256,16.01
tf_efficientnetv2_b2,877.18,290.276,256,260,10.1
fbnetv3_d,875.63,290.586,256,256,10.31
nf_regnet_b2,875.24,583.366,512,272,14.31
tf_efficientnet_cc_b0_4e,872.5,438.913,384,224,13.31
tf_efficientnet_cc_b0_8e,867.49,441.45,384,224,24.01
efficientnet_b0_gn,860.35,296.448,256,224,5.29
eca_resnext26ts,852.81,299.643,256,256,10.3
seresnext26ts,848.06,301.17,256,256,10.39
botnet26t_256,835.32,459.139,384,256,12.49
gluon_resnet50_v1b,835.19,458.946,384,224,25.56
twins_svt_small,835.02,458.264,384,224,24.06
swsl_resnet50,834.38,459.391,384,224,25.56
resnet50,833.17,460.051,384,224,25.56
tf_efficientnet_b1_ap,832.71,305.903,256,240,7.79
tf_efficientnet_b1,832.37,306.044,256,240,7.79
tf_efficientnet_b1_ns,831.62,306.366,256,240,7.79
tv_resnet50,831.54,460.947,384,224,25.56
efficientnet_b2_pruned,831.45,306.38,256,260,8.31
seresnext26tn_32x4d,831.09,461.371,384,224,16.81
seresnext26d_32x4d,830.66,461.598,384,224,16.81
gcresnext26ts,830.6,307.372,256,256,10.48
seresnext26t_32x4d,830.1,461.943,384,224,16.81
ssl_resnet50,828.96,462.397,384,224,25.56
coat_lite_mini,825.96,464.063,384,224,11.01
vgg11,818.58,625.301,512,224,132.86
halonet26t,813.08,471.69,384,256,12.48
vit_small_resnet26d_224,812.32,471.613,384,224,63.61
eca_botnext26ts_256,804.88,317.468,256,256,10.59
efficientnet_lite2,800.14,318.978,256,260,6.09
efficientnet_b1,797.8,319.366,256,256,7.79
resnetv2_50t,793.9,644.111,512,224,25.57
ecaresnext26t_32x4d,792.87,483.752,384,224,15.41
ecaresnext50t_32x4d,790.88,484.99,384,224,15.41
resnetv2_50d,790.5,646.873,512,224,25.57
tresnet_m,787.27,647.568,512,224,31.39
convnext_tiny,782.73,326.109,256,224,28.59
eca_halonext26ts,781.32,327.03,256,256,10.76
vovnet39a,779.14,656.53,512,224,22.6
ecaresnet101d_pruned,778.75,655.702,512,224,24.88
mixnet_m,778.63,491.619,384,224,5.01
cspresnet50,774.32,495.068,384,256,21.62
tf_efficientnet_lite2,771.04,331.043,256,260,6.09
gluon_resnet50_v1c,769.5,498.184,384,224,25.58
ecaresnetlight,767.59,666.098,512,224,30.16
resmlp_24_224,766.31,332.575,256,224,30.02
resmlp_24_distilled_224,766.21,332.58,256,224,30.02
cspresnext50,765.28,500.93,384,224,20.57
resnet50t,753.13,509.003,384,224,25.57
resnet50d,752.95,509.12,384,224,25.58
gluon_resnet50_v1d,752.94,509.122,384,224,25.58
legacy_seresnet50,751.28,510.001,384,224,28.09
mobilevit_xs,751.23,339.638,256,256,2.32
tf_mixnet_m,748.19,511.684,384,224,5.01
ese_vovnet39b,748.06,683.786,512,224,24.57
resnet32ts,744.11,343.453,256,256,17.96
visformer_small,743.29,515.928,384,224,40.22
dpn68b,737.5,519.461,384,224,12.61
selecsls84,735.99,694.378,512,224,50.95
resnet33ts,732.63,348.809,256,256,19.68
nf_seresnet50,732.06,523.375,384,224,28.09
rexnetr_200,724.64,263.786,192,224,16.52
lambda_resnet26t,724.61,529.342,384,256,10.96
seresnet50,722.32,530.5,384,224,28.09
dpn68,721.48,531.095,384,224,12.61
res2net50_48w_2s,717.64,534.245,384,224,25.29
gmixer_24_224,717.31,355.331,256,224,24.72
eca_resnet33ts,712.11,358.824,256,256,19.68
seresnet33ts,709.27,360.12,256,256,19.78
bat_resnext26ts,708.45,360.137,256,256,10.73
rexnet_200,703.57,271.754,192,224,16.37
resnetblur50,702.19,546.026,384,224,25.56
cspresnet50d,701.76,546.305,384,256,21.64
vgg11_bn,698.67,549.368,384,224,132.87
twins_pcpvt_small,698.21,364.986,256,224,24.11
eca_vovnet39b,697.82,733.074,512,224,22.6
efficientnet_em,694.64,551.807,384,240,6.9
dla60,693.8,552.499,384,224,22.04
tf_efficientnet_em,692.96,368.419,256,240,6.9
cspresnet50w,692.76,553.433,384,256,28.12
resnest26d,688.5,556.976,384,224,17.07
gcresnet33ts,687.02,371.584,256,256,19.88
tv_densenet121,686.65,371.007,256,224,7.98
densenet121,686.51,371.091,256,224,7.98
vit_small_r26_s32_224,685.79,371.994,256,224,36.43
convnext_tiny_hnf,683.74,373.449,256,224,28.59
xcit_nano_12_p16_384_dist,682.7,373.202,256,384,3.05
xcit_tiny_24_p16_224_dist,681.26,372.348,256,224,12.12
lambda_resnet26rpt_256,679.63,281.896,192,256,10.99
xcit_tiny_24_p16_224,679.38,373.443,256,224,12.12
vit_base_resnet26d_224,679.21,563.967,384,224,101.4
resnetaa50d,678.8,564.84,384,224,25.58
nf_ecaresnet50,673.73,568.984,384,224,25.56
hrnet_w18_small_v2,672.52,758.874,512,224,15.6
gluon_resnet50_v1s,671.98,570.574,384,224,25.68
efficientnet_b0_g8_gn,661.67,385.796,256,224,6.56
seresnet50t,659.6,581.03,384,224,28.1
efficientnet_b3_pruned,657.34,387.781,256,300,9.86
haloregnetz_b,656.48,388.419,256,224,11.68
tv_resnext50_32x4d,651.27,588.791,384,224,25.03
swsl_resnext50_32x4d,651.11,588.925,384,224,25.03
gluon_resnext50_32x4d,650.97,589.059,384,224,25.03
ssl_resnext50_32x4d,650.9,589.136,384,224,25.03
resnext50_32x4d,649.99,589.957,384,224,25.03
resnetrs50,648.71,590.781,384,224,35.69
densenet121d,646.33,394.275,256,224,8.0
regnetx_032,643.77,595.296,384,224,15.3
resnetblur50d,642.77,397.429,256,224,25.58
res2net50_26w_4s,636.71,601.86,384,224,25.7
swin_tiny_patch4_window7_224,635.13,402.044,256,224,28.29
skresnet50,634.96,603.369,384,224,25.8
poolformer_s24,634.53,402.132,256,224,21.39
gmlp_s16_224,632.58,301.939,192,224,19.42
ese_vovnet57b,632.35,606.334,384,224,38.61
crossvit_small_240,625.73,407.457,256,240,26.86
xcit_small_12_p16_224_dist,625.08,407.723,256,224,26.25
xcit_small_12_p16_224,624.67,407.992,256,224,26.25
densenetblur121d,617.78,412.569,256,224,8.0
ecaresnet50d,612.94,625.548,384,224,25.58
tf_efficientnet_b2_ns,610.27,313.114,192,260,9.11
tf_efficientnet_b2_ap,610.1,313.168,192,260,9.11
tf_efficientnet_b2,609.89,313.274,192,260,9.11
mixnet_l,603.6,634.707,384,224,7.33
gluon_inception_v3,601.71,636.771,384,299,23.83
inception_v3,601.07,637.475,384,299,23.83
adv_inception_v3,601.02,637.509,384,299,23.83
tf_inception_v3,600.65,637.912,384,299,23.83
sehalonet33ts,599.96,425.883,256,256,13.69
seresnetaa50d,599.53,425.823,256,224,28.11
resnext50d_32x4d,598.58,426.825,256,224,25.05
mobilevit_s,596.24,320.895,192,256,5.58
resnetv2_50x1_bit_distilled,588.8,325.252,192,224,25.55
gcresnet50t,583.98,436.858,256,256,25.9
skresnet50d,583.54,437.238,256,224,25.82
swin_s3_tiny_224,576.57,442.971,256,224,28.33
seresnext50_32x4d,576.37,443.036,256,224,27.56
gluon_seresnext50_32x4d,576.3,443.051,256,224,27.56
tf_mixnet_l,576.22,442.743,256,224,7.33
legacy_seresnext50_32x4d,575.52,443.681,256,224,27.56
res2next50,575.07,443.835,256,224,24.67
repvgg_b1g4,572.25,893.649,512,224,39.97
cspresnext50_iabn,572.06,669.169,384,256,20.57
convnext_tiny_hnfd,570.41,447.777,256,224,28.63
res2net50_14w_8s,569.05,447.639,256,224,25.06
resnest50d_1s4x24d,568.93,448.655,256,224,25.68
cait_xxs24_224,568.71,447.7,256,224,11.96
crossvit_15_240,567.12,336.74,192,240,27.53
semobilevit_s,566.94,337.375,192,256,5.74
densenet169,563.82,451.519,256,224,14.15
efficientnet_b2,561.37,340.497,192,288,9.11
efficientnet_cc_b1_8e,559.34,455.785,256,240,39.72
efficientnet_b2a,558.58,342.255,192,288,9.11
darknet53,556.85,458.909,256,256,41.61
dla60_res2net,555.54,459.405,256,224,20.85
dla60x,553.4,461.598,256,224,17.35
mixer_b16_224,552.46,462.598,256,224,59.88
nf_resnet101,550.61,695.793,384,224,44.55
tf_efficientnet_cc_b1_8e,549.81,463.949,256,240,39.72
regnetx_040,549.5,697.703,384,224,22.12
mixer_b16_224_miil,549.25,465.267,256,224,59.88
crossvit_15_dagger_240,547.66,348.734,192,240,28.21
nf_regnet_b3,547.65,465.691,256,320,18.59
vovnet57a,547.59,934.12,512,224,36.64
resnetv2_101,544.49,468.639,256,224,44.54
xcit_nano_12_p8_224,542.16,470.425,256,224,3.05
xcit_nano_12_p8_224_dist,541.55,470.928,256,224,3.05
tf_efficientnetv2_b3,541.54,352.751,192,300,14.36
sebotnet33ts_256,540.04,236.225,128,256,13.7
gcresnext50ts,538.97,354.583,192,256,15.67
nf_resnet50,536.27,715.168,384,288,25.56
resnet50_gn,532.97,359.41,192,224,25.56
vit_base_r26_s32_224,532.84,359.064,192,224,101.38
vit_base_patch32_384,531.15,481.156,256,384,88.3
efficientnetv2_rw_t,527.04,362.193,192,288,13.65
resnet101,526.31,484.87,256,224,44.55
gluon_resnet101_v1b,525.74,485.353,256,224,44.55
tv_resnet101,523.69,487.267,256,224,44.55
vit_large_patch32_224,515.11,495.389,256,224,306.54
vit_base_resnet50d_224,514.44,495.989,256,224,110.97
mixer_l32_224,507.93,376.544,192,224,206.94
resnetv2_101d,506.46,503.899,256,224,44.56
swin_v2_cr_tiny_224,505.66,378.391,192,224,28.33
vit_tiny_patch16_384,505.36,252.456,128,384,5.79
resmlp_36_224,505.0,378.005,192,224,44.69
resmlp_36_distilled_224,504.83,378.088,192,224,44.69
swin_v2_cr_tiny_ns_224,503.02,380.384,192,224,28.33
repvgg_b1,501.28,1020.314,512,224,57.42
dla60_res2next,500.08,510.484,256,224,17.03
gluon_resnet101_v1c,498.53,511.943,256,224,44.57
wide_resnet50_2,491.97,779.7,384,224,68.88
gluon_resnet101_v1d,491.71,519.004,256,224,44.57
resnest50d,484.85,526.671,256,224,27.48
vgg13,482.75,795.25,384,224,133.05
cspdarknet53,481.82,530.306,256,256,27.64
gc_efficientnetv2_rw_t,480.93,396.503,192,288,13.68
convnext_small,478.98,399.137,192,224,50.22
efficientnet_lite3,478.82,266.239,128,300,8.2
ecaresnet26t,478.49,534.465,256,320,16.01
nest_tiny,477.21,267.341,128,224,17.06
regnetz_b16,476.53,401.488,192,288,9.72
dla102,475.31,537.042,256,224,33.27
cspdarknet53_iabn,474.61,806.628,384,256,27.64
res2net50_26w_6s,474.4,537.903,256,224,37.05
jx_nest_tiny,469.53,271.701,128,224,17.06
twins_pcpvt_base,465.48,409.697,192,224,43.83
vgg13_bn,465.35,549.842,256,224,133.05
halonet50ts,463.11,413.595,192,256,22.73
regnetx_080,462.29,829.538,384,224,39.57
lambda_resnet50ts,461.63,414.909,192,256,21.54
coat_lite_small,461.47,414.609,192,224,19.84
legacy_seresnet101,460.47,553.804,256,224,49.33
vgg16,459.25,557.204,256,224,138.36
tf_efficientnet_lite3,458.84,277.849,128,300,8.2
resnetaa101d,458.35,556.916,256,224,44.57
gluon_resnet101_v1s,456.37,559.352,256,224,44.67
xcit_tiny_12_p16_384_dist,451.6,423.4,192,384,6.72
seresnet101,447.82,569.466,256,224,49.33
densenet201,447.49,426.067,192,224,20.01
mixnet_xl,447.09,570.692,256,224,11.9
nf_seresnet101,444.89,573.162,256,224,49.33
convit_small,441.84,433.517,192,224,27.78
resnetblur101d,441.53,578.26,256,224,44.57
nfnet_l0,426.47,599.103,256,288,35.07
skresnext50_32x4d,424.37,601.881,256,224,27.48
poolformer_s36,424.29,450.68,192,224,30.86
botnet50ts_256,421.6,302.649,128,256,22.74
halo2botnet50ts_256,418.8,457.379,192,256,22.64
gluon_resnext101_32x4d,418.26,610.512,256,224,44.18
resnext101_32x4d,417.6,611.486,256,224,44.18
ssl_resnext101_32x4d,417.52,611.578,256,224,44.18
swsl_resnext101_32x4d,417.45,611.654,256,224,44.18
fbnetv3_g,415.62,305.885,128,288,16.62
twins_svt_base,413.84,461.835,192,224,56.07
ese_vovnet39b_evos,413.01,308.979,128,224,24.58
res2net101_26w_4s,405.25,629.29,256,224,45.21
eca_nfnet_l0,404.73,631.533,256,288,24.14
tresnet_l,403.54,1265.42,512,224,55.99
lamhalobotnet50ts_256,403.43,474.897,192,256,22.57
volo_d1_224,400.45,478.074,192,224,26.63
crossvit_18_240,400.32,317.735,128,240,43.27
resnet51q,397.98,481.556,192,288,35.7
nf_ecaresnet101,396.22,644.266,256,224,44.55
dla102x,392.86,487.139,192,224,26.31
vit_base_patch16_224_miil,392.0,489.026,192,224,86.54
swin_small_patch4_window7_224,391.85,488.136,192,224,49.61
regnety_032,391.81,651.958,256,288,19.44
regnetx_064,390.82,654.137,256,224,26.21
res2net50_26w_8s,389.26,655.478,256,224,48.4
vgg16_bn,388.47,658.66,256,224,138.37
deit_base_patch16_224,386.56,495.833,192,224,86.57
crossvit_18_dagger_240,386.08,329.466,128,240,44.27
vit_base_patch16_224,385.82,496.783,192,224,86.57
resnest50d_4s2x40d,385.78,662.242,256,224,30.42
xception,384.95,331.72,128,299,22.86
vit_base_patch16_224_sam,384.94,497.961,192,224,86.57
ecaresnet101d,381.61,669.061,256,224,44.57
deit_base_distilled_patch16_224,379.86,504.583,192,224,87.34
resnetv2_152,379.0,673.236,256,224,60.19
repvgg_b2g4,377.95,1353.629,512,224,61.76
ese_vovnet99b,373.94,683.067,256,224,63.2
vit_small_resnet50d_s16_224,373.61,512.677,192,224,57.53
cait_xxs36_224,371.71,512.777,192,224,17.3
resnet152,371.52,514.561,192,224,60.19
tv_resnet152,371.15,515.007,192,224,60.19
gluon_resnet152_v1b,368.93,518.118,192,224,60.19
gluon_seresnext101_32x4d,368.19,519.228,192,224,48.96
seresnext101_32x4d,367.5,520.242,192,224,48.96
nfnet_f0,366.97,696.402,256,256,71.49
legacy_seresnext101_32x4d,366.06,522.375,192,224,48.96
tf_efficientnet_b3_ap,364.58,349.402,128,300,12.23
tf_efficientnet_b3_ns,364.49,349.53,128,300,12.23
tf_efficientnet_b3,364.34,349.633,128,300,12.23
resnetv2_152d,361.2,529.264,192,224,60.2
resnet61q,358.48,356.024,128,288,36.85
resnetv2_50d_frn,357.45,356.923,128,224,25.59
ese_vovnet99b_iabn,357.31,1071.637,384,224,63.2
gluon_resnet152_v1c,355.88,537.206,192,224,60.21
hrnet_w18,355.73,714.824,256,224,21.3
efficientnet_b3,355.08,358.849,128,320,12.23
efficientnet_b3a,354.83,359.115,128,320,12.23
regnety_040,354.78,539.617,192,288,20.65
beit_base_patch16_224,353.55,541.973,192,224,86.53
gluon_resnet152_v1d,353.52,540.757,192,224,60.21
regnety_040s_gn,353.11,360.919,128,224,20.65
vgg19,352.0,1090.659,384,224,143.67
xcit_tiny_12_p8_224,351.32,362.59,128,224,6.71
xcit_tiny_12_p8_224_dist,351.06,362.838,128,224,6.71
xception41p,343.38,371.913,128,299,26.91
regnetv_040,342.33,372.402,128,288,20.64
tnt_s_patch16_224,339.73,563.196,192,224,23.76
repvgg_b2,339.2,1508.352,512,224,89.02
vgg19_bn,336.08,761.352,256,224,143.68
gluon_resnet152_v1s,334.98,570.781,192,224,60.32
densenet161,332.41,382.595,128,224,28.68
dm_nfnet_f0,331.82,770.266,256,256,71.49
dla169,331.14,577.405,192,224,53.39
resnetv2_50d_gn,330.91,385.985,128,288,25.57
convnext_base_in22ft1k,328.04,388.471,128,224,88.59
convnext_tiny_in22ft1k,327.7,388.845,128,224,88.59
convnext_small_in22ft1k,326.36,390.508,128,224,88.59
convnext_base,325.78,391.109,128,224,88.59
xcit_small_24_p16_224_dist,322.26,393.856,128,224,47.67
xcit_small_24_p16_224,321.22,395.027,128,224,47.67
repvgg_b3g4,321.08,1194.912,384,224,83.83
pit_b_224,319.89,399.203,128,224,73.76
twins_pcpvt_large,319.03,397.109,128,224,60.99
pit_b_distilled_224,318.55,400.853,128,224,74.79
legacy_seresnet152,315.59,605.069,192,224,66.82
dpn92,314.54,812.398,256,224,37.67
inception_v4,312.24,612.734,192,299,42.68
regnetx_120,309.13,827.171,256,224,46.11
hrnet_w32,308.88,823.922,256,224,41.23
convmixer_1024_20_ks9_p14,308.12,830.025,256,224,24.38
ecaresnet50t,306.63,416.493,128,320,25.57
seresnet152,305.84,415.318,128,224,66.82
coat_tiny,303.51,419.779,128,224,5.5
vit_small_patch16_36x1_224,303.33,419.341,128,224,64.67
regnetz_c16,302.72,421.37,128,320,13.46
vit_small_patch16_18x2_224,302.0,421.199,128,224,64.67
hrnet_w30,301.17,845.104,256,224,37.71
swin_v2_cr_small_224,300.16,424.024,128,224,49.7
nest_small,296.43,322.186,96,224,38.35
tresnet_xl,295.35,1296.648,384,224,78.44
jx_nest_small,294.56,324.278,96,224,38.35
efficientnet_el,291.47,438.031,128,300,10.59
xception41,291.46,437.936,128,299,26.97
efficientnet_el_pruned,291.16,438.564,128,300,10.59
regnety_120,291.14,658.173,192,224,51.82
cait_s24_224,290.55,438.065,128,224,46.92
nf_regnet_b4,288.27,441.948,128,384,30.21
twins_svt_large,288.14,442.123,128,224,99.27
wide_resnet101_2,287.9,665.343,192,224,126.89
mixnet_xxl,287.84,442.746,128,224,23.96
tf_efficientnet_el,286.59,445.507,128,300,10.59
poolformer_m36,284.22,448.517,128,224,56.17
swin_s3_small_224,277.99,343.436,96,224,49.74
repvgg_b3,276.27,1388.871,384,224,123.09
swin_base_patch4_window7_224,273.38,466.273,128,224,87.77
gmlp_b16_224,266.77,358.289,96,224,73.08
gluon_resnext101_64x4d,266.55,478.624,128,224,83.46
dla102x2,265.99,479.601,128,224,41.28
resnet200,264.41,481.093,128,224,64.67
resnetv2_50d_evob,261.14,366.354,96,224,25.59
regnetx_160,260.8,735.124,192,224,54.28
xception65p,258.86,493.161,128,299,39.82
inception_resnet_v2,255.7,747.606,192,299,55.84
ens_adv_inception_resnet_v2,255.53,748.108,192,299,55.84
resnetrs101,253.22,503.169,128,288,63.62
resnext101_32x8d,252.1,506.144,128,224,88.79
ig_resnext101_32x8d,251.82,506.789,128,224,88.79
ssl_resnext101_32x8d,251.44,507.525,128,224,88.79
swsl_resnext101_32x8d,251.43,507.504,128,224,88.79
crossvit_base_240,250.95,380.881,96,240,105.03
dpn98,249.4,511.692,128,224,61.57
efficientnet_lite4,247.4,257.372,64,380,13.01
coat_mini,246.33,517.632,128,224,10.34
efficientnetv2_s,245.94,388.167,96,384,21.46
gluon_seresnext101_64x4d,243.95,522.441,128,224,88.23
tf_efficientnetv2_s_in21ft1k,243.54,391.981,96,384,21.46
tf_efficientnetv2_s,242.89,393.02,96,384,21.46
resnet101d,240.52,530.628,128,320,44.57
resnest101e,240.15,530.415,128,256,48.28
tf_efficientnet_lite4,239.36,265.97,64,380,13.01
vit_small_patch16_384,235.44,270.99,64,384,22.2
xcit_tiny_24_p16_384_dist,233.56,407.54,96,384,12.12
efficientnetv2_rw_s,232.48,273.03,64,384,23.94
regnety_064,232.21,549.529,128,288,30.58
xcit_medium_24_p16_224_dist,231.18,550.33,128,224,84.4
xcit_medium_24_p16_224,231.11,550.432,128,224,84.4
vit_large_r50_s32_224,229.5,415.85,96,224,328.99
regnetv_064,225.87,564.974,128,288,30.58
vit_small_r26_s32_384,225.43,282.622,64,384,36.47
convit_base,225.0,567.909,128,224,86.54
gluon_xception65,223.32,427.945,96,299,39.92
xception65,222.42,429.727,96,299,39.92
tnt_b_patch16_224,222.32,573.844,128,224,65.41
volo_d2_224,221.58,431.459,96,224,58.68
regnety_080,221.2,577.488,128,288,39.18
hrnet_w40,220.15,867.238,192,224,57.56
swin_s3_base_224,219.92,433.728,96,224,71.13
xcit_small_12_p16_384_dist,216.0,442.656,96,384,26.25
swin_v2_cr_base_224,214.24,445.643,96,224,87.88
hrnet_w48,213.16,895.867,192,224,77.47
resnetv2_50d_evos,211.1,226.199,48,288,25.59
nest_base,210.29,302.692,64,224,67.72
jx_nest_base,209.17,304.343,64,224,67.72
vit_base_r50_s16_224,208.73,458.333,96,224,98.66
tresnet_m_448,206.95,924.987,192,448,31.39
hrnet_w44,206.72,924.031,192,224,67.06
efficientnet_b4,203.38,312.633,64,384,19.34
regnetz_b16_evos,201.27,316.106,64,288,9.74
regnetz_040h,199.85,318.401,64,320,28.94
regnetz_040,199.66,318.691,64,320,27.12
efficientnet_b3_gn,199.47,319.133,64,320,11.73
densenet264,199.22,477.999,96,224,72.69
regnetz_d8,197.46,322.524,64,320,23.37
eca_nfnet_l1,189.9,672.191,128,320,41.41
tf_efficientnet_b4,188.81,336.904,64,380,19.34
tf_efficientnet_b4_ns,188.45,337.501,64,380,19.34
tf_efficientnet_b4_ap,188.4,337.642,64,380,19.34
poolformer_m48,187.63,509.206,96,224,73.47
dpn131,186.23,685.159,128,224,79.25
regnetz_d32,186.08,342.366,64,320,27.58
xcit_nano_12_p8_384_dist,183.21,347.477,64,384,3.05
convnext_large_in22ft1k,182.13,525.349,96,224,197.77
convnext_large,180.72,529.451,96,224,197.77
xcit_tiny_24_p8_224_dist,179.19,532.437,96,224,12.11
xcit_tiny_24_p8_224,179.14,532.524,96,224,12.11
dpn107,174.56,731.55,128,224,86.92
resnet152d,172.45,554.35,96,320,60.21
hrnet_w64,170.75,744.856,128,224,128.06
halonet_h1,170.55,373.795,64,256,8.1
xception71,168.15,378.514,64,299,42.34
mixer_l16_224,167.46,571.737,96,224,208.2
regnety_320,166.3,768.299,128,224,145.05
vit_large_patch32_384,165.23,579.454,96,384,306.63
densenet264d_iabn,165.07,1158.795,192,224,72.74
xcit_small_12_p8_224,164.33,387.646,64,224,26.21
xcit_small_12_p8_224_dist,164.16,388.107,64,224,26.21
efficientnet_b3_g8_gn,153.99,413.949,64,320,14.25
swin_large_patch4_window7_224,152.41,417.988,64,224,196.53
volo_d3_224,152.25,417.779,64,224,86.33
ecaresnet200d,150.97,632.522,96,256,64.69
seresnet200d,149.9,422.786,64,256,71.86
regnetx_320,146.61,871.937,128,224,107.81
seresnet152d,143.56,442.443,64,320,66.84
resnetv2_50x1_bitm,142.06,337.009,48,448,25.55
gluon_senet154,141.6,674.51,96,224,115.09
resnetrs152,141.55,448.821,64,320,86.62
senet154,141.16,676.729,96,224,115.09
legacy_senet154,139.88,683.0,96,224,115.09
regnety_160,136.04,704.372,96,288,83.59
xcit_large_24_p16_224_dist,130.76,486.175,64,224,189.1
xcit_large_24_p16_224,130.33,487.671,64,224,189.1
seresnext101_32x8d,126.87,502.294,64,288,93.57
nfnet_f1,125.34,763.802,96,320,132.63
resnet200d,124.61,510.654,64,320,64.69
regnetz_c16_evos,123.93,385.448,48,320,13.49
resnext101_64x4d,122.56,781.647,96,288,83.46
efficientnetv2_m,120.01,396.838,48,416,54.14
swin_v2_cr_large_224,119.95,397.782,48,224,196.68
xcit_tiny_12_p8_384_dist,119.32,400.563,48,384,6.71
vit_large_patch16_224,116.44,548.026,64,224,304.33
crossvit_15_dagger_408,116.23,273.461,32,408,28.5
seresnet269d,116.02,545.703,64,256,113.67
vit_base_patch16_18x2_224,115.21,552.796,64,224,256.73
convnext_xlarge_in22ft1k,113.61,561.618,64,224,350.2
convnext_base_384_in22ft1k,112.88,423.483,48,384,88.59
dm_nfnet_f1,112.71,565.567,64,320,132.63
convnext_tiny_384_in22ft1k,112.65,424.398,48,384,88.59
convnext_small_384_in22ft1k,112.59,424.604,48,384,88.59
nf_regnet_b5,112.22,567.655,64,456,49.74
swin_v2_cr_tiny_384,111.16,286.572,32,384,28.33
xcit_small_24_p16_384_dist,110.42,431.325,48,384,47.67
beit_large_patch16_224,107.44,593.678,64,224,304.43
regnetz_e8,103.46,461.955,48,320,57.7
efficientnetv2_rw_m,103.11,307.008,32,416,53.24
tresnet_l_448,101.52,1257.535,128,448,55.99
swsl_resnext101_32x16d,99.54,962.862,96,224,194.03
ig_resnext101_32x16d,99.3,965.129,96,224,194.03
ssl_resnext101_32x16d,99.25,965.706,96,224,194.03
volo_d1_384,98.94,322.06,32,384,26.78
resnetrs200,98.6,482.325,48,320,93.21
cait_xxs24_384,97.24,491.195,48,384,12.03
deit_base_patch16_384,95.92,332.748,32,384,86.86
vit_base_patch16_384,95.88,332.951,32,384,86.86
volo_d4_224,95.4,500.622,48,224,192.96
eca_nfnet_l2,94.36,675.671,64,384,56.72
deit_base_distilled_patch16_384,93.97,339.685,32,384,87.63
efficientnet_b5,90.42,351.458,32,456,30.39
tf_efficientnetv2_m,89.36,354.724,32,480,54.14
tf_efficientnetv2_m_in21ft1k,89.12,355.723,32,480,54.14
tf_efficientnet_b5,88.7,358.215,32,456,30.39
tf_efficientnet_b5_ap,88.68,358.368,32,456,30.39
tf_efficientnet_b5_ns,88.58,358.774,32,456,30.39
crossvit_18_dagger_408,87.86,362.177,32,408,44.61
resnetv2_101x1_bitm,87.29,364.971,32,448,44.54
convmixer_768_32,87.13,1100.537,96,224,21.11
resnetv2_152x2_bit_teacher,87.1,364.899,32,224,236.34
vit_large_patch14_224,84.6,565.739,48,224,304.2
regnetz_d8_evos,83.97,379.015,32,320,23.46
beit_base_patch16_384,82.88,385.038,32,384,86.74
xcit_small_24_p8_224_dist,82.62,383.85,32,224,47.63
xcit_small_24_p8_224,82.5,384.488,32,224,47.63
resnest200e,79.64,597.77,48,320,70.2
tresnet_xl_448,77.44,1236.244,96,448,78.44
xcit_medium_24_p16_384_dist,76.61,414.275,32,384,84.4
vit_large_r50_s32_384,76.27,417.132,32,384,329.09
swin_base_patch4_window12_384,73.39,434.11,32,384,87.9
nfnet_f2,71.5,668.121,48,352,193.78
resmlp_big_24_224_in22ft1k,68.15,468.085,32,224,129.14
resmlp_big_24_224,68.13,468.141,32,224,129.14
resmlp_big_24_distilled_224,68.08,468.542,32,224,129.14
pnasnet5large,66.87,474.686,32,331,86.06
swin_v2_cr_small_384,66.28,359.629,24,384,49.7
nasnetalarge,65.59,482.923,32,331,88.75
cait_xs24_384,65.34,487.167,32,384,26.67
dm_nfnet_f2,64.55,740.206,48,352,193.78
cait_xxs36_384,62.84,505.392,32,384,17.37
vit_base_patch8_224,62.83,381.142,24,224,86.58
convnext_large_384_in22ft1k,61.61,517.714,32,384,197.77
ecaresnet269d,61.52,515.695,32,352,102.09
volo_d5_224,61.49,517.153,32,224,295.46
xcit_tiny_24_p8_384_dist,60.46,525.849,32,384,12.11
xcit_medium_24_p8_224,59.24,536.775,32,224,84.32
xcit_medium_24_p8_224_dist,59.23,536.852,32,224,84.32
vit_base_resnet50_384,58.93,405.608,24,384,98.95
vit_base_r50_s16_384,58.91,405.68,24,384,98.95
resnetrs270,58.9,537.38,32,352,129.86
xcit_small_12_p8_384_dist,56.03,426.632,24,384,26.21
volo_d2_384,55.33,287.363,16,384,58.87
ig_resnext101_32x32d,52.68,605.908,32,224,468.53
eca_nfnet_l3,50.62,628.636,32,448,72.04
convmixer_1536_20,49.63,966.338,48,224,51.63
cait_s24_384,49.12,486.034,24,384,47.06
efficientnet_b6,48.5,327.112,16,528,43.04
tf_efficientnet_b6_ns,48.02,330.391,16,528,43.04
tf_efficientnet_b6_ap,47.83,331.608,16,528,43.04
tf_efficientnet_b6,47.78,331.966,16,528,43.04
efficientnetv2_l,47.17,334.927,16,480,118.52
swin_v2_cr_base_384,47.07,337.445,16,384,87.88
tf_efficientnetv2_l_in21ft1k,46.85,337.127,16,480,118.52
tf_efficientnetv2_l,46.53,339.329,16,480,118.52
swin_v2_cr_huge_224,46.2,343.813,16,224,657.83
xcit_large_24_p16_384_dist,44.95,530.527,24,384,189.1
swin_large_patch4_window12_384,41.24,386.07,16,384,196.74
vit_huge_patch14_224,39.65,401.44,16,224,632.05
resnetrs350,37.15,638.194,24,384,163.96
convnext_xlarge_384_in22ft1k,36.94,431.409,16,384,350.2
nfnet_f3,35.43,673.367,24,416,254.92
resnest269e,33.7,705.124,24,416,110.93
xcit_large_24_p8_224_dist,33.34,476.606,16,224,188.93
xcit_large_24_p8_224,33.33,476.71,16,224,188.93
dm_nfnet_f3,32.28,738.882,24,416,254.92
resnetv2_50x3_bitm,31.96,499.813,16,448,217.32
cait_s36_384,31.71,500.682,16,384,68.37
resnetv2_152x2_bit_teacher_384,31.41,506.876,16,384,236.34
tf_efficientnetv2_xl_in21ft1k,31.07,380.33,12,512,208.12
efficientnetv2_xl,30.57,386.62,12,512,208.12
efficientnet_b7,30.54,258.47,8,600,66.35
tf_efficientnet_b7,30.24,261.049,8,600,66.35
tf_efficientnet_b7_ap,30.18,261.545,8,600,66.35
tf_efficientnet_b7_ns,30.17,261.527,8,600,66.35
vit_large_patch16_384,29.1,410.723,12,384,304.72
xcit_small_24_p8_384_dist,28.47,558.499,16,384,47.63
swin_v2_cr_large_384,28.33,421.122,12,384,196.68
ig_resnext101_32x48d,27.82,573.581,16,224,828.41
resnetrs420,25.59,615.408,16,416,191.89
beit_large_patch16_384,24.96,478.806,12,384,305.0
volo_d3_448,23.79,333.801,8,448,86.63
vit_giant_patch14_224,22.41,354.342,8,224,1012.61
resnetv2_152x2_bitm,21.79,364.675,8,448,236.34
xcit_medium_24_p8_384_dist,19.42,408.574,8,384,84.32
nfnet_f4,18.89,630.122,12,512,316.07
resnetv2_101x3_bitm,17.61,452.554,8,448,387.93
dm_nfnet_f4,17.17,693.324,12,512,316.07
volo_d4_448,16.84,353.766,6,448,193.41
efficientnet_b8,14.4,412.697,6,672,87.41
tf_efficientnet_b8_ap,14.32,415.19,6,672,87.41
tf_efficientnet_b8,14.24,417.31,6,672,87.41
nfnet_f5,12.31,643.8,8,544,377.21
cait_m36_384,11.75,507.058,6,384,271.22
xcit_large_24_p8_384_dist,11.36,524.55,6,384,188.93
dm_nfnet_f5,11.21,706.654,8,544,377.21
volo_d5_448,11.01,359.686,4,448,295.91
swin_v2_cr_huge_384,10.89,364.734,4,384,657.94
tf_efficientnet_l2_ns_475,10.43,377.703,4,475,480.31
nfnet_f6,10.18,778.679,8,576,438.36
beit_large_patch16_512,9.37,425.057,4,512,305.67
dm_nfnet_f6,8.56,692.978,6,576,438.36
volo_d5_512,7.76,383.588,3,512,296.09
nfnet_f7,7.54,787.101,6,608,499.5
cait_m48_448,4.71,419.695,2,448,356.46
resnetv2_152x4_bitm,4.53,438.64,2,480,936.53
tf_efficientnet_l2_ns,2.96,331.907,1,800,480.31
efficientnet_l2,2.92,336.419,1,800,480.31
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nchw-pt112-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,10001.12,50.423,512,106,2.04
mobilenetv3_small_050,7406.47,68.392,512,224,1.59
tf_mobilenetv3_small_minimal_100,6438.14,78.983,512,224,2.04
mobilenetv3_small_075,6186.83,82.006,512,224,2.04
tf_mobilenetv3_small_075,5783.46,87.782,512,224,2.04
mobilenetv3_small_100,5749.13,88.315,512,224,2.54
lcnet_035,5673.53,89.75,512,224,1.64
tf_mobilenetv3_small_100,5383.9,94.36,512,224,2.54
levit_128s,5298.88,95.701,512,224,7.78
lcnet_050,5280.37,96.452,512,224,1.88
tinynet_d,5161.83,98.416,512,152,2.34
mixer_s32_224,4696.33,108.475,512,224,19.1
resnet10t,4669.46,109.393,512,176,5.44
vit_small_patch32_224,4447.28,114.289,512,224,22.88
lcnet_075,4278.23,119.175,512,224,2.36
vit_tiny_r_s16_p8_224,4137.87,122.895,512,224,6.34
levit_128,3895.0,130.318,512,224,9.21
regnetx_002,3718.05,137.026,512,224,2.68
lcnet_100,3569.0,142.969,512,224,2.95
mnasnet_small,3450.28,147.453,512,224,2.03
regnety_002,3414.18,149.006,512,224,3.16
cs3darknet_focus_s,3251.91,156.949,512,256,3.27
mobilenetv2_035,3160.04,161.202,512,224,1.68
levit_192,3046.5,166.9,512,224,10.95
gernet_s,3034.31,168.028,512,224,8.17
tinynet_c,2919.98,174.314,512,184,2.46
mnasnet_050,2847.14,179.025,512,224,2.22
cs3darknet_s,2821.27,180.951,512,256,3.28
resnet18,2764.22,184.877,512,224,11.69
ssl_resnet18,2760.71,185.109,512,224,11.69
mobilenetv2_050,2751.58,185.257,512,224,1.97
swsl_resnet18,2742.47,186.338,512,224,11.69
semnasnet_050,2741.67,185.816,512,224,2.08
gluon_resnet18_v1b,2741.53,186.395,512,224,11.69
lcnet_150,2713.5,188.193,512,224,4.5
regnetx_004,2695.23,188.875,512,224,5.16
ese_vovnet19b_slim_dw,2588.37,197.313,512,224,1.9
seresnet18,2562.51,199.293,512,224,11.78
nf_regnet_b0,2561.76,198.646,512,192,8.76
legacy_seresnet18,2500.8,204.207,512,224,11.78
tf_efficientnetv2_b0,2483.22,204.949,512,192,7.14
levit_256,2482.39,205.091,512,224,18.89
mobilenetv3_large_075,2392.41,213.119,512,224,3.99
resnet14t,2385.69,214.281,512,176,10.08
tf_mobilenetv3_large_minimal_100,2347.68,217.368,512,224,3.92
vit_tiny_patch16_224,2293.54,222.408,512,224,5.72
regnetx_006,2293.09,222.433,512,224,6.2
deit_tiny_patch16_224,2290.53,222.68,512,224,5.72
tf_mobilenetv3_large_075,2259.6,225.688,512,224,3.99
deit_tiny_distilled_patch16_224,2253.36,226.358,512,224,5.91
edgenext_xx_small,2231.33,228.598,512,256,1.33
ghostnet_050,2189.91,232.414,512,224,2.59
mobilenetv3_rw,2184.31,233.512,512,224,5.48
mnasnet_075,2176.02,234.492,512,224,3.17
mobilenetv3_large_100,2167.29,235.344,512,224,5.48
mobilenetv3_large_100_miil,2165.63,235.504,512,224,5.48
levit_256d,2159.5,235.516,512,224,26.21
resnet18d,2129.12,240.084,512,224,11.71
hardcorenas_a,2118.32,240.968,512,224,5.26
regnety_004,2100.99,242.536,512,224,4.34
pit_ti_distilled_224,2086.5,244.504,512,224,5.1
pit_ti_224,2079.54,245.311,512,224,4.85
ese_vovnet19b_slim,2066.1,247.446,512,224,3.17
mnasnet_100,2053.84,248.477,512,224,4.38
tf_mobilenetv3_large_100,2053.63,248.437,512,224,5.48
mnasnet_b1,2053.54,248.485,512,224,4.38
semnasnet_075,2008.51,253.986,512,224,2.91
hardcorenas_b,2008.46,253.96,512,224,5.18
mobilenetv2_075,1983.69,257.32,512,224,2.64
hardcorenas_c,1977.37,257.94,512,224,5.52
xcit_nano_12_p16_224_dist,1970.62,258.036,512,224,3.05
xcit_nano_12_p16_224,1969.78,258.084,512,224,3.05
tinynet_b,1965.95,259.368,512,188,3.73
hardcorenas_d,1880.3,271.085,512,224,7.5
tf_efficientnetv2_b1,1876.23,271.395,512,192,8.14
resnetblur18,1872.21,273.11,512,224,11.69
spnasnet_100,1862.13,273.955,512,224,4.42
mnasnet_a1,1859.21,274.476,512,224,3.89
semnasnet_100,1857.75,274.693,512,224,3.89
mobilenetv2_100,1832.14,278.633,512,224,3.5
regnety_006,1809.24,281.912,512,224,6.06
visformer_tiny,1802.41,283.384,512,224,10.32
mixer_b32_224,1784.58,286.101,512,224,60.29
skresnet18,1730.13,295.275,512,224,11.96
tinynet_a,1710.13,298.117,512,192,6.19
vit_base_patch32_224_sam,1703.64,299.668,512,224,88.22
vit_base_patch32_224,1703.57,299.695,512,224,88.22
efficientnet_lite0,1674.68,304.971,512,224,4.65
cs3darknet_focus_m,1668.48,306.209,512,256,9.3
hardcorenas_e,1650.74,309.021,512,224,8.07
hardcorenas_f,1646.88,309.777,512,224,8.2
gluon_resnet34_v1b,1634.03,312.731,512,224,21.8
regnetx_008,1632.2,312.851,512,224,7.26
tv_resnet34,1630.02,313.513,512,224,21.8
resnet34,1622.41,314.992,512,224,21.8
ghostnet_100,1601.5,318.319,512,224,5.18
tf_efficientnet_lite0,1591.79,320.884,512,224,4.65
fbnetc_100,1567.77,325.605,512,224,5.57
pit_xs_distilled_224,1551.83,329.02,512,224,11.0
pit_xs_224,1549.02,329.642,512,224,10.62
mixer_s16_224,1543.23,331.197,512,224,18.53
dla46_c,1532.94,333.18,512,224,1.3
mnasnet_140,1525.17,334.879,512,224,7.12
seresnet34,1505.77,339.147,512,224,21.96
cs3darknet_m,1499.82,340.716,512,256,9.31
regnety_008,1498.63,340.596,512,224,6.26
levit_384,1491.26,342.207,512,224,39.13
edgenext_x_small,1481.71,344.446,512,256,2.34
ese_vovnet19b_dw,1466.46,348.623,512,224,6.54
legacy_seresnet34,1465.81,348.38,512,224,21.96
efficientnet_b0,1459.11,262.1,384,224,5.29
gernet_m,1456.76,350.74,512,224,21.14
vit_small_patch32_384,1448.56,352.604,512,384,22.92
regnetz_005,1448.06,352.165,512,224,7.12
rexnet_100,1447.81,264.049,384,224,4.8
rexnetr_100,1441.71,265.216,384,224,4.88
nf_resnet26,1422.76,359.346,512,224,16.0
hrnet_w18_small,1410.43,361.614,512,224,13.19
selecsls42,1405.04,363.736,512,224,30.35
selecsls42b,1401.22,364.735,512,224,32.46
mobilenetv2_110d,1400.15,273.199,384,224,4.52
tf_efficientnet_b0_ap,1398.67,273.43,384,224,5.29
mobilevitv2_050,1396.45,365.664,512,256,1.37
tf_efficientnet_b0_ns,1395.54,274.064,384,224,5.29
tf_efficientnet_b0,1395.32,274.114,384,224,5.29
tf_efficientnetv2_b2,1392.9,365.948,512,208,10.1
vit_tiny_r_s16_p8_384,1392.75,274.873,384,384,6.36
resnet34d,1379.64,370.514,512,224,21.82
ghostnet_130,1364.55,373.824,512,224,7.36
gmixer_12_224,1352.72,377.701,512,224,12.7
crossvit_tiny_240,1349.19,377.902,512,240,7.01
gmlp_ti16_224,1340.6,284.894,384,224,5.87
semnasnet_140,1340.57,380.992,512,224,6.11
dla46x_c,1338.33,381.81,512,224,1.07
xcit_tiny_12_p16_224,1323.84,384.926,512,224,6.72
xcit_tiny_12_p16_224_dist,1317.19,386.895,512,224,6.72
resnetrs50,1317.01,387.565,512,160,35.69
mobilevit_xxs,1316.84,290.489,384,256,1.27
resnet26,1312.7,389.566,512,224,16.0
efficientnet_b1_pruned,1301.95,391.798,512,240,6.33
mobilenetv2_140,1267.4,302.189,384,224,6.11
dla60x_c,1262.98,404.404,512,224,1.32
crossvit_9_240,1260.08,303.33,384,240,8.55
convnext_nano_hnf,1235.34,413.703,512,224,15.59
convnext_nano_ols,1234.94,413.902,512,224,15.6
poolformer_s12,1234.11,414.201,512,224,11.92
convnext_nano,1233.61,414.261,512,224,15.59
resmlp_12_distilled_224,1232.37,414.645,512,224,15.35
resmlp_12_224,1232.04,414.762,512,224,15.35
fbnetv3_b,1226.89,415.617,512,224,8.6
nf_regnet_b2,1219.45,418.235,512,240,14.31
repvgg_b0,1217.24,419.512,512,224,15.82
selecsls60b,1214.07,420.825,512,224,32.77
selecsls60,1211.7,421.663,512,224,30.67
nf_regnet_b1,1209.03,421.975,512,256,10.22
crossvit_9_dagger_240,1206.16,316.906,384,240,8.78
nf_seresnet26,1198.39,426.558,512,224,17.4
mixnet_s,1181.75,431.958,512,224,4.13
nf_ecaresnet26,1174.85,435.233,512,224,16.0
efficientnet_lite1,1171.46,217.556,256,240,5.42
darknet17,1164.06,439.537,512,256,14.3
efficientnet_es_pruned,1160.76,440.317,512,224,5.44
efficientnet_es,1160.37,440.47,512,224,5.44
regnetx_016,1139.3,448.473,512,224,9.19
fbnetv3_d,1138.14,335.598,384,224,10.31
tf_efficientnet_es,1136.29,449.83,512,224,5.44
rexnetr_130,1133.04,224.76,256,224,7.61
dla34,1132.96,451.315,512,224,15.74
resnet26d,1119.56,456.822,512,224,16.01
tf_mixnet_s,1118.11,456.605,512,224,4.13
tf_efficientnet_lite1,1110.94,229.444,256,240,5.42
edgenext_small,1109.37,460.388,512,256,5.59
convit_tiny,1095.04,466.531,512,224,5.71
rexnet_130,1094.78,232.699,256,224,7.56
mobilenetv2_120d,1078.49,236.158,256,224,5.83
darknet21,1073.87,476.43,512,256,20.86
ecaresnet50d_pruned,1067.01,478.899,512,224,19.94
deit_small_patch16_224,1053.64,363.563,384,224,22.05
vit_small_patch16_224,1052.92,363.872,384,224,22.05
deit_small_distilled_patch16_224,1032.61,370.971,384,224,22.44
sedarknet21,1031.46,495.893,512,256,20.95
gernet_l,1030.31,496.058,512,256,31.08
efficientnet_b1,1030.3,246.963,256,224,7.79
rexnetr_150,1022.06,249.288,256,224,9.78
repvgg_a2,1010.18,506.008,512,224,28.21
edgenext_small_rw,1009.52,506.183,512,256,7.83
skresnet34,1008.96,506.323,512,224,22.28
resnest14d,979.06,522.497,512,224,10.61
cs3darknet_focus_l,977.57,391.957,384,256,21.15
deit3_small_patch16_224,977.26,391.961,384,224,22.06
deit3_small_patch16_224_in21ft1k,976.5,392.276,384,224,22.06
rexnet_150,965.2,264.04,256,224,9.73
regnety_016,954.26,534.657,512,224,11.2
vit_base_patch32_plus_256,951.64,537.091,512,256,119.48
mobilevitv2_075,947.54,269.157,256,256,2.87
legacy_seresnext26_32x4d,946.21,405.17,384,224,16.79
pit_s_224,942.8,270.615,256,224,23.46
pit_s_distilled_224,939.97,271.455,256,224,24.04
vit_srelpos_small_patch16_224,922.29,415.451,384,224,21.97
vit_relpos_small_patch16_224,921.7,415.439,384,224,21.98
efficientnet_b0_g16_evos,909.42,421.149,384,224,8.11
resnext26ts,905.09,423.733,384,256,10.3
cs3darknet_l,902.18,282.891,256,256,21.16
coat_lite_tiny,893.97,428.624,384,224,5.72
resnet26t,890.89,574.188,512,256,16.01
efficientnet_b0_gn,881.54,289.263,256,224,5.29
resnetv2_50,880.1,580.976,512,224,25.55
efficientnet_b2_pruned,874.13,291.317,256,260,8.31
seresnext26ts,867.62,294.407,256,256,10.39
eca_resnext26ts,867.54,294.527,256,256,10.3
tf_efficientnet_b1,863.78,294.816,256,240,7.79
tf_efficientnet_b1_ap,863.54,294.906,256,240,7.79
tf_efficientnet_b1_ns,863.39,294.941,256,240,7.79
cs3sedarknet_l,861.38,444.523,384,256,21.91
tf_efficientnetv2_b3,855.0,297.539,256,240,14.36
efficientnet_lite2,852.1,299.402,256,260,6.09
twins_svt_small,851.73,449.18,384,224,24.06
gcresnext26ts,850.58,300.113,256,256,10.48
efficientnetv2_rw_t,850.16,298.981,256,224,13.65
botnet26t_256,849.43,451.465,384,256,12.49
ecaresnetlight,846.78,603.683,512,224,30.16
seresnext26t_32x4d,845.6,453.458,384,224,16.81
seresnext26tn_32x4d,845.31,453.612,384,224,16.81
seresnext26d_32x4d,844.96,453.775,384,224,16.81
coat_lite_mini,842.06,455.115,384,224,11.01
tf_efficientnet_cc_b0_8e,837.03,457.594,384,224,24.01
ecaresnet101d_pruned,837.02,609.921,512,224,24.88
ecaresnext26t_32x4d,835.25,459.196,384,224,15.41
ecaresnext50t_32x4d,834.39,459.653,384,224,15.41
cspresnet50,830.57,461.498,384,256,21.62
swsl_resnet50,829.79,616.192,512,224,25.56
ssl_resnet50,829.64,616.294,512,224,25.56
gluon_resnet50_v1b,829.63,616.32,512,224,25.56
visformer_small,828.8,462.625,384,224,40.22
tv_resnet50,826.55,618.618,512,224,25.56
resnet50,826.06,618.983,512,224,25.56
vgg11,825.98,619.706,512,224,132.86
halonet26t,824.96,464.902,384,256,12.48
vovnet39a,817.65,625.544,512,224,22.6
tf_efficientnet_lite2,816.76,312.458,256,260,6.09
convnext_tiny_hnf,815.48,312.97,256,224,28.59
convnext_tiny_hnfd,815.19,313.078,256,224,28.59
vit_small_resnet26d_224,813.66,470.891,384,224,63.61
convnext_tiny,813.16,313.859,256,224,28.59
efficientnet_cc_b0_8e,812.96,471.165,384,224,24.01
vit_relpos_base_patch32_plus_rpn_256,811.26,630.0,512,256,119.42
mixnet_m,810.2,630.361,512,224,5.01
efficientnet_cc_b0_4e,808.8,473.577,384,224,13.31
convnext_tiny_in22ft1k,808.5,315.666,256,224,28.59
efficientnet_b2a,800.27,318.401,256,256,9.11
efficientnet_b2,799.96,318.544,256,256,9.11
regnetz_b16,796.72,319.811,256,224,9.72
tresnet_m,792.8,643.233,512,224,31.39
mobilevit_xs,792.23,321.979,256,256,2.32
ecaresnet26t,791.55,484.557,384,256,16.01
gc_efficientnetv2_rw_t,791.43,320.691,256,224,13.68
resnetv2_50t,790.83,646.602,512,224,25.57
resnetv2_50d,790.11,647.216,512,224,25.57
regnetx_032,787.5,486.368,384,224,15.3
ese_vovnet39b,786.37,650.429,512,224,24.57
tf_efficientnet_cc_b0_4e,784.54,488.254,384,224,13.31
eca_botnext26ts_256,781.43,326.976,256,256,10.59
resnet32ts,781.02,327.191,256,256,17.96
tf_mixnet_m,777.93,492.059,384,224,5.01
resnet33ts,767.78,332.812,256,256,19.68
gluon_resnet50_v1c,763.32,502.196,384,224,25.58
eca_halonext26ts,763.09,334.836,256,256,10.76
rexnetr_200,762.4,250.686,192,224,16.52
dpn68b,756.57,506.252,384,224,12.61
lambda_resnet26t,751.39,510.429,384,256,10.96
vit_relpos_small_patch16_rpn_224,751.37,510.029,384,224,21.97
resnet50t,748.03,512.487,384,224,25.57
cspresnet50d,746.91,341.842,256,256,21.64
gluon_resnet50_v1d,746.5,513.543,384,224,25.58
resnet50d,744.99,514.575,384,224,25.58
legacy_seresnet50,744.88,514.356,384,224,28.09
cspresnet50w,744.04,343.166,256,256,28.12
eca_resnet33ts,743.27,343.735,256,256,19.68
efficientnet_b0_g8_gn,743.27,343.315,256,224,6.56
seresnet33ts,742.4,344.003,256,256,19.78
resnetaa50,741.95,516.711,384,224,25.56
selecsls84,740.99,689.736,512,224,50.95
dpn68,739.97,517.747,384,224,12.61
res2net50_48w_2s,738.06,519.427,384,224,25.29
vit_small_r26_s32_224,737.22,345.982,256,224,36.43
eca_vovnet39b,735.59,695.354,512,224,22.6
lambda_resnet26rpt_256,735.09,260.579,192,256,10.99
nf_regnet_b3,732.33,522.471,384,288,18.59
rexnet_200,731.68,261.239,192,224,16.37
densenet121,730.11,348.758,256,224,7.98
resnest26d,728.94,526.039,384,224,17.07
bat_resnext26ts,728.42,350.197,256,256,10.73
mobilevitv2_100,727.72,262.852,192,256,4.9
tv_densenet121,727.58,350.07,256,224,7.98
nf_seresnet50,727.17,526.884,384,224,28.09
gcresnet33ts,725.89,351.666,256,256,19.88
eca_nfnet_l0,723.69,706.434,512,224,24.14
nfnet_l0,719.96,532.162,384,224,35.07
seresnet50,714.65,536.208,384,224,28.09
twins_pcpvt_small,714.45,356.63,256,224,24.11
nf_ecaresnet50,713.69,537.063,384,224,25.56
dla60,709.61,540.13,384,224,22.04
efficientnet_em,708.33,360.423,256,240,6.9
hrnet_w18_small_v2,705.02,723.712,512,224,15.6
resnetblur50d,704.94,362.275,256,224,25.58
vgg11_bn,703.05,545.962,384,224,132.87
resnetblur50,698.61,548.824,384,224,25.56
regnety_032,696.58,549.77,384,224,19.44
nf_resnet50,696.17,550.716,384,256,25.56
efficientnet_b3_pruned,694.05,367.106,256,300,9.86
tf_efficientnet_em,690.66,369.697,256,240,6.9
skresnet50,685.67,371.92,256,224,25.8
xcit_tiny_24_p16_224,683.44,371.201,256,224,12.12
poolformer_s24,681.93,374.176,256,224,21.39
xcit_tiny_24_p16_224_dist,681.85,371.937,256,224,12.12
vit_base_resnet26d_224,680.96,562.594,384,224,101.4
vovnet57a,678.75,564.837,384,224,36.64
densenet121d,678.22,375.614,256,224,8.0
resnetaa50d,673.73,569.117,384,224,25.58
gluon_resnet50_v1s,669.16,573.001,384,224,25.68
gmixer_24_224,666.22,382.715,256,224,24.72
swsl_resnext50_32x4d,663.66,577.766,384,224,25.03
resnext50_32x4d,663.39,577.966,384,224,25.03
ssl_resnext50_32x4d,663.18,578.185,384,224,25.03
tv_resnext50_32x4d,662.37,578.888,384,224,25.03
gluon_resnext50_32x4d,662.06,579.185,384,224,25.03
haloregnetz_b,660.09,386.296,256,224,11.68
ese_vovnet57b,656.27,584.17,384,224,38.61
cspresnext50,656.07,389.365,256,256,20.57
seresnet50t,655.71,584.407,384,224,28.1
vit_relpos_medium_patch16_cls_224,654.69,389.857,256,224,38.76
seresnetaa50d,654.11,390.147,256,224,28.11
densenetblur121d,649.47,392.249,256,224,8.0
res2net50_26w_4s,648.76,590.62,384,224,25.7
fbnetv3_g,647.3,294.603,192,240,16.62
swin_tiny_patch4_window7_224,646.76,394.841,256,224,28.29
ecaresnet50d,643.9,595.437,384,224,25.58
regnety_040,640.15,598.298,384,224,20.65
gmlp_s16_224,638.67,299.017,192,224,19.42
crossvit_small_240,637.47,399.952,256,240,26.86
resnext50d_32x4d,635.21,402.121,256,224,25.05
nfnet_f0,634.03,806.334,512,192,71.49
vit_srelpos_medium_patch16_224,629.85,405.54,256,224,38.74
mobilevit_s,629.67,303.779,192,256,5.58
skresnet50d,628.92,405.574,256,224,25.82
vit_relpos_medium_patch16_224,628.2,406.369,256,224,38.75
resnest50d_1s4x24d,628.12,406.263,256,224,25.68
mixnet_l,627.47,406.445,256,224,7.33
tf_efficientnet_b2_ns,627.11,304.591,192,260,9.11
tf_efficientnet_b2_ap,626.79,304.757,192,260,9.11
tf_efficientnet_b2,626.11,305.153,192,260,9.11
regnetx_040,624.89,613.356,384,224,22.12
regnetv_040,622.71,409.581,256,224,20.64
darknetaa53,614.47,415.819,256,256,36.02
seresnext50_32x4d,613.62,416.021,256,224,27.56
gluon_seresnext50_32x4d,613.35,416.206,256,224,27.56
sehalonet33ts,613.13,416.664,256,256,13.69
legacy_seresnext50_32x4d,612.89,416.52,256,224,27.56
dla60x,612.79,416.731,256,224,17.35
gcresnet50t,611.79,626.12,384,256,25.9
xcit_nano_12_p16_384_dist,611.55,416.81,256,384,3.05
resmlp_24_224,609.69,418.351,256,224,30.02
resmlp_24_distilled_224,609.51,418.474,256,224,30.02
gcresnext50ts,606.82,314.923,192,256,15.67
tf_inception_v3,603.29,635.057,384,299,23.83
gluon_inception_v3,603.22,635.143,384,299,23.83
adv_inception_v3,603.01,635.347,384,299,23.83
inception_v3,602.27,636.205,384,299,23.83
tf_mixnet_l,600.24,424.956,256,224,7.33
dm_nfnet_f0,600.1,638.573,384,192,71.49
xcit_small_12_p16_224,598.44,425.955,256,224,26.25
xcit_small_12_p16_224_dist,598.22,426.013,256,224,26.25
semobilevit_s,597.07,320.258,192,256,5.74
densenet169,592.78,429.221,256,224,14.15
res2next50,591.98,431.144,256,224,24.67
resnetv2_101,590.74,431.806,256,224,44.54
darknet53,590.64,432.606,256,256,41.61
resnetv2_50x1_bit_distilled,587.0,326.262,192,224,25.55
res2net50_14w_8s,586.94,433.992,256,224,25.06
swin_s3_tiny_224,586.39,435.576,256,224,28.33
repvgg_b1g4,584.26,875.234,512,224,39.97
dla60_res2net,583.07,437.618,256,224,20.85
crossvit_15_240,576.62,331.16,192,240,27.53
cait_xxs24_224,576.52,441.46,256,224,11.96
cs3darknet_focus_x,569.86,448.292,256,256,35.02
resnet101,568.98,448.321,256,224,44.55
gluon_resnet101_v1b,568.4,448.834,256,224,44.55
tv_resnet101,566.24,450.547,256,224,44.55
resnetrs101,564.72,451.1,256,192,63.62
efficientnet_cc_b1_8e,564.18,452.061,256,240,39.72
crossvit_15_dagger_240,558.23,342.033,192,240,28.21
vit_base_resnet50d_224,557.61,457.501,256,224,110.97
mobilevitv2_125,557.38,343.473,192,256,7.48
xcit_nano_12_p8_224_dist,555.43,459.069,256,224,3.05
xcit_nano_12_p8_224,555.18,459.311,256,224,3.05
sebotnet33ts_256,554.49,230.012,128,256,13.7
resnet51q,551.31,463.504,256,256,35.7
resnetv2_101d,548.02,465.6,256,224,44.56
tf_efficientnet_cc_b1_8e,547.16,466.173,256,240,39.72
resnetv2_50d_gn,546.54,350.469,192,224,25.57
nf_resnet101,543.9,704.337,384,224,44.55
vit_base_patch32_384,542.76,470.804,256,384,88.3
gluon_resnet101_v1c,537.15,475.0,256,224,44.57
cspdarknet53,537.1,475.617,256,256,27.64
cs3darknet_x,534.86,477.64,256,256,35.05
vit_base_r26_s32_224,534.67,357.767,192,224,101.38
resnest50d,534.66,477.434,256,224,27.48
resnet50_gn,531.78,360.235,192,224,25.56
regnetz_c16,530.35,360.552,192,256,13.46
gluon_resnet101_v1d,528.59,482.76,256,224,44.57
mixer_b16_224,528.02,484.004,256,224,59.88
mixer_l32_224,527.33,362.504,192,224,206.94
mixer_b16_224_miil,526.58,485.347,256,224,59.88
vit_large_patch32_224,521.73,489.021,256,224,306.54
dla60_res2next,520.31,490.572,256,224,17.03
ecaresnet50t,516.29,494.896,256,256,25.57
cs3sedarknet_xdw,516.24,246.008,128,256,21.6
lambda_resnet50ts,515.16,371.658,192,256,21.54
vit_tiny_patch16_384,512.2,249.072,128,384,5.79
resnet61q,510.55,375.027,192,256,36.85
swinv2_cr_tiny_224,505.83,504.823,256,224,28.33
halonet50ts,503.76,380.122,192,256,22.73
repvgg_b1,503.57,1015.623,512,224,57.42
swinv2_cr_tiny_ns_224,502.5,508.144,256,224,28.33
cs3sedarknet_x,501.96,508.547,256,256,35.4
dla102,497.28,513.14,256,224,33.27
wide_resnet50_2,495.68,773.85,384,224,68.88
res2net50_26w_6s,493.57,516.914,256,224,37.05
resnetaa101d,490.51,520.338,256,224,44.57
convnext_small,489.81,390.224,192,224,50.22
convnext_small_in22ft1k,489.45,390.576,192,224,50.22
legacy_seresnet101,487.73,522.616,256,224,49.33
vit_relpos_medium_patch16_rpn_224,485.5,526.221,256,224,38.73
efficientnet_lite3,484.47,263.098,128,300,8.2
gluon_resnet101_v1s,483.47,527.891,256,224,44.67
seresnet101,480.65,530.38,256,224,49.33
cs3edgenet_x,477.59,535.019,256,256,47.82
nest_tiny,476.68,267.593,128,224,17.06
nf_seresnet101,474.46,537.213,256,224,49.33
mobilevitv2_150_in22ft1k,473.86,269.132,128,256,10.59
mobilevitv2_150,473.84,269.144,128,256,10.59
resnetblur101d,472.23,540.497,256,224,44.57
jx_nest_tiny,472.22,270.163,128,224,17.06
nf_ecaresnet101,469.53,543.375,256,224,44.55
vgg13_bn,468.37,546.3,256,224,133.05
twins_pcpvt_base,466.47,408.848,192,224,43.83
tf_efficientnet_lite3,465.4,273.895,128,300,8.2
vgg16,465.36,824.954,384,224,138.36
sequencer2d_s,462.43,412.819,192,224,27.65
mixnet_xl,460.21,554.308,256,224,11.9
coat_lite_small,457.06,418.568,192,224,19.84
efficientnet_b3a,456.95,278.403,128,288,12.23
efficientnet_b3,456.81,278.448,128,288,12.23
regnetx_080,454.56,843.629,384,224,39.57
regnetx_064,452.43,564.953,256,224,26.21
halo2botnet50ts_256,451.35,424.392,192,256,22.64
ecaresnet101d,447.44,570.33,256,224,44.57
densenet201,447.44,425.987,192,224,20.01
nf_regnet_b4,445.8,428.533,192,320,30.21
convit_small,443.63,431.737,192,224,27.78
efficientnetv2_s,433.27,293.157,128,288,21.46
skresnext50_32x4d,432.3,590.802,256,224,27.48
cs3se_edgenet_x,431.68,443.324,192,256,50.72
botnet50ts_256,428.8,297.529,128,256,22.74
ssl_resnext101_32x4d,427.28,447.74,192,224,44.18
resnext101_32x4d,427.18,447.921,192,224,44.18
swsl_resnext101_32x4d,427.16,447.915,192,224,44.18
gluon_resnext101_32x4d,427.13,447.97,192,224,44.18
poolformer_s36,425.0,449.906,192,224,30.86
ese_vovnet39b_evos,421.31,302.862,128,224,24.58
resnet101d,418.0,457.739,192,256,44.57
dla102x,417.16,458.658,192,224,26.31
res2net101_26w_4s,416.51,612.121,256,224,45.21
lamhalobotnet50ts_256,413.09,463.774,192,256,22.57
twins_svt_base,411.8,464.138,192,224,56.07
crossvit_18_240,406.79,312.611,128,240,43.27
tresnet_l,404.84,1261.389,512,224,55.99
efficientnetv2_rw_s,402.34,315.806,128,288,23.94
volo_d1_224,401.47,476.8,192,224,26.63
resmlp_36_224,401.06,476.505,192,224,44.69
res2net50_26w_8s,400.52,636.999,256,224,48.4
resmlp_36_distilled_224,400.14,477.557,192,224,44.69
swin_small_patch4_window7_224,399.67,478.499,192,224,49.61
resnest50d_4s2x40d,396.0,645.092,256,224,30.42
vit_base_patch16_224_miil,395.78,484.311,192,224,86.54
crossvit_18_dagger_240,394.08,322.72,128,240,44.27
deit_base_patch16_224,390.88,490.307,192,224,86.57
vit_base_patch16_224,390.86,490.391,192,224,86.57
vit_base_patch16_224_sam,390.67,490.608,192,224,86.57
mobilevitv2_175_in22ft1k,389.97,327.241,128,256,14.25
mobilevitv2_175,389.95,327.23,128,256,14.25
tf_efficientnetv2_s_in21ft1k,389.66,326.288,128,300,21.46
tf_efficientnetv2_s,389.1,326.713,128,300,21.46
vgg16_bn,388.69,658.276,256,224,138.37
regnety_064,388.46,657.256,256,224,30.58
regnety_080,385.84,662.273,256,224,39.18
deit_base_distilled_patch16_224,385.62,497.03,192,224,87.34
xception,385.56,331.194,128,299,22.86
regnety_040s_gn,384.97,330.927,128,224,20.65
repvgg_b2g4,379.42,1348.329,512,224,61.76
resnetv2_152,379.4,503.868,192,224,60.19
regnetz_d8,378.76,336.282,128,256,23.37
hrnet_w18,378.26,671.883,256,224,21.3
ese_vovnet99b,377.27,677.036,256,224,63.2
vit_small_resnet50d_s16_224,376.52,508.654,192,224,57.53
cait_xxs36_224,375.8,507.032,192,224,17.3
gluon_seresnext101_32x4d,375.02,509.78,192,224,48.96
regnetz_040,374.91,339.544,128,256,27.12
seresnext101_32x4d,374.73,510.176,192,224,48.96
regnetv_064,372.69,513.522,192,224,30.58
regnetz_040h,372.64,341.593,128,256,28.94
legacy_seresnext101_32x4d,372.06,513.705,192,224,48.96
deit3_base_patch16_224_in21ft1k,371.79,515.431,192,224,86.59
deit3_base_patch16_224,371.73,515.464,192,224,86.59
tf_efficientnet_b3,370.15,344.089,128,300,12.23
tf_efficientnet_b3_ap,370.14,344.111,128,300,12.23
tf_efficientnet_b3_ns,370.1,344.134,128,300,12.23
resnet152,370.08,516.516,192,224,60.19
vit_relpos_base_patch16_clsgap_224,369.76,518.105,192,224,86.43
vit_relpos_base_patch16_cls_224,369.34,518.67,192,224,86.43
resnetv2_50d_frn,369.16,345.594,128,224,25.59
gluon_resnet152_v1b,369.02,517.998,192,224,60.19
tv_resnet152,369.0,518.088,192,224,60.19
regnetz_b16_evos,365.3,348.518,128,224,9.74
sequencer2d_m,363.12,525.505,192,224,38.31
ese_vovnet99b_iabn,362.9,1055.043,384,224,63.2
resnetv2_152d,360.99,529.48,192,224,60.2
beit_base_patch16_224,358.29,534.776,192,224,86.53
xcit_tiny_12_p16_384_dist,357.91,534.55,192,384,6.72
vit_relpos_base_patch16_224,355.33,539.194,192,224,86.43
gluon_resnet152_v1c,354.77,538.797,192,224,60.21
regnetz_d32,354.52,359.397,128,256,27.58
swinv2_tiny_window8_256,354.35,540.569,192,256,28.35
resnetv2_50d_evos,353.36,270.506,96,224,25.59
dpn92,353.0,723.617,256,224,37.67
vgg19,352.0,1090.655,384,224,143.67
gluon_resnet152_v1d,351.06,544.563,192,224,60.21
densenet161,346.02,367.416,128,224,28.68
xception41p,344.85,370.318,128,299,26.91
gluon_resnet152_v1s,344.7,368.96,128,224,60.32
mobilevitv2_200,342.4,372.843,128,256,18.45
tnt_s_patch16_224,342.25,559.037,192,224,23.76
mobilevitv2_200_in22ft1k,342.08,373.147,128,256,18.45
eca_nfnet_l1,341.07,561.084,192,256,41.41
hrnet_w32,340.54,747.043,256,224,41.23
dla169,338.11,565.259,192,224,53.39
convnext_base_in22ft1k,337.76,377.102,128,224,88.59
convnext_base,336.98,378.091,128,224,88.59
repvgg_b2,335.01,1527.215,512,224,89.02
repvgg_b3g4,334.01,1148.557,384,224,83.83
vgg13,331.37,1544.923,512,224,133.05
pit_b_224,331.17,385.577,128,224,73.76
vgg19_bn,330.96,773.109,256,224,143.68
pit_b_distilled_224,329.46,387.568,128,224,74.79
regnetx_120,327.41,780.952,256,224,46.11
twins_pcpvt_large,322.96,392.17,128,224,60.99
hrnet_w30,321.86,790.607,256,224,37.71
legacy_seresnet152,319.56,397.245,128,224,66.82
inception_v4,316.87,603.734,192,299,42.68
seresnet152,313.75,608.677,192,224,66.82
vit_small_patch16_36x1_224,310.56,409.448,128,224,64.67
dla102x2,309.09,412.537,128,224,41.28
xcit_small_24_p16_224_dist,307.83,412.466,128,224,47.67
convmixer_1024_20_ks9_p14,307.81,830.813,256,224,24.38
vit_small_patch16_18x2_224,307.61,413.3,128,224,64.67
xcit_small_24_p16_224,307.46,412.867,128,224,47.67
regnety_120,307.05,623.971,192,224,51.82
poolformer_m36,303.34,420.132,128,224,56.17
efficientnet_el_pruned,301.49,423.464,128,300,10.59
efficientnet_el,301.45,423.5,128,300,10.59
swinv2_cr_small_ns_224,300.41,423.619,128,224,49.7
swinv2_cr_small_224,297.65,427.521,128,224,49.7
mixnet_xxl,297.33,428.503,128,224,23.96
cait_s24_224,296.96,428.341,128,224,46.92
nest_small,296.72,321.888,96,224,38.35
coat_tiny,296.44,429.708,128,224,5.5
tf_efficientnet_el,295.51,432.07,128,300,10.59
jx_nest_small,294.83,323.932,96,224,38.35
efficientnet_b4,293.1,325.442,96,320,19.34
xception41,293.07,435.505,128,299,26.97
xcit_tiny_12_p8_224_dist,291.52,437.287,128,224,6.71
tresnet_xl,291.4,875.028,256,224,78.44
resnext101_64x4d,291.33,437.816,128,224,83.46
gluon_resnext101_64x4d,291.25,437.881,128,224,83.46
swin_s3_small_224,289.76,439.818,128,224,49.74
wide_resnet101_2,289.62,661.356,192,224,126.89
xcit_tiny_12_p8_224,289.33,440.549,128,224,6.71
twins_svt_large,289.11,440.647,128,224,99.27
resnet152d,281.47,452.46,128,256,60.21
swin_base_patch4_window7_224,279.66,455.817,128,224,87.77
convnext_tiny_384_in22ft1k,278.8,343.389,96,384,28.59
resnet200,276.62,459.688,128,224,64.67
ssl_resnext101_32x8d,276.52,461.341,128,224,88.79
ig_resnext101_32x8d,276.39,461.582,128,224,88.79
resnext101_32x8d,276.22,461.854,128,224,88.79
swsl_resnext101_32x8d,276.22,461.764,128,224,88.79
repvgg_b3,271.93,1411.039,384,224,123.09
nfnet_f1,271.43,705.161,192,224,132.63
resnetv2_50d_evob,268.92,355.729,96,224,25.59
gmlp_b16_224,268.22,356.298,96,224,73.08
dpn98,267.62,476.602,128,224,61.57
regnetx_160,266.55,719.244,192,224,54.28
regnety_160,264.44,724.758,192,224,83.59
gluon_seresnext101_64x4d,264.12,482.344,128,224,88.23
ens_adv_inception_resnet_v2,261.47,730.952,192,299,55.84
inception_resnet_v2,261.44,730.919,192,299,55.84
xception65p,259.32,492.32,128,299,39.82
efficientnet_lite4,255.23,249.374,64,380,13.01
vit_base_patch16_rpn_224,254.51,753.593,192,224,86.54
resnest101e,253.9,501.575,128,256,48.28
crossvit_base_240,253.73,376.737,96,240,105.03
seresnext101_32x8d,251.44,506.792,128,224,93.57
vit_relpos_base_patch16_rpn_224,250.62,765.002,192,224,86.41
vit_base_patch16_plus_240,248.4,514.352,128,240,117.56
tf_efficientnet_lite4,247.32,257.437,64,380,13.01
efficientnet_b3_gn,245.44,258.998,64,288,11.73
dm_nfnet_f1,244.51,521.138,128,224,132.63
seresnext101d_32x8d,242.49,525.503,128,224,93.59
seresnet152d,242.25,392.75,96,256,66.84
xcit_tiny_24_p16_384_dist,241.62,526.318,128,384,12.12
vit_small_patch16_384,239.05,266.865,64,384,22.2
vit_relpos_base_patch16_plus_240,238.57,535.322,128,240,117.38
vit_large_r50_s32_224,237.94,401.033,96,224,328.99
resnetrs152,237.63,535.199,128,256,86.62
swinv2_tiny_window16_256,237.41,403.072,96,256,28.35
seresnextaa101d_32x8d,228.0,559.15,128,224,93.59
xcit_medium_24_p16_224_dist,227.91,558.239,128,224,84.4
deit3_small_patch16_384_in21ft1k,227.77,280.008,64,384,22.21
deit3_small_patch16_384,227.76,280.015,64,384,22.21
xcit_medium_24_p16_224,227.75,558.491,128,224,84.4
vit_small_r26_s32_384,227.25,280.302,64,384,36.47
convit_base,224.86,568.198,128,224,86.54
gluon_xception65,224.1,426.474,96,299,39.92
swin_s3_base_224,223.36,426.944,96,224,71.13
tnt_b_patch16_224,222.94,572.213,128,224,65.41
xception65,222.93,428.728,96,299,39.92
coat_mini,222.88,572.209,128,224,10.34
volo_d2_224,222.28,430.111,96,224,58.68
xcit_small_12_p16_384_dist,221.79,430.984,96,384,26.25
poolformer_m48,220.53,432.741,96,224,73.47
hrnet_w40,219.45,869.959,192,224,57.56
vit_base_r50_s16_224,215.41,443.988,96,224,98.66
swinv2_cr_base_ns_224,213.88,446.428,96,224,87.88
sequencer2d_l,213.86,444.098,96,224,54.3
swinv2_small_window8_256,212.59,449.01,96,256,49.73
swinv2_cr_base_224,211.39,451.682,96,224,87.88
mobilevitv2_150_384_in22ft1k,210.38,303.23,64,384,10.59
nest_base,210.2,302.774,64,224,67.72
tresnet_m_448,209.55,913.447,192,448,31.39
efficientnetv2_m,207.96,304.545,64,320,54.14
jx_nest_base,207.78,306.371,64,224,67.72
regnetz_c16_evos,207.35,306.824,64,256,13.49
hrnet_w44,206.45,925.026,192,224,67.06
resnet200d,204.47,623.017,128,256,64.69
efficientnet_b3_g8_gn,203.44,312.836,64,288,14.25
hrnet_w48,202.15,628.427,128,224,77.47
densenet264,202.1,470.789,96,224,72.69
dpn131,198.25,643.486,128,224,79.25
tf_efficientnet_b4,194.83,326.399,64,380,19.34
tf_efficientnet_b4_ap,194.65,326.738,64,380,19.34
tf_efficientnet_b4_ns,194.23,327.375,64,380,19.34
xcit_nano_12_p8_384_dist,187.76,338.965,64,384,3.05
efficientnetv2_rw_m,187.31,338.14,64,320,53.24
dpn107,187.14,682.151,128,224,86.92
convnext_large_in22ft1k,187.05,511.402,96,224,197.77
convnext_large,187.01,511.523,96,224,197.77
nf_regnet_b5,186.49,512.09,96,384,49.74
xcit_tiny_24_p8_224_dist,183.21,520.533,96,224,12.11
xcit_tiny_24_p8_224,183.21,520.609,96,224,12.11
halonet_h1,177.48,359.151,64,256,8.1
hrnet_w64,176.04,722.362,128,224,128.06
mobilevitv2_175_384_in22ft1k,175.76,363.135,64,384,14.25
senet154,174.83,545.792,96,224,115.09
regnety_320,174.41,732.528,128,224,145.05
gluon_senet154,174.03,548.162,96,224,115.09
regnetz_e8,173.89,365.999,64,256,57.7
legacy_senet154,170.27,560.493,96,224,115.09
xception71,168.81,376.911,64,299,42.34
xcit_small_12_p8_224,168.52,377.961,64,224,26.21
xcit_small_12_p8_224_dist,168.32,378.375,64,224,26.21
vit_large_patch32_384,168.05,569.595,96,384,306.63
convnext_small_384_in22ft1k,164.94,386.292,64,384,50.22
mixer_l16_224,164.43,582.335,96,224,208.2
ecaresnet200d,161.74,392.334,64,256,64.69
seresnet200d,161.56,391.892,64,256,71.86
resnetrs200,160.52,394.222,64,256,93.21
densenet264d_iabn,158.12,804.924,128,224,72.74
regnetx_320,155.94,819.702,128,224,107.81
swin_large_patch4_window7_224,153.79,414.255,64,224,196.53
volo_d3_224,152.73,416.584,64,224,86.33
mobilevitv2_200_384_in22ft1k,150.68,317.559,48,384,18.45
swinv2_base_window8_256,150.28,423.39,64,256,87.92
resnetv2_50x1_bitm,149.04,321.21,48,448,25.55
nfnet_f2,148.92,641.446,96,256,193.78
swinv2_small_window16_256,142.83,445.591,64,256,49.73
tf_efficientnetv2_m,142.41,333.833,48,384,54.14
eca_nfnet_l2,142.2,672.291,96,320,56.72
tf_efficientnetv2_m_in21ft1k,141.35,336.246,48,384,54.14
regnetz_d8_evos,132.2,360.995,48,256,23.46
swinv2_cr_tiny_384,131.5,485.388,64,384,28.33
ig_resnext101_32x16d,130.47,734.203,96,224,194.03
ssl_resnext101_32x16d,130.4,734.63,96,224,194.03
swsl_resnext101_32x16d,130.37,734.771,96,224,194.03
xcit_large_24_p16_224,126.98,500.577,64,224,189.1
xcit_large_24_p16_224_dist,126.97,500.662,64,224,189.1
seresnet269d,125.7,503.318,64,256,113.67
dm_nfnet_f2,125.2,507.681,64,256,193.78
swinv2_cr_large_224,124.7,510.736,64,224,196.68
xcit_tiny_12_p8_384_dist,122.38,390.412,48,384,6.71
resnetrs270,121.5,520.761,64,256,129.86
crossvit_15_dagger_408,117.57,270.29,32,408,28.5
vit_large_patch16_224,117.08,544.981,64,224,304.33
vit_base_patch16_18x2_224,116.55,546.378,64,224,256.73
convnext_base_384_in22ft1k,115.97,412.084,48,384,88.59
convnext_xlarge_in22ft1k,115.91,550.445,64,224,350.2
deit3_large_patch16_224_in21ft1k,113.19,563.501,64,224,304.37
deit3_large_patch16_224,113.17,563.634,64,224,304.37
xcit_small_24_p16_384_dist,112.88,421.839,48,384,47.67
beit_large_patch16_224,107.8,591.544,64,224,304.43
swinv2_base_window16_256,103.68,460.461,48,256,87.92
swinv2_base_window12to16_192to256_22kft1k,103.56,461.021,48,256,87.92
tresnet_l_448,103.2,1236.839,128,448,55.99
volo_d1_384,99.39,320.613,32,384,26.78
cait_xxs24_384,97.83,488.033,48,384,12.03
vit_base_patch16_384,96.96,329.192,32,384,86.86
deit_base_patch16_384,96.37,331.171,32,384,86.86
volo_d4_224,95.75,498.748,48,224,192.96
deit_base_distilled_patch16_384,94.83,336.556,32,384,87.63
efficientnet_b5,93.71,338.901,32,456,30.39
deit3_base_patch16_384,93.22,342.328,32,384,86.88
deit3_base_patch16_384_in21ft1k,92.68,344.327,32,384,86.88
tf_efficientnet_b5,92.16,344.785,32,456,30.39
tf_efficientnet_b5_ns,92.11,344.939,32,456,30.39
tf_efficientnet_b5_ap,91.98,345.405,32,456,30.39
resnetv2_152x2_bit_teacher,89.37,355.711,32,224,236.34
crossvit_18_dagger_408,88.76,358.492,32,408,44.61
xcit_small_24_p8_224,87.25,546.829,48,224,47.63
xcit_small_24_p8_224_dist,86.87,549.24,48,224,47.63
convmixer_768_32,85.53,1121.025,96,224,21.11
vit_large_patch14_224,85.27,561.239,48,224,304.2
eca_nfnet_l3,84.61,563.702,48,352,72.04
resnetv2_101x1_bitm,84.61,187.399,16,448,44.54
beit_base_patch16_384,83.67,381.291,32,384,86.74
resnest200e,83.33,570.802,48,320,70.2
tf_efficientnetv2_l_in21ft1k,83.27,379.678,32,384,118.52
efficientnetv2_l,83.27,379.867,32,384,118.52
tf_efficientnetv2_l,82.74,382.367,32,384,118.52
ecaresnet269d,82.15,579.477,48,320,102.09
tresnet_xl_448,78.31,1222.487,96,448,78.44
xcit_medium_24_p16_384_dist,77.9,407.346,32,384,84.4
vit_large_r50_s32_384,77.49,410.426,32,384,329.09
swinv2_cr_small_384,76.31,416.793,32,384,49.7
swin_base_patch4_window12_384,74.03,430.327,32,384,87.9
pnasnet5large,68.77,461.392,32,331,86.06
resnetrs350,68.24,460.967,32,288,163.96
nfnet_f3,67.87,703.087,48,320,254.92
nasnetalarge,67.38,469.785,32,331,88.75
resmlp_big_24_distilled_224,67.03,475.867,32,224,129.14
resmlp_big_24_224_in22ft1k,67.03,475.857,32,224,129.14
resmlp_big_24_224,67.02,475.97,32,224,129.14
cait_xs24_384,65.59,485.229,32,384,26.67
convnext_large_384_in22ft1k,63.62,501.159,32,384,197.77
vit_base_patch8_224,63.42,377.591,24,224,86.58
cait_xxs36_384,63.23,502.345,32,384,17.37
ig_resnext101_32x32d,62.72,508.666,32,224,468.53
xcit_tiny_24_p8_384_dist,62.14,511.676,32,384,12.11
volo_d5_224,61.58,516.467,32,224,295.46
vit_base_resnet50_384,61.06,391.43,24,384,98.95
vit_base_r50_s16_384,61.0,391.773,24,384,98.95
swinv2_large_window12to16_192to256_22kft1k,60.93,391.352,24,256,196.74
xcit_medium_24_p8_224,60.43,526.111,32,224,84.32
xcit_medium_24_p8_224_dist,60.03,529.652,32,224,84.32
xcit_small_12_p8_384_dist,57.75,413.763,24,384,26.21
dm_nfnet_f3,57.26,554.375,32,320,254.92
volo_d2_384,55.56,286.247,16,384,58.87
efficientnet_b6,54.98,288.003,16,528,43.04
swinv2_cr_base_384,54.71,436.265,24,384,87.88
tf_efficientnet_b6,54.38,291.338,16,528,43.04
tf_efficientnet_b6_ns,54.21,292.241,16,528,43.04
tf_efficientnet_b6_ap,54.17,292.479,16,528,43.04
efficientnetv2_xl,53.77,291.666,16,384,208.12
tf_efficientnetv2_xl_in21ft1k,53.07,295.611,16,384,208.12
convmixer_1536_20,50.1,957.271,48,224,51.63
swinv2_cr_huge_224,49.51,482.114,24,224,657.83
cait_s24_384,49.37,483.331,24,384,47.06
resnetrs420,48.12,489.4,24,320,191.89
xcit_large_24_p16_384_dist,45.6,522.909,24,384,189.1
swin_large_patch4_window12_384,41.65,382.145,16,384,196.74
convnext_xlarge_384_in22ft1k,40.18,595.51,24,384,350.2
vit_huge_patch14_224,39.94,398.436,16,224,632.05
deit3_huge_patch14_224_in21ft1k,38.36,414.578,16,224,632.13
deit3_huge_patch14_224,38.33,414.855,16,224,632.13
nfnet_f4,36.85,646.047,24,384,316.07
resnest269e,35.75,664.499,24,416,110.93
resnetv2_50x3_bitm,34.88,457.822,16,448,217.32
xcit_large_24_p8_224_dist,33.7,471.344,16,224,188.93
xcit_large_24_p8_224,33.68,471.512,16,224,188.93
resnetv2_152x2_bit_teacher_384,32.69,487.138,16,384,236.34
ig_resnext101_32x48d,32.39,492.417,16,224,828.41
swinv2_cr_large_384,32.36,491.839,16,384,196.68
cait_s36_384,31.92,497.404,16,384,68.37
efficientnet_b7,31.74,248.492,8,600,66.35
dm_nfnet_f4,31.4,758.349,24,384,316.07
tf_efficientnet_b7,31.4,251.12,8,600,66.35
tf_efficientnet_b7_ns,31.37,251.394,8,600,66.35
tf_efficientnet_b7_ap,31.35,251.548,8,600,66.35
xcit_small_24_p8_384_dist,29.2,544.65,16,384,47.63
vit_large_patch16_384,29.07,411.127,12,384,304.72
deit3_large_patch16_384,28.22,423.365,12,384,304.76
deit3_large_patch16_384_in21ft1k,28.19,423.825,12,384,304.76
swinv2_base_window12to24_192to384_22kft1k,28.14,423.938,12,384,87.92
beit_large_patch16_384,25.12,475.56,12,384,305.0
volo_d3_448,23.79,333.686,8,448,86.63
nfnet_f5,22.84,694.067,16,416,377.21
resnetv2_152x2_bitm,22.69,350.236,8,448,236.34
vit_giant_patch14_224,22.29,356.113,8,224,1012.61
dm_nfnet_f5,20.95,756.72,16,416,377.21
xcit_medium_24_p8_384_dist,19.97,397.272,8,384,84.32
efficientnet_b8,19.9,297.659,6,672,87.41
tf_efficientnet_b8_ap,19.66,301.198,6,672,87.41
tf_efficientnet_b8,19.65,301.246,6,672,87.41
nfnet_f6,18.5,641.193,12,448,438.36
resnetv2_101x3_bitm,18.0,442.742,8,448,387.93
volo_d4_448,16.87,353.154,6,448,193.41
swinv2_large_window12to24_192to384_22kft1k,16.59,359.187,6,384,196.74
dm_nfnet_f6,15.07,522.261,8,448,438.36
swinv2_cr_huge_384,12.92,461.964,6,384,657.94
nfnet_f7,12.67,622.861,8,480,499.5
cait_m36_384,11.76,506.439,6,384,271.22
xcit_large_24_p8_384_dist,11.53,516.755,6,384,188.93
volo_d5_448,11.08,357.783,4,448,295.91
tf_efficientnet_l2_ns_475,10.91,360.832,4,475,480.31
beit_large_patch16_512,9.42,422.333,4,512,305.67
volo_d5_512,7.72,385.462,3,512,296.09
resnetv2_152x4_bitm,4.91,404.529,2,480,936.53
cait_m48_448,4.71,419.69,2,448,356.46
efficientnet_l2,3.43,285.826,1,800,480.31
tf_efficientnet_l2_ns,3.42,287.247,1,800,480.31
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nhwc-pt111-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,10725.36,46.047,512,106,2.04
mobilenetv3_small_050,9864.52,50.786,512,224,1.59
lcnet_035,9593.72,52.888,512,224,1.64
lcnet_050,8283.82,61.296,512,224,1.88
tf_mobilenetv3_small_minimal_100,8178.73,62.055,512,224,2.04
tinynet_d,7987.22,63.336,512,152,2.34
mobilenetv3_small_075,7734.29,65.482,512,224,2.04
mobilenetv3_small_100,7481.49,67.702,512,224,2.54
tf_mobilenetv3_small_075,7093.89,71.455,512,224,2.04
tf_mobilenetv3_small_100,6879.11,73.705,512,224,2.54
levit_128s,6303.14,80.293,512,224,7.78
lcnet_075,5742.95,88.676,512,224,2.36
lcnet_100,5331.75,95.531,512,224,2.95
mixer_s32_224,4714.36,108.029,512,224,19.1
mnasnet_small,4652.19,109.156,512,224,2.03
mnasnet_050,4534.41,112.14,512,224,2.22
levit_128,4434.56,114.332,512,224,9.21
vit_small_patch32_224,4334.06,117.284,512,224,22.88
mobilenetv2_035,4197.24,121.203,512,224,1.68
tinynet_c,4165.97,121.921,512,184,2.46
gernet_s,4117.74,123.649,512,224,8.17
semnasnet_050,4027.14,126.223,512,224,2.08
vit_tiny_r_s16_p8_224,3857.49,131.88,512,224,6.34
levit_192,3823.94,132.765,512,224,10.95
lcnet_150,3663.02,139.3,512,224,4.5
resnet18,3584.19,142.504,512,224,11.69
gluon_resnet18_v1b,3584.07,142.508,512,224,11.69
swsl_resnet18,3583.72,142.531,512,224,11.69
ssl_resnet18,3558.1,143.543,512,224,11.69
mobilenetv2_050,3541.93,143.76,512,224,1.97
mobilenetv3_large_075,3343.71,152.255,512,224,3.99
ese_vovnet19b_slim_dw,3243.14,157.395,512,224,1.9
tf_mobilenetv3_large_minimal_100,3227.09,157.922,512,224,3.92
seresnet18,3222.19,158.398,512,224,11.78
legacy_seresnet18,3130.77,163.021,512,224,11.78
tf_mobilenetv3_large_075,3109.15,163.824,512,224,3.99
mnasnet_075,3102.76,164.235,512,224,3.17
ghostnet_050,3069.54,165.437,512,224,2.59
mobilenetv3_rw,3020.36,168.644,512,224,5.48
mobilenetv3_large_100,2997.0,169.969,512,224,5.48
mobilenetv3_large_100_miil,2996.51,169.991,512,224,5.48
levit_256,2923.52,174.041,512,224,18.89
hardcorenas_a,2875.54,177.351,512,224,5.26
resnet18d,2830.02,180.547,512,224,11.71
mnasnet_b1,2826.42,180.324,512,224,4.38
mnasnet_100,2810.01,181.39,512,224,4.38
tf_mobilenetv3_large_100,2800.51,181.961,512,224,5.48
tinynet_b,2773.33,183.58,512,188,3.73
hardcorenas_b,2665.39,191.158,512,224,5.18
semnasnet_075,2649.51,192.342,512,224,2.91
hardcorenas_c,2643.17,192.764,512,224,5.52
ese_vovnet19b_slim,2613.26,195.551,512,224,3.17
mobilenetv2_075,2538.45,200.896,512,224,2.64
tf_efficientnetv2_b0,2507.59,202.986,512,224,7.14
spnasnet_100,2504.82,203.411,512,224,4.42
levit_256d,2485.6,204.456,512,224,26.21
hardcorenas_d,2483.8,204.983,512,224,7.5
semnasnet_100,2411.15,211.44,512,224,3.89
mnasnet_a1,2396.96,212.676,512,224,3.89
mobilenetv2_100,2381.65,214.209,512,224,3.5
regnetx_002,2371.97,215.169,512,224,2.68
tinynet_a,2274.6,223.875,512,192,6.19
regnety_002,2255.16,226.095,512,224,3.16
ghostnet_100,2251.56,226.062,512,224,5.18
fbnetc_100,2248.09,226.804,512,224,5.57
deit_tiny_patch16_224,2233.8,228.385,512,224,5.72
vit_tiny_patch16_224,2229.44,228.819,512,224,5.72
efficientnet_lite0,2209.28,231.003,512,224,4.65
hardcorenas_f,2207.45,230.839,512,224,8.2
deit_tiny_distilled_patch16_224,2193.62,232.567,512,224,5.91
hardcorenas_e,2183.35,233.392,512,224,8.07
xcit_nano_12_p16_224_dist,2148.4,236.588,512,224,3.05
xcit_nano_12_p16_224,2147.35,236.626,512,224,3.05
tv_resnet34,2081.13,245.45,512,224,21.8
resnet34,2080.93,245.474,512,224,21.8
gluon_resnet34_v1b,2070.75,246.674,512,224,21.8
pit_ti_distilled_224,2069.0,246.563,512,224,5.1
pit_ti_224,2067.14,246.798,512,224,4.85
tf_efficientnet_lite0,2051.57,248.83,512,224,4.65
skresnet18,2011.18,253.956,512,224,11.96
resnet26,1965.66,259.994,512,224,16.0
resnetblur18,1945.89,262.773,512,224,11.69
gernet_m,1920.15,265.947,512,224,21.14
ese_vovnet19b_dw,1905.39,268.216,512,224,6.54
nf_resnet26,1877.61,272.201,512,224,16.0
hrnet_w18_small,1858.6,274.127,512,224,13.19
seresnet34,1854.8,275.134,512,224,21.96
mnasnet_140,1835.61,278.136,512,224,7.12
legacy_seresnet34,1814.57,281.284,512,224,21.96
efficientnet_b0,1800.57,212.181,384,224,5.29
levit_384,1799.63,283.374,512,224,39.13
resnet34d,1799.03,284.0,512,224,21.82
mobilenetv2_110d,1768.64,216.1,384,224,4.52
rexnetr_100,1759.23,217.141,384,224,4.88
selecsls42,1754.3,291.22,512,224,30.35
selecsls42b,1748.59,292.176,512,224,32.46
mixer_b32_224,1728.02,295.478,512,224,60.29
tf_efficientnet_b0_ns,1702.0,224.532,384,224,5.29
tf_efficientnet_b0_ap,1700.69,224.751,384,224,5.29
tf_efficientnet_b0,1700.24,224.78,384,224,5.29
mixer_s16_224,1649.18,309.899,512,224,18.53
semnasnet_140,1640.99,311.119,512,224,6.11
tf_efficientnet_es,1622.88,314.744,512,224,5.44
efficientnet_es,1618.83,315.528,512,224,5.44
efficientnet_es_pruned,1616.07,316.054,512,224,5.44
vit_base_patch32_224_sam,1613.76,316.429,512,224,88.22
vit_base_patch32_224,1612.96,316.587,512,224,88.22
resnet26d,1609.37,317.63,512,224,16.01
tf_efficientnetv2_b1,1607.05,237.525,384,240,8.14
ghostnet_130,1600.19,318.63,512,224,7.36
pit_xs_distilled_224,1591.2,320.859,512,224,11.0
pit_xs_224,1589.39,321.258,512,224,10.62
repvgg_b0,1586.15,321.714,512,224,15.82
resmlp_12_224,1552.02,329.099,512,224,15.35
resmlp_12_distilled_224,1551.98,329.119,512,224,15.35
gmixer_12_224,1551.47,329.195,512,224,12.7
mobilenetv2_140,1539.46,248.646,384,224,6.11
mobilevit_xxs,1515.36,252.293,384,256,1.27
selecsls60,1486.56,343.544,512,224,30.67
selecsls60b,1480.82,344.867,512,224,32.77
nf_seresnet26,1471.47,347.302,512,224,17.4
xcit_tiny_12_p16_224,1422.37,358.218,512,224,6.72
xcit_tiny_12_p16_224_dist,1420.76,358.558,512,224,6.72
efficientnet_lite1,1418.67,179.483,256,240,5.42
vit_small_patch32_384,1414.68,361.045,512,384,22.92
efficientnet_b1_pruned,1384.32,368.407,512,240,6.33
gmlp_ti16_224,1378.39,276.995,384,224,5.87
dla46_c,1367.99,373.533,512,224,1.3
nf_ecaresnet26,1361.11,375.605,512,224,16.0
poolformer_s12,1359.83,375.831,512,224,11.92
rexnetr_130,1350.37,188.438,256,224,7.61
tf_efficientnet_lite1,1343.78,189.557,256,240,5.42
crossvit_tiny_240,1320.33,386.159,512,240,7.01
mobilenetv2_120d,1309.71,194.276,256,224,5.83
resnetv2_50,1296.0,394.281,512,224,25.55
gernet_l,1283.32,398.106,512,256,31.08
rexnet_100,1277.92,299.324,384,224,4.8
crossvit_9_240,1236.5,309.16,384,240,8.55
resnet26t,1227.85,416.475,512,256,16.01
ssl_resnet50,1223.5,417.643,512,224,25.56
resnet50,1222.61,417.956,512,224,25.56
tv_resnet50,1222.03,418.147,512,224,25.56
swsl_resnet50,1222.0,418.189,512,224,25.56
gluon_resnet50_v1b,1221.97,418.181,512,224,25.56
crossvit_9_dagger_240,1195.59,319.731,384,240,8.78
vit_tiny_r_s16_p8_384,1193.45,320.912,384,384,6.36
rexnetr_150,1185.63,214.799,256,224,9.78
fbnetv3_b,1184.86,322.487,384,256,8.6
botnet26t_256,1168.73,327.971,384,256,12.49
tf_efficientnetv2_b2,1157.41,219.649,256,260,10.1
regnetx_004,1150.82,443.834,512,224,5.16
repvgg_a2,1142.14,447.42,512,224,28.21
skresnet34,1140.48,447.803,512,224,22.28
resnetv2_50t,1134.35,450.541,512,224,25.57
fbnetv3_d,1133.65,223.966,256,256,10.31
resnetv2_50d,1131.93,451.499,512,224,25.57
gluon_resnet50_v1c,1131.41,338.54,384,224,25.58
halonet26t,1122.34,341.57,384,256,12.48
convit_tiny,1108.09,461.02,512,224,5.71
efficientnet_lite2,1096.26,232.544,256,260,6.09
dla34,1094.33,467.287,512,224,15.74
convnext_nano_hnf,1075.54,356.227,384,224,15.59
resnet50d,1070.21,357.929,384,224,25.58
gluon_resnet50_v1d,1070.13,357.997,384,224,25.58
resnet50t,1068.62,358.508,384,224,25.57
mixnet_s,1051.2,485.837,512,224,4.13
legacy_seresnext26_32x4d,1051.18,486.414,512,224,16.79
tf_efficientnet_lite2,1045.58,243.879,256,260,6.09
vit_small_resnet26d_224,1042.37,367.395,384,224,63.61
deit_small_patch16_224,1032.62,371.017,384,224,22.05
vit_small_patch16_224,1027.63,372.823,384,224,22.05
regnety_004,1027.25,497.255,512,224,4.34
tf_efficientnet_b1_ns,1026.16,247.955,256,240,7.79
tf_efficientnet_b1_ap,1026.11,248.006,256,240,7.79
tf_efficientnet_b1,1025.11,248.193,256,240,7.79
resnet32ts,1021.77,249.96,256,256,17.96
deit_small_distilled_patch16_224,1010.79,379.058,384,224,22.44
resnet33ts,1009.39,252.998,256,256,19.68
res2net50_48w_2s,1006.25,380.82,384,224,25.29
vovnet39a,1004.46,509.092,512,224,22.6
seresnext26d_32x4d,1002.27,382.471,384,224,16.81
seresnext26t_32x4d,1001.74,382.665,384,224,16.81
seresnext26tn_32x4d,1001.47,382.779,384,224,16.81
legacy_seresnet50,993.86,385.256,384,224,28.09
tf_efficientnet_em,979.63,260.356,256,240,6.9
efficientnet_em,978.46,260.687,256,240,6.9
dla46x_c,973.77,525.047,512,224,1.07
eca_resnet33ts,964.39,264.788,256,256,19.68
pit_s_224,961.0,265.507,256,224,23.46
pit_s_distilled_224,960.14,265.718,256,224,24.04
tf_mixnet_s,958.59,532.877,512,224,4.13
seresnet50,956.87,400.165,384,224,28.09
efficientnet_b1,954.91,266.596,256,256,7.79
seresnet33ts,954.85,267.313,256,256,19.78
ecaresnetlight,952.86,536.422,512,224,30.16
vit_base2_patch32_256,950.48,537.853,512,256,119.46
ese_vovnet39b,947.73,539.578,512,224,24.57
ecaresnext50t_32x4d,947.69,404.65,384,224,15.41
ecaresnext26t_32x4d,947.22,404.844,384,224,15.41
dla60,945.45,405.196,384,224,22.04
gluon_resnet50_v1s,943.31,406.227,384,224,25.68
resnetaa50d,941.94,406.82,384,224,25.58
eca_vovnet39b,939.18,544.514,512,224,22.6
vgg11,930.6,550.023,512,224,132.86
gcresnet33ts,927.58,274.995,256,256,19.88
lambda_resnet26rpt_256,921.55,207.755,192,256,10.99
dla60x_c,921.01,554.951,512,224,1.32
ecaresnet50d_pruned,911.85,560.565,512,224,19.94
resnetblur50,909.58,421.362,384,224,25.56
mobilevit_xs,909.51,210.0,192,256,2.32
cspresnet50,906.79,422.612,384,256,21.62
rexnetr_200,896.75,212.966,192,224,16.52
coat_lite_tiny,890.79,430.193,384,224,5.72
nf_seresnet50,886.81,431.807,384,224,28.09
dpn68b,878.29,436.039,384,224,12.61
selecsls84,872.56,585.545,512,224,50.95
twins_svt_small,868.85,440.366,384,224,24.06
hrnet_w18_small_v2,867.42,587.948,512,224,15.6
seresnet50t,865.51,442.504,384,224,28.1
cspresnext50,862.61,444.309,384,224,20.57
resnetrs50,861.96,444.354,384,224,35.69
cspresnet50w,860.12,445.567,384,256,28.12
cspresnet50d,849.09,451.363,384,256,21.64
densenet121,845.28,301.077,256,224,7.98
tv_densenet121,845.28,301.063,256,224,7.98
rexnet_150,842.23,302.82,256,224,9.73
tv_resnext50_32x4d,836.18,458.41,384,224,25.03
swsl_resnext50_32x4d,836.09,458.464,384,224,25.03
res2net50_26w_4s,835.77,458.208,384,224,25.7
coat_lite_mini,833.77,459.672,384,224,11.01
vit_base_resnet26d_224,833.37,459.491,384,224,101.4
resnext50_32x4d,832.87,460.244,384,224,25.03
ssl_resnext50_32x4d,832.34,460.521,384,224,25.03
dpn68,831.95,460.44,384,224,12.61
gluon_resnext50_32x4d,831.77,460.799,384,224,25.03
vovnet57a,828.64,616.994,512,224,36.64
efficientnet_b2_pruned,825.77,308.457,256,260,8.31
resnetblur50d,818.48,311.928,256,224,25.58
skresnet50,810.12,472.628,384,224,25.8
tf_efficientnet_b2_ap,809.72,235.646,192,260,9.11
tf_efficientnet_b2_ns,809.32,235.717,192,260,9.11
tf_efficientnet_b2,809.06,235.843,192,260,9.11
vgg11_bn,805.38,476.555,384,224,132.87
densenet121d,805.31,316.045,256,224,8.0
nf_ecaresnet50,804.89,476.102,384,224,25.56
rexnet_130,801.42,318.304,256,224,7.56
ecaresnet50d,793.07,483.245,384,224,25.58
ese_vovnet57b,790.94,484.567,384,224,38.61
regnetx_006,790.56,646.782,512,224,6.2
gcresnet50t,788.09,323.372,256,256,25.9
regnety_006,787.76,648.873,512,224,6.06
convnext_tiny,781.17,326.737,256,224,28.59
tf_inception_v3,774.78,494.217,384,299,23.83
gluon_inception_v3,774.48,494.404,384,299,23.83
seresnetaa50d,773.82,329.682,256,224,28.11
inception_v3,772.99,495.38,384,299,23.83
adv_inception_v3,769.85,497.351,384,299,23.83
resmlp_24_distilled_224,767.92,331.893,256,224,30.02
resnext50d_32x4d,765.61,333.516,256,224,25.05
resmlp_24_224,762.25,334.383,256,224,30.02
gmixer_24_224,759.72,335.461,256,224,24.72
resnetv2_101,757.49,336.449,256,224,44.54
res2net50_14w_8s,756.34,336.303,256,224,25.06
xcit_nano_12_p16_384_dist,754.95,337.338,256,384,3.05
sehalonet33ts,749.52,340.746,256,256,13.69
densenetblur121d,739.98,344.167,256,224,8.0
dla60_res2net,736.45,346.205,256,224,20.85
skresnet50d,736.23,346.324,256,224,25.82
mobilevit_s,734.66,260.236,192,256,5.58
gluon_resnet101_v1b,731.02,348.601,256,224,44.55
tv_resnet101,727.65,350.31,256,224,44.55
resnet101,727.5,350.369,256,224,44.55
xcit_tiny_24_p16_224,726.25,349.143,256,224,12.12
xcit_tiny_24_p16_224_dist,725.28,349.489,256,224,12.12
efficientnet_b2,724.21,263.654,192,288,9.11
twins_pcpvt_small,724.17,351.883,256,224,24.11
efficientnet_b2a,723.56,263.856,192,288,9.11
ecaresnet101d_pruned,714.19,715.116,512,224,24.88
nf_resnet50,710.87,539.322,384,288,25.56
nf_resnet101,707.69,540.968,384,224,44.55
seresnext50_32x4d,706.91,361.01,256,224,27.56
gluon_seresnext50_32x4d,706.58,361.114,256,224,27.56
efficientnet_b0_gn,705.84,361.599,256,224,5.29
legacy_seresnext50_32x4d,704.96,362.028,256,224,27.56
nf_regnet_b0,703.19,726.933,512,256,8.76
darknet53,701.97,363.892,256,256,41.61
resnetv2_101d,699.92,364.177,256,224,44.56
densenet169,698.35,364.121,256,224,14.15
gluon_resnet101_v1c,697.85,365.313,256,224,44.57
dla60x,694.54,367.645,256,224,17.35
poolformer_s24,690.11,369.675,256,224,21.39
semobilevit_s,684.71,279.143,192,256,5.74
efficientnetv2_rw_t,684.48,278.424,192,288,13.65
vit_small_r26_s32_224,682.34,373.878,256,224,36.43
convnext_tiny_hnf,681.83,374.501,256,224,28.59
gluon_resnet101_v1d,674.08,378.237,256,224,44.57
tf_efficientnetv2_b3,670.7,284.467,192,300,14.36
xcit_small_12_p16_224,670.22,380.208,256,224,26.25
xcit_small_12_p16_224_dist,669.97,380.26,256,224,26.25
sebotnet33ts_256,666.38,191.3,128,256,13.7
rexnet_200,665.99,287.162,192,224,16.37
vgg13,663.21,578.818,384,224,133.05
regnety_008,661.82,772.637,512,224,6.26
wide_resnet50_2,661.04,580.088,384,224,68.88
dla102,650.38,392.047,256,224,33.27
gmlp_s16_224,648.9,294.316,192,224,19.42
vit_base_resnet50d_224,646.39,394.444,256,224,110.97
swin_tiny_patch4_window7_224,639.35,399.428,256,224,28.29
ecaresnet26t,628.75,406.586,256,320,16.01
repvgg_b1,627.31,815.089,512,224,57.42
gluon_resnet101_v1s,624.3,408.497,256,224,44.67
crossvit_small_240,624.04,408.587,256,240,26.86
resnetaa101d,621.23,410.532,256,224,44.57
eca_botnext26ts_256,618.03,413.613,256,256,10.59
resnext26ts,615.62,623.252,384,256,10.3
gc_efficientnetv2_rw_t,605.04,314.593,192,288,13.68
eca_halonext26ts,604.41,422.932,256,256,10.76
seresnext26ts,598.53,427.051,256,256,10.39
eca_resnext26ts,598.25,427.346,256,256,10.3
convnext_tiny_hnfd,592.58,430.992,256,224,28.63
regnetx_008,591.8,864.35,512,224,7.26
resnetv2_50x1_bit_distilled,589.64,324.794,192,224,25.55
legacy_seresnet101,588.41,432.899,256,224,49.33
gcresnext26ts,585.17,436.656,256,256,10.48
halonet50ts,584.39,327.573,192,256,22.73
xcit_nano_12_p8_224,583.42,437.05,256,224,3.05
cait_xxs24_224,582.93,436.673,256,224,11.96
xcit_nano_12_p8_224_dist,581.01,438.848,256,224,3.05
mixer_b16_224,580.44,440.268,256,224,59.88
mixer_b16_224_miil,579.94,440.653,256,224,59.88
swin_s3_tiny_224,578.76,441.339,256,224,28.33
seresnet101,574.05,443.691,256,224,49.33
mixnet_m,573.23,891.634,512,224,5.01
res2net50_26w_6s,569.28,447.908,256,224,37.05
vgg13_bn,568.79,449.813,256,224,133.05
crossvit_15_240,568.46,335.957,192,240,27.53
resnetblur101d,567.71,449.352,256,224,44.57
cspdarknet53,565.64,451.547,256,256,27.64
efficientnet_lite3,563.01,226.244,128,300,8.2
tf_efficientnet_lite3,561.05,227.067,128,300,8.2
crossvit_15_dagger_240,549.96,347.22,192,240,28.21
resnext101_32x4d,548.6,465.031,256,224,44.18
tf_mixnet_m,547.03,700.438,384,224,5.01
swsl_resnext101_32x4d,546.13,467.192,256,224,44.18
gluon_resnext101_32x4d,545.31,467.899,256,224,44.18
ssl_resnext101_32x4d,545.31,467.944,256,224,44.18
densenet201,539.32,353.029,192,224,20.01
vgg16,536.49,715.548,384,224,138.36
bat_resnext26ts,533.44,478.688,256,256,10.73
nf_seresnet101,532.2,478.733,256,224,49.33
vit_base_r26_s32_224,528.57,361.981,192,224,101.38
resnetv2_152,524.49,485.916,256,224,60.19
botnet50ts_256,524.41,243.109,128,256,22.74
res2net101_26w_4s,523.46,486.536,256,224,45.21
efficientnet_b3_pruned,519.43,491.117,256,300,9.86
vit_base_patch32_384,517.32,494.013,256,384,88.3
vit_large_patch32_224,511.41,498.976,256,224,306.54
halo2botnet50ts_256,510.64,375.017,192,256,22.64
mixer_l32_224,510.1,374.908,192,224,206.94
swin_v2_cr_tiny_224,506.89,377.55,192,224,28.33
resmlp_36_distilled_224,505.72,377.435,192,224,44.69
resmlp_36_224,505.39,377.735,192,224,44.69
vit_tiny_patch16_384,503.94,253.174,128,384,5.79
res2next50,502.84,507.818,256,224,24.67
dla102x,501.98,380.938,192,224,26.31
swin_v2_cr_tiny_ns_224,501.36,381.688,192,224,28.33
resnet152,499.83,381.783,192,224,60.19
gluon_resnet152_v1b,499.79,381.933,192,224,60.19
tv_resnet152,496.47,384.401,192,224,60.19
xception,494.06,258.29,128,299,22.86
visformer_tiny,491.83,1040.332,512,224,10.32
mixnet_l,488.23,784.993,384,224,7.33
gluon_resnet152_v1c,485.58,393.139,192,224,60.21
resnet50_gn,484.73,395.256,192,224,25.56
resnetv2_152d,484.59,393.938,192,224,60.2
twins_pcpvt_base,480.18,397.102,192,224,43.83
nest_tiny,477.31,267.267,128,224,17.06
res2net50_26w_8s,476.93,534.581,256,224,48.4
ecaresnet101d,475.58,536.508,256,224,44.57
convnext_small,473.89,403.445,192,224,50.22
gluon_resnet152_v1d,473.52,403.216,192,224,60.21
tf_mixnet_l,470.86,542.123,256,224,7.33
jx_nest_tiny,470.71,271.015,128,224,17.06
vgg16_bn,469.97,544.386,256,224,138.37
nf_ecaresnet101,465.87,547.628,256,224,44.55
coat_lite_small,463.34,412.922,192,224,19.84
poolformer_s36,458.24,417.134,192,224,30.86
efficientnet_el,455.42,279.993,128,300,10.59
efficientnet_el_pruned,454.32,280.643,128,300,10.59
vgg19,451.86,849.574,384,224,143.67
convit_small,450.19,425.458,192,224,27.78
fbnetv3_g,449.02,283.045,128,288,16.62
ese_vovnet99b,448.97,568.658,256,224,63.2
seresnext101_32x4d,448.16,426.163,192,224,48.96
gluon_seresnext101_32x4d,447.49,426.94,192,224,48.96
gluon_resnet152_v1s,446.73,427.49,192,224,60.32
legacy_seresnext101_32x4d,446.01,428.263,192,224,48.96
nf_regnet_b3,442.03,577.378,256,320,18.59
tf_efficientnet_el,441.61,288.785,128,300,10.59
ese_vovnet39b_evos,439.23,290.493,128,224,24.58
dla60_res2next,437.89,583.208,256,224,17.03
volo_d1_224,437.25,437.756,192,224,26.63
dla169,436.98,436.866,192,224,53.39
skresnext50_32x4d,435.09,586.972,256,224,27.48
hrnet_w32,433.46,438.263,192,224,41.23
vit_small_resnet50d_s16_224,432.94,442.239,192,224,57.53
twins_svt_base,429.83,444.617,192,224,56.07
hrnet_w18,419.32,605.846,256,224,21.3
crossvit_18_240,400.15,317.858,128,240,43.27
vgg19_bn,399.74,640.035,256,224,143.68
ecaresnet50t,399.44,319.549,128,320,25.57
inception_v4,397.78,480.469,192,299,42.68
tf_efficientnet_b3,393.44,323.695,128,300,12.23
swin_small_patch4_window7_224,393.38,486.272,192,224,49.61
legacy_seresnet152,392.87,485.406,192,224,66.82
tf_efficientnet_b3_ap,392.08,324.783,128,300,12.23
vit_base_patch16_224_miil,391.87,489.169,192,224,86.54
tf_efficientnet_b3_ns,391.61,325.176,128,300,12.23
crossvit_18_dagger_240,387.95,327.891,128,240,44.27
vit_base_patch16_224,385.23,497.575,192,224,86.57
vit_base_patch16_224_sam,384.98,497.891,192,224,86.57
deit_base_patch16_224,384.15,498.956,192,224,86.57
cait_xxs36_224,380.29,501.012,192,224,17.3
repvgg_b2,380.03,1346.183,512,224,89.02
regnetx_016,379.21,1349.264,512,224,9.19
deit_base_distilled_patch16_224,378.28,506.739,192,224,87.34
densenet161,376.13,337.907,128,224,28.68
haloregnetz_b,375.52,680.247,256,224,11.68
xcit_tiny_12_p8_224,374.86,339.659,128,224,6.71
xcit_tiny_12_p8_224_dist,374.8,339.672,128,224,6.71
seresnet152,372.93,339.954,128,224,66.82
dla102x2,362.92,351.15,128,224,41.28
wide_resnet101_2,360.46,531.121,192,224,126.89
gluon_resnext101_64x4d,357.83,356.147,128,224,83.46
efficientnet_b3a,354.6,359.335,128,320,12.23
resnet200,354.57,357.998,128,224,64.67
xception41p,353.88,360.846,128,299,26.91
regnety_016,353.36,1447.082,512,224,11.2
efficientnet_b3,353.18,360.796,128,320,12.23
beit_base_patch16_224,352.29,543.93,192,224,86.53
resnest14d,349.84,1463.045,512,224,10.61
hrnet_w30,346.82,733.381,256,224,37.71
ens_adv_inception_resnet_v2,341.5,558.819,192,299,55.84
inception_resnet_v2,341.03,559.722,192,299,55.84
xcit_small_24_p16_224_dist,341.01,371.894,128,224,47.67
tnt_s_patch16_224,340.3,562.32,192,224,23.76
xcit_small_24_p16_224,339.02,373.952,128,224,47.67
efficientnet_lite4,334.87,189.779,64,380,13.01
dpn92,332.25,769.002,256,224,37.67
nf_regnet_b1,330.04,1549.911,512,288,10.22
twins_pcpvt_large,327.73,386.698,128,224,60.99
resnet101d,327.41,389.353,128,320,44.57
convnext_small_in22ft1k,327.31,389.301,128,224,88.59
convnext_base,327.3,389.374,128,224,88.59
convnext_base_in22ft1k,326.61,390.177,128,224,88.59
convnext_tiny_in22ft1k,324.94,392.181,128,224,88.59
tf_efficientnet_lite4,322.62,197.041,64,380,13.01
resnetrs101,321.74,395.61,128,288,63.62
pit_b_224,318.92,400.391,128,224,73.76
pit_b_distilled_224,318.09,401.455,128,224,74.79
gcresnext50ts,316.73,604.706,192,256,15.67
repvgg_b3,315.56,1215.795,384,224,123.09
gluon_seresnext101_64x4d,313.23,406.424,128,224,88.23
regnetz_d8,311.3,204.013,64,320,23.37
poolformer_m36,307.82,413.95,128,224,56.17
xception41,305.16,418.228,128,299,26.97
resnetv2_50d_gn,304.62,419.347,128,288,25.57
coat_tiny,304.05,418.955,128,224,5.5
vit_small_patch16_36x1_224,302.22,420.928,128,224,64.67
swin_v2_cr_small_224,302.05,421.43,128,224,49.7
cait_s24_224,301.09,422.497,128,224,46.92
mixnet_xl,300.81,849.169,256,224,11.9
vit_small_patch16_18x2_224,299.9,424.102,128,224,64.67
resnetv2_50d_frn,299.64,426.047,128,224,25.59
efficientnetv2_s,299.05,318.819,96,384,21.46
twins_svt_large,298.59,426.599,128,224,99.27
tf_efficientnetv2_s,297.45,320.544,96,384,21.46
tf_efficientnetv2_s_in21ft1k,295.84,322.238,96,384,21.46
efficientnetv2_rw_s,295.58,214.343,64,384,23.94
nest_small,295.19,323.571,96,224,38.35
jx_nest_small,293.04,325.974,96,224,38.35
hrnet_w40,291.68,653.529,192,224,57.56
regnetz_005,286.8,1783.819,512,224,7.12
nf_regnet_b2,284.19,1799.964,512,272,14.31
dpn98,283.21,450.397,128,224,61.57
gluon_xception65,280.82,339.911,96,299,39.92
xception65,279.18,341.922,96,299,39.92
resnet51q,277.93,689.969,192,288,35.7
nf_regnet_b4,277.28,459.47,128,384,30.21
swin_s3_small_224,277.05,344.678,96,224,49.74
swin_base_patch4_window7_224,275.83,462.244,128,224,87.77
xception65p,274.95,464.241,128,299,39.82
gmlp_b16_224,270.89,352.851,96,224,73.08
hrnet_w48,266.5,475.648,128,224,77.47
resnest26d,258.35,1485.604,384,224,17.07
resnest50d_1s4x24d,258.11,990.508,256,224,25.68
xcit_tiny_24_p16_384_dist,251.56,378.207,96,384,12.12
regnetz_c16,250.16,510.248,128,320,13.46
crossvit_base_240,249.99,382.366,96,240,105.03
coat_mini,247.34,515.482,128,224,10.34
xcit_medium_24_p16_224,244.65,519.851,128,224,84.4
xcit_medium_24_p16_224_dist,244.08,521.069,128,224,84.4
hrnet_w44,241.77,789.352,192,224,67.06
efficientnet_b4,241.47,262.953,64,384,19.34
volo_d2_224,238.72,400.292,96,224,58.68
tf_efficientnet_b4,236.52,268.528,64,380,19.34
tf_efficientnet_b4_ap,236.39,268.69,64,380,19.34
tf_efficientnet_b4_ns,236.1,269.028,64,380,19.34
vit_small_patch16_384,235.52,270.897,64,384,22.2
resnetv2_50d_evob,235.19,406.951,96,224,25.59
tresnet_m,234.84,2177.505,512,224,31.39
nfnet_l0,233.22,1096.449,256,288,35.07
visformer_small,232.72,1649.37,384,224,40.22
xcit_small_12_p16_384_dist,230.87,414.048,96,384,26.25
vit_large_r50_s32_224,228.88,417.083,96,224,328.99
convit_base,228.65,558.76,128,224,86.54
eca_nfnet_l0,226.92,1127.142,256,288,24.14
resnetv2_50d_evos,223.6,285.051,64,288,25.59
tnt_b_patch16_224,222.54,573.324,128,224,65.41
vit_small_r26_s32_384,221.41,287.746,64,384,36.47
densenet264,220.0,432.388,96,224,72.69
swin_s3_base_224,219.07,435.557,96,224,71.13
hrnet_w64,218.91,579.973,128,224,128.06
resnext101_64x4d,217.22,440.378,96,288,83.46
resnet152d,216.09,441.927,96,320,60.21
xception71,215.62,294.681,64,299,42.34
swin_v2_cr_base_224,215.23,443.662,96,224,87.88
dpn131,211.54,603.038,128,224,79.25
nest_base,210.35,302.564,64,224,67.72
vit_base_r50_s16_224,208.87,457.959,96,224,98.66
jx_nest_base,208.37,305.48,64,224,67.72
resnet61q,207.91,614.63,128,288,36.85
mixnet_xxl,202.79,629.315,128,224,23.96
xcit_nano_12_p8_384_dist,196.18,324.445,64,384,3.05
poolformer_m48,193.91,492.638,96,224,73.47
xcit_tiny_24_p8_224,190.8,499.85,96,224,12.11
xcit_tiny_24_p8_224_dist,190.62,500.3,96,224,12.11
seresnet200d,190.31,500.165,96,256,71.86
ecaresnet200d,182.23,523.49,96,256,64.69
regnetz_b16,181.6,1055.83,192,288,9.72
convnext_large_in22ft1k,180.86,529.028,96,224,197.77
convnext_large,180.64,529.707,96,224,197.77
convmixer_768_32,179.25,534.249,96,224,21.11
repvgg_b1g4,178.41,2868.637,512,224,39.97
regnety_032,178.01,1436.656,256,288,19.44
regnetx_032,177.68,2160.015,384,224,15.3
resnest50d,177.64,1439.786,256,224,27.48
gluon_senet154,177.29,538.24,96,224,115.09
senet154,176.81,539.67,96,224,115.09
halonet_h1,175.85,362.526,64,256,8.1
legacy_senet154,175.45,543.752,96,224,115.09
xcit_small_12_p8_224,174.99,363.954,64,224,26.21
xcit_small_12_p8_224_dist,174.85,364.256,64,224,26.21
seresnet152d,173.74,364.967,64,320,66.84
dpn107,173.23,552.482,96,224,86.92
mixer_l16_224,172.58,554.751,96,224,208.2
resnetrs152,171.14,370.433,64,320,86.62
resnest50d_4s2x40d,167.22,1529.64,256,224,30.42
resnet200d,166.19,382.131,64,320,64.69
volo_d3_224,162.25,391.709,64,224,86.33
regnetx_040,161.87,2371.182,384,224,22.12
vit_large_patch32_384,161.83,591.666,96,384,306.63
efficientnet_b3_gn,160.22,397.781,64,320,11.73
swin_large_patch4_window7_224,151.35,420.987,64,224,196.53
regnetx_080,150.02,2558.515,384,224,39.57
regnety_040s_gn,149.61,853.983,128,224,20.65
efficientnetv2_m,149.31,318.263,48,416,54.14
ssl_resnext101_32x8d,147.46,866.477,128,224,88.79
resnext101_32x8d,147.32,867.312,128,224,88.79
swsl_resnext101_32x8d,147.16,868.264,128,224,88.79
ig_resnext101_32x8d,146.91,869.691,128,224,88.79
regnetz_e8,146.27,326.163,48,320,57.7
resnetv2_50x1_bitm,140.77,340.162,48,448,25.55
seresnet269d,137.16,460.633,64,256,113.67
xcit_large_24_p16_224,136.77,464.553,64,224,189.1
xcit_large_24_p16_224_dist,136.73,464.738,64,224,189.1
xcit_tiny_12_p8_384_dist,128.06,373.098,48,384,6.71
efficientnetv2_rw_m,126.21,250.013,32,416,53.24
regnetx_064,124.13,2061.422,256,224,26.21
resnetrs200,123.75,383.587,48,320,93.21
dm_nfnet_f0,121.58,2104.417,256,256,71.49
swin_v2_cr_large_224,120.98,394.327,48,224,196.68
regnety_040,120.25,1595.17,192,288,20.65
nfnet_f0,119.63,2138.729,256,256,71.49
regnetv_040,118.92,1074.873,128,288,20.64
ese_vovnet99b_iabn,118.27,3243.951,384,224,63.2
xcit_small_24_p16_384_dist,117.41,405.393,48,384,47.67
regnetz_b16_evos,117.22,544.08,64,288,9.74
crossvit_15_dagger_408,116.43,273.026,32,408,28.5
efficientnet_b0_g8_gn,115.43,2216.717,256,224,6.56
vit_large_patch16_224,115.39,553.095,64,224,304.33
regnetz_c16_evos,115.15,414.978,48,320,13.49
vit_base_patch16_18x2_224,114.12,558.126,64,224,256.73
convnext_xlarge_in22ft1k,114.03,559.475,64,224,350.2
convnext_tiny_384_in22ft1k,112.53,424.859,48,384,88.59
convnext_small_384_in22ft1k,112.49,424.983,48,384,88.59
convnext_base_384_in22ft1k,112.43,425.187,48,384,88.59
swin_v2_cr_tiny_384,111.07,286.85,32,384,28.33
tf_efficientnetv2_m,109.5,289.071,32,480,54.14
tf_efficientnetv2_m_in21ft1k,109.37,289.435,32,480,54.14
beit_large_patch16_224,106.59,598.365,64,224,304.43
volo_d1_384,104.96,303.527,32,384,26.78
tresnet_l,104.79,4882.686,512,224,55.99
repvgg_b2g4,102.33,5002.104,512,224,61.76
eca_nfnet_l1,101.26,1262.246,128,320,41.41
volo_d4_224,101.17,471.916,48,224,192.96
cspdarknet53_iabn,98.65,3890.155,384,256,27.64
cait_xxs24_384,98.52,484.701,48,384,12.03
efficientnet_b5,97.28,326.485,32,456,30.39
tf_efficientnet_b5,95.75,331.792,32,456,30.39
tf_efficientnet_b5_ns,95.73,331.833,32,456,30.39
vit_base_patch16_384,95.69,333.568,32,384,86.86
deit_base_patch16_384,95.67,333.658,32,384,86.86
regnetz_d8_evos,95.66,332.456,32,320,23.46
tf_efficientnet_b5_ap,95.45,332.725,32,456,30.39
regnetz_040,94.56,674.98,64,320,27.12
regnetz_040h,94.14,678.01,64,320,28.94
deit_base_distilled_patch16_384,93.65,340.87,32,384,87.63
tresnet_xl,90.76,4227.401,384,224,78.44
cspresnext50_iabn,89.98,4265.102,384,256,20.57
resnest101e,89.71,1424.366,128,256,48.28
crossvit_18_dagger_408,87.99,361.633,32,408,44.61
xcit_small_24_p8_224,87.73,361.414,32,224,47.63
xcit_small_24_p8_224_dist,87.7,361.441,32,224,47.63
resnetv2_101x1_bitm,86.89,366.637,32,448,44.54
nf_regnet_b5,86.53,736.892,64,456,49.74
resnetv2_152x2_bit_teacher,86.4,368.012,32,224,236.34
repvgg_b3g4,84.75,4530.071,384,224,83.83
vit_large_patch14_224,84.3,567.814,48,224,304.2
beit_base_patch16_384,82.73,385.724,32,384,86.74
seresnext101_32x8d,81.65,781.61,64,288,93.57
xcit_medium_24_p16_384_dist,81.08,391.168,32,384,84.4
ecaresnet269d,77.88,406.425,32,352,102.09
regnetx_120,77.54,3300.773,256,224,46.11
pnasnet5large,76.44,414.721,32,331,86.06
vit_large_r50_s32_384,75.76,419.984,32,384,329.09
regnety_120,75.34,2547.007,192,224,51.82
resnetrs270,75.27,419.128,32,352,129.86
swin_base_patch4_window12_384,73.61,432.836,32,384,87.9
regnety_064,72.69,1759.173,128,288,30.58
regnetz_d32,72.18,885.049,64,320,27.58
regnetv_064,71.81,1780.738,128,288,30.58
resmlp_big_24_224,68.34,466.717,32,224,129.14
resmlp_big_24_224_in22ft1k,68.31,466.955,32,224,129.14
resmlp_big_24_distilled_224,68.26,467.278,32,224,129.14
regnety_320,67.49,1895.092,128,224,145.05
nasnetalarge,66.93,473.19,32,331,88.75
swin_v2_cr_small_384,66.26,359.874,24,384,49.7
cait_xs24_384,65.98,482.447,32,384,26.67
regnety_080,65.45,1954.421,128,288,39.18
regnetx_160,65.01,2952.336,192,224,54.28
xcit_tiny_24_p8_384_dist,64.61,492.002,32,384,12.11
volo_d5_224,64.26,494.733,32,224,295.46
cait_xxs36_384,63.75,498.023,32,384,17.37
vit_base_patch8_224,62.96,380.293,24,224,86.58
xcit_medium_24_p8_224,62.71,506.97,32,224,84.32
xcit_medium_24_p8_224_dist,62.63,507.407,32,224,84.32
efficientnet_b3_g8_gn,62.43,1023.448,64,320,14.25
convnext_large_384_in22ft1k,61.7,516.836,32,384,197.77
convmixer_1024_20_ks9_p14,61.37,4170.722,256,224,24.38
efficientnet_b0_g16_evos,60.46,6350.16,384,224,8.11
tf_efficientnetv2_l_in21ft1k,60.25,261.19,16,480,118.52
xcit_small_12_p8_384_dist,60.04,398.02,24,384,26.21
efficientnetv2_l,59.19,265.957,16,480,118.52
vit_base_resnet50_384,59.0,405.136,24,384,98.95
tf_efficientnetv2_l,58.87,267.382,16,480,118.52
vit_base_r50_s16_384,58.85,406.196,24,384,98.95
volo_d2_384,58.21,273.125,16,384,58.87
tresnet_m_448,53.55,3582.526,192,448,31.39
cait_s24_384,49.8,479.385,24,384,47.06
regnety_160,48.14,1993.0,96,288,83.59
ig_resnext101_32x16d,47.52,2018.737,96,224,194.03
swsl_resnext101_32x16d,47.43,2022.391,96,224,194.03
xcit_large_24_p16_384_dist,47.39,502.961,24,384,189.1
ssl_resnext101_32x16d,47.29,2028.601,96,224,194.03
resnetrs350,47.22,500.442,24,384,163.96
swin_v2_cr_base_384,47.19,336.677,16,384,87.88
swin_v2_cr_huge_224,46.26,343.404,16,224,657.83
regnetx_320,46.16,2771.769,128,224,107.81
eca_nfnet_l2,44.82,1425.204,64,384,56.72
efficientnet_b6,42.73,371.597,16,528,43.04
tf_efficientnet_b6,41.54,382.29,16,528,43.04
tf_efficientnet_b6_ns,41.5,382.621,16,528,43.04
tf_efficientnet_b6_ap,41.36,384.088,16,528,43.04
swin_large_patch4_window12_384,41.02,388.265,16,384,196.74
nfnet_f1,40.44,2371.478,96,320,132.63
vit_huge_patch14_224,39.64,401.543,16,224,632.05
dm_nfnet_f1,38.26,1670.645,64,320,132.63
convnext_xlarge_384_in22ft1k,37.04,430.195,16,384,350.2
efficientnet_b7,36.68,214.625,8,600,66.35
efficientnetv2_xl,36.54,322.755,12,512,208.12
tf_efficientnetv2_xl_in21ft1k,36.36,324.371,12,512,208.12
tf_efficientnet_b7_ap,36.21,217.402,8,600,66.35
tf_efficientnet_b7,36.05,218.422,8,600,66.35
tf_efficientnet_b7_ns,35.41,221.975,8,600,66.35
xcit_large_24_p8_224,34.96,454.269,16,224,188.93
xcit_large_24_p8_224_dist,34.92,454.696,16,224,188.93
resnetrs420,32.2,487.619,16,416,191.89
cait_s36_384,32.09,494.814,16,384,68.37
resnest200e,32.0,1494.763,48,320,70.2
densenet264d_iabn,31.93,4003.992,128,224,72.74
resnetv2_50x3_bitm,31.81,502.194,16,448,217.32
xcit_small_24_p8_384_dist,29.97,396.971,12,384,47.63
resnetv2_152x2_bit_teacher_384,29.92,398.698,12,384,236.34
vit_large_patch16_384,28.85,414.372,12,384,304.72
swin_v2_cr_large_384,28.47,419.18,12,384,196.68
tresnet_l_448,25.64,4988.984,128,448,55.99
beit_large_patch16_384,25.11,475.839,12,384,305.0
volo_d3_448,24.79,320.233,8,448,86.63
eca_nfnet_l3,24.19,1319.468,32,448,72.04
tresnet_xl_448,23.17,4140.191,96,448,78.44
nfnet_f2,22.36,2143.929,48,352,193.78
vit_giant_patch14_224,22.25,356.944,8,224,1012.61
dm_nfnet_f2,22.12,2166.49,48,352,193.78
efficientnet_cc_b0_8e,21.78,44.083,1,224,24.01
resnetv2_152x2_bitm,21.74,365.619,8,448,236.34
tf_efficientnet_cc_b0_4e,21.44,44.859,1,224,13.31
tf_efficientnet_cc_b0_8e,20.98,45.875,1,224,24.01
xcit_medium_24_p8_384_dist,20.42,388.21,8,384,84.32
efficientnet_cc_b0_4e,20.36,47.276,1,224,13.31
ig_resnext101_32x32d,18.17,1760.063,32,224,468.53
volo_d4_448,17.6,338.311,6,448,193.41
resnetv2_101x3_bitm,17.53,454.801,8,448,387.93
tf_efficientnet_cc_b1_8e,17.15,56.006,1,240,39.72
efficientnet_cc_b1_8e,16.21,59.398,1,240,39.72
resnest269e,13.09,1826.778,24,416,110.93
tf_efficientnet_b8_ap,12.18,488.771,6,672,87.41
efficientnet_b8,12.12,491.31,6,672,87.41
nfnet_f3,12.08,1982.52,24,416,254.92
xcit_large_24_p8_384_dist,11.91,500.622,6,384,188.93
tf_efficientnet_b8,11.9,500.516,6,672,87.41
cait_m36_384,11.87,501.638,6,384,271.22
dm_nfnet_f3,11.74,2040.434,24,416,254.92
volo_d5_448,11.52,343.952,4,448,295.91
swin_v2_cr_huge_384,10.9,364.423,4,384,657.94
convmixer_1536_20,9.63,4981.706,48,224,51.63
beit_large_patch16_512,9.42,422.773,4,512,305.67
tf_efficientnet_l2_ns_475,9.0,327.787,3,475,480.31
ig_resnext101_32x48d,8.5,1880.808,16,224,828.41
volo_d5_512,8.05,369.448,3,512,296.09
nfnet_f4,6.47,1849.874,12,512,316.07
dm_nfnet_f4,6.2,1929.065,12,512,316.07
cait_m48_448,4.75,415.897,2,448,356.46
nfnet_f5,4.63,1719.792,8,544,377.21
resnetv2_152x4_bitm,4.49,443.185,2,480,936.53
dm_nfnet_f5,4.47,1782.137,8,544,377.21
nfnet_f6,3.5,1707.387,6,576,438.36
dm_nfnet_f6,3.39,1759.608,6,576,438.36
nfnet_f7,2.67,1489.771,4,608,499.5
efficientnet_l2,2.09,473.733,1,800,480.31
tf_efficientnet_l2_ns,2.09,474.031,1,800,480.31
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nhwc-pt112-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,11915.85,41.681,512,106,2.04
mobilenetv3_small_050,11290.99,44.293,512,224,1.59
lcnet_035,10015.98,50.125,512,224,1.64
lcnet_050,9286.37,54.37,512,224,1.88
tf_mobilenetv3_small_minimal_100,9042.22,55.986,512,224,2.04
mobilenetv3_small_075,8679.98,58.254,512,224,2.04
mobilenetv3_small_100,8035.08,62.981,512,224,2.54
tinynet_d,7990.69,63.223,512,152,2.34
tf_mobilenetv3_small_075,7930.1,63.8,512,224,2.04
tf_mobilenetv3_small_100,7330.24,69.047,512,224,2.54
lcnet_075,6950.91,73.156,512,224,2.36
levit_128s,6539.16,77.346,512,224,7.78
resnet10t,6318.63,80.774,512,176,5.44
mnasnet_small,5607.09,90.422,512,224,2.03
lcnet_100,5354.67,95.126,512,224,2.95
mixer_s32_224,4943.04,103.013,512,224,19.1
mobilenetv2_035,4789.43,106.101,512,224,1.68
mnasnet_050,4680.08,108.62,512,224,2.22
levit_128,4558.28,111.213,512,224,9.21
cs3darknet_focus_s,4469.48,114.041,512,256,3.27
vit_small_patch32_224,4445.76,114.324,512,224,22.88
tinynet_c,4167.16,121.826,512,184,2.46
gernet_s,4165.03,122.198,512,224,8.17
cs3darknet_s,4110.51,124.007,512,256,3.28
regnetx_002,4105.04,124.027,512,224,2.68
mobilenetv2_050,4051.14,125.606,512,224,1.97
vit_tiny_r_s16_p8_224,4025.23,126.328,512,224,6.34
semnasnet_050,3904.91,130.185,512,224,2.08
regnety_002,3777.81,134.562,512,224,3.16
levit_192,3727.29,136.213,512,224,10.95
ghostnet_050,3670.99,138.144,512,224,2.59
ese_vovnet19b_slim_dw,3629.92,140.575,512,224,1.9
lcnet_150,3576.28,142.665,512,224,4.5
gluon_resnet18_v1b,3482.17,146.691,512,224,11.69
resnet18,3481.78,146.713,512,224,11.69
swsl_resnet18,3480.5,146.765,512,224,11.69
ssl_resnet18,3477.04,146.904,512,224,11.69
resnet14t,3472.37,147.102,512,176,10.08
tf_efficientnetv2_b0,3428.08,148.143,512,192,7.14
tf_mobilenetv3_large_minimal_100,3366.45,151.356,512,224,3.92
mnasnet_075,3238.88,157.273,512,224,3.17
tf_mobilenetv3_large_075,3189.08,159.67,512,224,3.99
seresnet18,3138.91,162.608,512,224,11.78
mobilenetv3_large_075,3095.0,164.56,512,224,3.99
legacy_seresnet18,3076.04,165.928,512,224,11.78
hardcorenas_a,2971.63,171.576,512,224,5.26
levit_256,2956.43,172.043,512,224,18.89
mnasnet_b1,2930.02,173.933,512,224,4.38
mnasnet_100,2929.31,173.976,512,224,4.38
tf_mobilenetv3_large_100,2907.93,175.204,512,224,5.48
resnet18d,2875.3,177.69,512,224,11.71
tinynet_b,2851.82,178.435,512,188,3.73
hardcorenas_b,2772.42,183.73,512,224,5.18
hardcorenas_c,2763.94,184.272,512,224,5.52
mobilenetv3_rw,2754.46,184.981,512,224,5.48
nf_regnet_b0,2740.89,185.595,512,192,8.76
mobilenetv3_large_100_miil,2733.62,186.4,512,224,5.48
mobilenetv3_large_100,2732.43,186.472,512,224,5.48
ese_vovnet19b_slim,2684.58,190.344,512,224,3.17
spnasnet_100,2610.47,195.171,512,224,4.42
mobilenetv2_075,2609.91,195.379,512,224,2.64
semnasnet_075,2603.1,195.762,512,224,2.91
hardcorenas_d,2566.48,198.271,512,224,7.5
tf_efficientnetv2_b1,2548.95,199.349,512,192,8.14
levit_256d,2522.09,201.424,512,224,26.21
fbnetc_100,2397.58,212.548,512,224,5.57
tinynet_a,2334.41,218.035,512,192,6.19
mobilenetv2_100,2313.1,220.563,512,224,3.5
vit_tiny_patch16_224,2299.56,221.804,512,224,5.72
mnasnet_a1,2291.94,222.453,512,224,3.89
deit_tiny_patch16_224,2290.33,222.697,512,224,5.72
semnasnet_100,2279.15,223.737,512,224,3.89
edgenext_xx_small,2271.04,224.572,512,256,1.33
dla46_c,2266.89,225.115,512,224,1.3
hardcorenas_f,2252.64,226.141,512,224,8.2
deit_tiny_distilled_patch16_224,2248.67,226.799,512,224,5.91
hardcorenas_e,2245.94,226.861,512,224,8.07
xcit_nano_12_p16_224_dist,2177.52,233.052,512,224,3.05
xcit_nano_12_p16_224,2170.17,234.054,512,224,3.05
tf_efficientnet_lite0,2134.89,239.057,512,224,4.65
ghostnet_100,2129.82,239.0,512,224,5.18
hrnet_w18_small,2121.96,239.906,512,224,13.19
regnety_004,2085.76,244.311,512,224,4.34
efficientnet_lite0,2079.28,245.485,512,224,4.65
cs3darknet_focus_m,2062.98,247.547,512,256,9.3
pit_ti_distilled_224,2061.94,247.414,512,224,5.1
mnasnet_140,2060.59,247.645,512,224,7.12
pit_ti_224,2057.02,247.989,512,224,4.85
gluon_resnet34_v1b,2039.68,250.446,512,224,21.8
tv_resnet34,2038.39,250.573,512,224,21.8
resnet34,2036.51,250.813,512,224,21.8
ese_vovnet19b_dw,1999.58,255.562,512,224,6.54
resnet26,1962.0,260.488,512,224,16.0
tf_efficientnetv2_b2,1951.52,260.748,512,208,10.1
skresnet18,1943.81,262.753,512,224,11.96
cs3darknet_m,1940.79,263.122,512,256,9.31
regnetz_005,1916.17,265.765,512,224,7.12
resnetblur18,1897.99,269.406,512,224,11.69
rexnetr_100,1893.12,201.724,384,224,4.88
nf_resnet26,1869.64,273.344,512,224,16.0
mobilenetv2_110d,1868.27,204.505,384,224,4.52
visformer_tiny,1861.63,274.356,512,224,10.32
mixer_b32_224,1856.75,274.965,512,224,60.29
seresnet34,1837.21,277.783,512,224,21.96
fbnetv3_b,1825.5,278.744,512,224,8.6
mobilevitv2_050,1824.87,279.552,512,256,1.37
gernet_m,1822.12,280.293,512,224,21.14
resnet34d,1813.16,281.758,512,224,21.82
levit_384,1801.0,283.153,512,224,39.13
legacy_seresnet34,1781.32,286.529,512,224,21.96
regnetx_004,1780.61,286.47,512,224,5.16
tf_efficientnet_b0_ns,1779.24,214.77,384,224,5.29
tf_efficientnet_b0,1779.02,214.765,384,224,5.29
tf_efficientnet_b0_ap,1777.73,214.898,384,224,5.29
efficientnet_b0,1751.72,291.183,512,224,5.29
selecsls42,1718.76,297.231,512,224,30.35
selecsls42b,1710.18,298.726,512,224,32.46
vit_base_patch32_224,1708.63,298.818,512,224,88.22
vit_base_patch32_224_sam,1707.5,298.997,512,224,88.22
efficientnet_es_pruned,1687.32,302.688,512,224,5.44
resnetrs50,1686.45,302.42,512,160,35.69
efficientnet_es,1686.11,302.906,512,224,5.44
mixer_s16_224,1660.76,307.737,512,224,18.53
darknet17,1654.01,309.253,512,256,14.3
mobilenetv2_140,1637.57,233.691,384,224,6.11
fbnetv3_d,1634.54,233.136,384,224,10.31
tf_efficientnet_es,1623.9,314.542,512,224,5.44
resnet26d,1623.31,314.899,512,224,16.01
mobilevit_xxs,1602.81,238.427,384,256,1.27
resmlp_12_distilled_224,1577.54,323.769,512,224,15.35
resmlp_12_224,1577.31,323.803,512,224,15.35
pit_xs_224,1555.66,328.198,512,224,10.62
pit_xs_distilled_224,1555.48,328.255,512,224,11.0
semnasnet_140,1546.19,330.184,512,224,6.11
ghostnet_130,1542.47,330.535,512,224,7.36
repvgg_b0,1538.07,331.828,512,224,15.82
efficientnet_lite1,1530.99,166.26,256,240,5.42
dla34,1524.02,335.337,512,224,15.74
edgenext_x_small,1512.48,337.399,512,256,2.34
darknet21,1486.14,344.159,512,256,20.86
selecsls60,1482.76,344.397,512,224,30.67
selecsls60b,1478.62,345.378,512,224,32.77
nf_seresnet26,1473.71,346.754,512,224,17.4
vit_small_patch32_384,1455.89,350.818,512,384,22.92
gmixer_12_224,1448.32,352.721,512,224,12.7
efficientnet_b1_pruned,1446.82,352.35,512,240,6.33
tf_efficientnet_lite1,1443.47,176.394,256,240,5.42
nf_ecaresnet26,1440.41,354.896,512,224,16.0
xcit_tiny_12_p16_224_dist,1426.36,357.157,512,224,6.72
xcit_tiny_12_p16_224,1426.18,357.168,512,224,6.72
sedarknet21,1401.98,364.696,512,256,20.95
rexnetr_130,1388.84,183.199,256,224,7.61
dla46x_c,1388.59,367.953,512,224,1.07
gmlp_ti16_224,1381.11,276.449,384,224,5.87
mixnet_s,1365.54,373.667,512,224,4.13
rexnet_100,1364.31,280.319,384,224,4.8
regnety_006,1361.43,374.963,512,224,6.06
mobilenetv2_120d,1352.9,188.013,256,224,5.83
legacy_seresnext26_32x4d,1349.26,378.798,512,224,16.79
crossvit_tiny_240,1348.01,378.219,512,240,7.01
vit_tiny_r_s16_p8_384,1345.31,284.562,384,384,6.36
poolformer_s12,1342.54,380.659,512,224,11.92
dla60x_c,1341.77,380.621,512,224,1.32
efficientnet_b1,1325.85,191.544,256,224,7.79
resnetv2_50,1288.61,396.553,512,224,25.55
regnetx_006,1286.44,397.176,512,224,6.2
crossvit_9_240,1258.73,303.637,384,240,8.55
convnext_nano_ols,1252.33,408.151,512,224,15.6
convnext_nano,1249.89,408.864,512,224,15.59
convnext_nano_hnf,1249.05,409.138,512,224,15.59
resnet26t,1237.34,413.275,512,256,16.01
tf_mixnet_s,1236.15,412.905,512,224,4.13
nf_regnet_b2,1229.56,414.759,512,240,14.31
rexnetr_150,1224.57,207.878,256,224,9.78
gluon_resnet50_v1b,1219.23,419.12,512,224,25.56
tv_resnet50,1218.99,419.17,512,224,25.56
crossvit_9_dagger_240,1218.38,313.701,384,240,8.78
resnet50,1218.01,419.528,512,224,25.56
swsl_resnet50,1217.39,419.737,512,224,25.56
ssl_resnet50,1217.38,419.757,512,224,25.56
cs3darknet_focus_l,1216.61,314.788,384,256,21.15
repvgg_a2,1214.87,420.579,512,224,28.21
cs3darknet_l,1203.14,318.267,384,256,21.16
gernet_l,1201.09,425.379,512,256,31.08
efficientnet_lite2,1191.67,213.855,256,260,6.09
nf_regnet_b1,1181.15,431.966,512,256,10.22
seresnext26d_32x4d,1178.86,325.051,384,224,16.81
botnet26t_256,1178.34,325.281,384,256,12.49
seresnext26tn_32x4d,1177.85,325.355,384,224,16.81
seresnext26t_32x4d,1176.65,325.669,384,224,16.81
mobilevitv2_075,1174.29,217.001,256,256,2.87
ecaresnext50t_32x4d,1159.52,330.605,384,224,15.41
ecaresnext26t_32x4d,1158.26,330.961,384,224,15.41
gluon_resnet50_v1c,1147.86,333.697,384,224,25.58
halonet26t,1136.15,337.402,384,256,12.48
resnetv2_50d,1134.86,450.316,512,224,25.57
resnetv2_50t,1132.89,451.133,512,224,25.57
edgenext_small,1127.71,452.849,512,256,5.59
tf_efficientnet_lite2,1121.02,227.403,256,260,6.09
convit_tiny,1118.98,456.53,512,224,5.71
skresnet34,1113.08,458.799,512,224,22.28
tf_efficientnet_b1,1099.77,231.299,256,240,7.79
tf_efficientnet_b1_ap,1099.37,231.402,256,240,7.79
efficientnetv2_rw_t,1098.86,230.78,256,224,13.65
tf_efficientnet_b1_ns,1098.29,231.567,256,240,7.79
ecaresnetlight,1091.16,468.275,512,224,30.16
gluon_resnet50_v1d,1084.38,353.226,384,224,25.58
dpn68b,1083.77,353.123,384,224,12.61
cs3sedarknet_l,1083.42,353.12,384,256,21.91
resnet50d,1078.0,355.348,384,224,25.58
resnet50t,1076.81,355.721,384,224,25.57
resnet32ts,1075.86,237.337,256,256,17.96
resnet33ts,1061.36,240.599,256,256,19.68
vit_small_patch16_224,1057.92,362.157,384,224,22.05
resnetaa50,1057.73,362.204,384,224,25.56
vit_small_resnet26d_224,1057.57,362.04,384,224,63.61
deit_small_patch16_224,1050.7,364.638,384,224,22.05
cspresnet50,1042.19,367.617,384,256,21.62
tf_efficientnetv2_b3,1041.71,243.94,256,240,14.36
regnetx_008,1034.73,493.971,512,224,7.26
ecaresnet26t,1033.34,371.048,384,256,16.01
deit_small_distilled_patch16_224,1028.8,372.398,384,224,22.44
vit_relpos_base_patch32_plus_rpn_256,1021.86,499.989,512,256,119.42
dla60,1020.05,375.488,384,224,22.04
res2net50_48w_2s,1018.83,376.079,384,224,25.29
gc_efficientnetv2_rw_t,1014.65,249.524,256,224,13.68
vit_relpos_small_patch16_rpn_224,1013.69,377.786,384,224,21.97
edgenext_small_rw,1011.18,505.339,512,256,7.83
pit_s_224,1010.83,378.943,384,224,23.46
seresnet33ts,1007.26,253.362,256,256,19.78
efficientnet_em,1007.19,253.179,256,240,6.9
vovnet39a,1006.62,507.995,512,224,22.6
legacy_seresnet50,1003.5,381.52,384,224,28.09
gluon_resnext50_32x4d,1001.3,382.689,384,224,25.03
tv_resnext50_32x4d,1001.18,382.711,384,224,25.03
resnext50_32x4d,1001.03,382.776,384,224,25.03
ssl_resnext50_32x4d,1000.68,382.908,384,224,25.03
eca_resnet33ts,999.77,255.368,256,256,19.68
swsl_resnext50_32x4d,997.37,384.186,384,224,25.03
regnety_008,993.3,514.408,512,224,6.26
dpn68,992.27,385.859,384,224,12.61
deit3_small_patch16_224,987.86,387.777,384,224,22.06
deit3_small_patch16_224_in21ft1k,987.15,388.058,384,224,22.06
gcresnet33ts,985.12,258.855,256,256,19.88
efficientnet_b2a,980.29,259.63,256,256,9.11
tf_efficientnet_em,980.0,260.253,256,240,6.9
efficientnet_b2,978.68,260.092,256,256,9.11
seresnet50,971.79,394.011,384,224,28.09
gluon_resnet50_v1s,970.71,394.714,384,224,25.68
vit_srelpos_small_patch16_224,969.18,395.281,384,224,21.97
vit_relpos_small_patch16_224,965.13,396.742,384,224,21.98
ecaresnet50d_pruned,964.18,530.07,512,224,19.94
cspresnet50d,956.82,266.672,256,256,21.64
vgg11,954.03,536.508,512,224,132.86
cspresnet50w,952.27,267.927,256,256,28.12
ese_vovnet39b,951.93,537.173,512,224,24.57
vit_base_patch32_plus_256,951.5,537.138,512,256,119.48
resnetaa50d,950.79,403.026,384,224,25.58
eca_vovnet39b,948.4,539.184,512,224,22.6
lambda_resnet26rpt_256,942.15,203.17,192,256,10.99
pit_s_distilled_224,934.29,273.079,256,224,24.04
mobilevit_xs,924.5,275.792,256,256,2.32
tv_densenet121,917.93,277.067,256,224,7.98
densenet121,913.65,278.353,256,224,7.98
resnetblur50,911.91,420.254,384,224,25.56
hrnet_w18_small_v2,910.26,559.998,512,224,15.6
coat_lite_tiny,909.29,421.406,384,224,5.72
mobilevitv2_100,907.45,281.094,256,256,4.9
nf_resnet50,900.11,425.722,384,256,25.56
resnext50d_32x4d,894.57,285.293,256,224,25.05
nf_seresnet50,892.73,428.967,384,224,28.09
rexnetr_200,890.57,214.407,192,224,16.52
efficientnet_cc_b0_4e,890.34,430.073,384,224,13.31
efficientnet_cc_b0_8e,889.37,430.553,384,224,24.01
dla60x,886.5,287.775,256,224,17.35
twins_svt_small,885.48,432.048,384,224,24.06
seresnet50t,879.71,435.29,384,224,28.1
mixnet_m,878.04,581.529,512,224,5.01
nf_ecaresnet50,875.38,437.674,384,224,25.56
efficientnet_b2_pruned,873.9,291.355,256,260,8.31
densenet121d,873.44,291.238,256,224,8.0
cspresnext50,868.23,294.006,256,256,20.57
rexnet_150,866.26,294.391,256,224,9.73
ecaresnet50d,862.65,444.205,384,224,25.58
fbnetv3_g,862.32,220.642,192,240,16.62
regnetz_b16,862.05,295.457,256,224,9.72
tf_efficientnet_cc_b0_4e,861.1,444.691,384,224,13.31
tf_efficientnet_cc_b0_8e,857.16,446.822,384,224,24.01
gcresnet50t,854.99,447.633,384,256,25.9
res2net50_26w_4s,851.03,449.921,384,224,25.7
coat_lite_mini,849.82,450.985,384,224,11.01
tf_efficientnet_b2_ap,849.52,224.466,192,260,9.11
tf_efficientnet_b2,848.58,224.736,192,260,9.11
tf_efficientnet_b2_ns,847.86,224.983,192,260,9.11
vit_base_resnet26d_224,844.62,453.315,384,224,101.4
vgg11_bn,832.74,460.889,384,224,132.87
vovnet57a,832.06,614.449,512,224,36.64
selecsls84,830.17,615.492,512,224,50.95
resnetblur50d,826.31,308.964,256,224,25.58
convnext_tiny_hnfd,820.9,310.941,256,224,28.59
convnext_tiny_hnf,819.46,311.471,256,224,28.59
convnext_tiny,819.24,311.536,256,224,28.59
convnext_tiny_in22ft1k,818.81,311.724,256,224,28.59
rexnet_130,816.78,312.226,256,224,7.56
seresnext50_32x4d,814.69,313.102,256,224,27.56
legacy_seresnext50_32x4d,813.61,313.477,256,224,27.56
gluon_seresnext50_32x4d,813.13,313.678,256,224,27.56
skresnet50,808.8,473.357,384,224,25.8
visformer_small,806.27,475.588,384,224,40.22
res2net50_14w_8s,794.56,319.93,256,224,25.06
densenetblur121d,789.33,322.521,256,224,8.0
seresnetaa50d,785.32,324.779,256,224,28.11
gluon_inception_v3,782.59,489.263,384,299,23.83
inception_v3,782.35,489.427,384,299,23.83
adv_inception_v3,778.18,491.976,384,299,23.83
resmlp_24_distilled_224,777.24,327.895,256,224,30.02
resmlp_24_224,776.95,327.972,256,224,30.02
tf_inception_v3,775.41,493.776,384,299,23.83
ese_vovnet57b,774.18,495.058,384,224,38.61
tf_mixnet_m,773.08,495.127,384,224,5.01
resnetv2_101,772.45,329.834,256,224,44.54
dla60_res2net,767.35,332.099,256,224,20.85
nf_regnet_b3,766.23,499.321,384,288,18.59
sehalonet33ts,763.66,334.4,256,256,13.69
ecaresnet101d_pruned,754.9,676.449,512,224,24.88
darknet53,753.16,339.081,256,256,41.61
densenet169,752.52,337.551,256,224,14.15
resnet101,747.89,340.74,256,224,44.55
gluon_resnet101_v1b,747.04,341.055,256,224,44.55
tv_resnet101,746.84,341.219,256,224,44.55
skresnet50d,739.17,344.891,256,224,25.82
twins_pcpvt_small,738.11,345.194,256,224,24.11
vit_small_r26_s32_224,733.9,347.477,256,224,36.43
mobilevit_s,733.0,260.821,192,256,5.58
darknetaa53,732.7,348.577,256,256,36.02
xcit_tiny_24_p16_224_dist,727.98,348.335,256,224,12.12
xcit_tiny_24_p16_224,727.1,348.63,256,224,12.12
efficientnet_b0_gn,724.56,352.174,256,224,5.29
efficientnet_b3_pruned,722.23,352.701,256,300,9.86
gluon_resnet101_v1c,717.66,355.143,256,224,44.57
resnext26ts,717.15,534.946,384,256,10.3
resnetv2_101d,715.45,356.238,256,224,44.56
gmixer_24_224,714.67,356.582,256,224,24.72
resnetrs101,714.37,356.071,256,192,63.62
nf_resnet101,712.1,537.603,384,224,44.55
efficientnet_lite3,702.44,181.104,128,300,8.2
mixnet_l,702.18,545.327,384,224,7.33
eca_resnext26ts,694.05,368.289,256,256,10.3
semobilevit_s,692.92,368.16,256,256,5.74
seresnext26ts,691.18,369.699,256,256,10.39
poolformer_s24,689.84,369.792,256,224,21.39
gluon_resnet101_v1d,688.26,370.323,256,224,44.57
dla102,688.03,370.524,256,224,33.27
vit_relpos_medium_patch16_rpn_224,687.13,371.514,256,224,38.73
sebotnet33ts_256,686.07,279.058,192,256,13.7
gcresnext26ts,683.09,373.929,256,256,10.48
regnetx_016,682.73,749.012,512,224,9.19
haloregnetz_b,680.78,374.495,256,224,11.68
cspdarknet53,679.01,375.961,256,256,27.64
vgg13,677.45,566.653,384,224,133.05
xcit_nano_12_p16_384_dist,671.72,379.231,256,384,3.05
wide_resnet50_2,668.78,573.358,384,224,68.88
tf_efficientnet_lite3,665.78,191.165,128,300,8.2
vit_relpos_medium_patch16_cls_224,661.77,385.665,256,224,38.76
vit_srelpos_medium_patch16_224,659.88,386.996,256,224,38.74
rexnet_200,659.06,290.146,192,224,16.37
vit_base_resnet50d_224,658.84,386.945,256,224,110.97
ecaresnet50t,657.78,388.237,256,256,25.57
gmlp_s16_224,657.63,290.408,192,224,19.42
vit_relpos_medium_patch16_224,657.05,388.484,256,224,38.75
tf_efficientnet_cc_b1_8e,654.82,389.25,256,240,39.72
regnety_016,650.07,785.757,512,224,11.2
swin_tiny_patch4_window7_224,648.69,393.641,256,224,28.29
xcit_small_12_p16_224,640.82,397.688,256,224,26.25
gluon_resnet101_v1s,640.79,397.908,256,224,44.67
xcit_small_12_p16_224_dist,639.99,398.193,256,224,26.25
crossvit_small_240,638.8,399.076,256,240,26.86
efficientnet_cc_b1_8e,637.42,399.94,256,240,39.72
resnetaa101d,634.86,401.619,256,224,44.57
cs3sedarknet_xdw,630.82,302.41,192,256,21.6
repvgg_b1,623.55,820.034,512,224,57.42
mobilevitv2_125,620.51,308.406,192,256,7.48
bat_resnext26ts,613.61,415.954,256,256,10.73
gluon_resnext101_32x4d,609.67,418.333,256,224,44.18
swsl_resnext101_32x4d,609.02,418.731,256,224,44.18
resnext101_32x4d,609.01,418.74,256,224,44.18
tf_mixnet_l,606.88,420.297,256,224,7.33
ssl_resnext101_32x4d,606.28,420.718,256,224,44.18
legacy_seresnet101,601.55,423.316,256,224,49.33
cs3darknet_focus_x,600.02,425.715,256,256,35.02
dla102x,598.42,319.231,192,224,26.31
halonet50ts,597.8,320.205,192,256,22.73
xcit_nano_12_p8_224,595.07,428.358,256,224,3.05
xcit_nano_12_p8_224_dist,593.27,429.695,256,224,3.05
cait_xxs24_224,593.22,428.92,256,224,11.96
seresnet101,590.42,431.41,256,224,49.33
swin_s3_tiny_224,588.57,433.98,256,224,28.33
resnetv2_50x1_bit_distilled,585.83,326.889,192,224,25.55
efficientnet_b0_g8_gn,582.67,438.264,256,224,6.56
crossvit_15_240,580.46,328.975,192,240,27.53
resnetblur101d,576.87,442.155,256,224,44.57
res2net50_26w_6s,573.8,444.339,256,224,37.05
vgg13_bn,573.47,446.125,256,224,133.05
efficientnet_b3a,572.29,221.925,128,288,12.23
efficientnet_b3,572.18,221.941,128,288,12.23
cs3darknet_x,571.12,447.259,256,256,35.05
densenet201,562.52,338.221,192,224,20.01
crossvit_15_dagger_240,562.49,339.489,192,240,28.21
efficientnetv2_s,559.15,226.702,128,288,21.46
eca_botnext26ts_256,558.6,457.666,256,256,10.59
mixer_b16_224,556.6,459.152,256,224,59.88
mixer_b16_224_miil,556.5,459.202,256,224,59.88
eca_halonext26ts,547.96,466.574,256,256,10.76
ecaresnet101d,546.58,466.555,256,224,44.57
vgg16,546.06,702.994,384,224,138.36
mixer_l32_224,543.38,351.819,192,224,206.94
vit_base_patch32_384,543.37,470.294,256,384,88.3
nf_seresnet101,540.53,471.014,256,224,49.33
resnetv2_152,536.63,474.697,256,224,60.19
botnet50ts_256,534.71,238.412,128,256,22.74
mobilevitv2_150,533.38,238.97,128,256,10.59
vit_base_r26_s32_224,533.28,358.697,192,224,101.38
mobilevitv2_150_in22ft1k,532.99,239.183,128,256,10.59
cs3sedarknet_x,531.85,479.872,256,256,35.4
nf_ecaresnet101,531.3,479.947,256,224,44.55
cs3edgenet_x,529.37,482.632,256,256,47.82
res2next50,528.59,483.023,256,224,24.67
res2net101_26w_4s,527.01,483.179,256,224,45.21
vit_large_patch32_224,524.74,486.172,256,224,306.54
resnet101d,523.85,364.964,192,256,44.57
efficientnetv2_rw_s,520.77,243.564,128,288,23.94
halo2botnet50ts_256,517.51,369.975,192,256,22.64
resmlp_36_distilled_224,513.23,371.84,192,224,44.69
vit_tiny_patch16_384,510.99,249.657,128,384,5.79
resmlp_36_224,509.53,374.55,192,224,44.69
swinv2_cr_tiny_224,506.82,503.861,256,224,28.33
mixnet_xl,505.67,504.387,256,224,11.9
resnetv2_50d_gn,505.25,379.149,192,224,25.57
swinv2_cr_tiny_ns_224,504.1,506.527,256,224,28.33
gluon_resnet152_v1b,502.02,380.204,192,224,60.19
regnetz_d8,501.8,253.463,128,256,23.37
resnet152,501.44,380.547,192,224,60.19
tv_resnet152,501.12,380.811,192,224,60.19
xception,497.64,256.367,128,299,22.86
regnety_032,496.85,771.405,384,224,19.44
tf_efficientnet_b3_ap,496.0,256.348,128,300,12.23
tf_efficientnet_b3,494.58,257.101,128,300,12.23
tf_efficientnet_b3_ns,492.45,258.213,128,300,12.23
convnext_small_in22ft1k,490.79,389.411,192,224,50.22
res2net50_26w_8s,489.22,520.921,256,224,48.4
tf_efficientnetv2_s_in21ft1k,488.77,259.622,128,300,21.46
tf_efficientnetv2_s,488.25,259.918,128,300,21.46
gluon_resnet152_v1c,488.2,390.894,192,224,60.21
convnext_small,487.15,392.394,192,224,50.22
twins_pcpvt_base,487.13,391.314,192,224,43.83
resnetv2_152d,486.82,392.059,192,224,60.2
legacy_seresnext101_32x4d,484.7,393.829,192,224,48.96
resnet50_gn,482.45,397.141,192,224,25.56
gluon_seresnext101_32x4d,480.63,397.197,192,224,48.96
hrnet_w32,480.24,528.215,256,224,41.23
sequencer2d_s,480.16,264.213,128,224,27.65
seresnext101_32x4d,479.03,398.526,192,224,48.96
nest_tiny,477.97,266.889,128,224,17.06
dla60_res2next,477.74,534.408,256,224,17.03
gluon_resnet152_v1d,476.32,400.788,192,224,60.21
regnetz_c16,475.79,402.061,192,256,13.46
hrnet_w18,473.42,535.867,256,224,21.3
jx_nest_tiny,472.84,269.807,128,224,17.06
regnetz_d32,471.85,269.596,128,256,27.58
regnetz_040,471.81,269.412,128,256,27.12
xception41p,471.79,270.424,128,299,26.91
vgg16_bn,470.3,543.999,256,224,138.37
regnetz_040h,469.27,270.846,128,256,28.94
poolformer_s36,467.39,408.832,192,224,30.86
resnet51q,463.81,551.102,256,256,35.7
efficientnet_el_pruned,461.98,275.957,128,300,10.59
efficientnet_el,461.97,275.94,128,300,10.59
coat_lite_small,461.36,414.606,192,224,19.84
nf_regnet_b4,457.97,417.049,192,320,30.21
vgg19,457.37,839.347,384,224,143.67
cs3se_edgenet_x,457.05,418.615,192,256,50.72
dla169,455.51,419.044,192,224,53.39
convit_small,454.72,421.197,192,224,27.78
gluon_resnet152_v1s,452.53,421.917,192,224,60.32
tf_efficientnet_el,449.8,283.446,128,300,10.59
gcresnext50ts,445.12,429.834,192,256,15.67
regnetx_040,442.35,866.937,384,224,22.12
vit_small_resnet50d_s16_224,437.26,437.826,192,224,57.53
volo_d1_224,437.04,437.842,192,224,26.63
mobilevitv2_175_in22ft1k,434.9,293.339,128,256,14.25
mobilevitv2_175,434.88,293.341,128,256,14.25
resnet61q,433.95,441.405,192,256,36.85
ese_vovnet99b,433.24,589.371,256,224,63.2
ese_vovnet39b_evos,430.54,296.328,128,224,24.58
twins_svt_base,425.02,449.64,192,224,56.07
resnest14d,411.87,1242.634,512,224,10.61
dla102x2,405.87,313.766,128,224,41.28
mobilevitv2_200_in22ft1k,405.83,314.414,128,256,18.45
mobilevitv2_200,405.76,314.447,128,256,18.45
inception_v4,405.43,471.399,192,299,42.68
crossvit_18_240,404.58,314.332,128,240,43.27
swin_small_patch4_window7_224,400.37,477.632,192,224,49.61
densenet161,399.17,318.189,128,224,28.68
vgg19_bn,398.5,642.012,256,224,143.68
legacy_seresnet152,398.4,478.588,192,224,66.82
vit_base_patch16_224_miil,397.86,481.79,192,224,86.54
sequencer2d_m,396.83,480.626,192,224,38.31
crossvit_18_dagger_240,396.31,320.926,128,240,44.27
resnetv2_50d_frn,394.18,323.553,128,224,25.59
vit_base_patch16_224,392.99,487.729,192,224,86.57
vit_base_patch16_224_sam,392.92,487.774,192,224,86.57
vit_base_patch16_rpn_224,391.32,489.846,192,224,86.54
xception41,391.05,326.045,128,299,26.97
deit_base_patch16_224,390.04,491.437,192,224,86.57
cait_xxs36_224,387.24,492.066,192,224,17.3
efficientnet_b0_g16_evos,386.23,993.13,384,224,8.11
deit_base_distilled_patch16_224,384.06,499.086,192,224,87.34
xcit_tiny_12_p16_384_dist,383.09,499.371,192,384,6.72
vit_relpos_base_patch16_rpn_224,382.95,500.328,192,224,86.41
seresnet152,379.38,334.127,128,224,66.82
resnetv2_50d_evos,374.27,340.812,128,224,25.59
deit3_base_patch16_224,374.05,512.341,192,224,86.59
deit3_base_patch16_224_in21ft1k,373.84,512.639,192,224,86.59
vit_relpos_base_patch16_clsgap_224,370.76,516.704,192,224,86.43
vit_relpos_base_patch16_cls_224,370.22,517.437,192,224,86.43
hrnet_w30,369.35,688.202,256,224,37.71
vit_relpos_base_patch16_224,368.93,519.279,192,224,86.43
gluon_resnext101_64x4d,363.93,350.084,128,224,83.46
resnext101_64x4d,363.79,350.21,128,224,83.46
beit_base_patch16_224,358.77,534.02,192,224,86.53
ens_adv_inception_resnet_v2,358.6,532.08,192,299,55.84
wide_resnet101_2,358.56,533.868,192,224,126.89
inception_resnet_v2,358.54,532.143,192,299,55.84
resnet200,357.55,355.04,128,224,64.67
resnet152d,357.54,355.729,128,256,60.21
swinv2_tiny_window8_256,357.0,536.56,192,256,28.35
efficientnet_b4,354.99,268.381,96,320,19.34
dpn92,353.0,723.721,256,224,37.67
repvgg_b2,352.05,1453.222,512,224,89.02
resnest50d_1s4x24d,349.97,730.142,256,224,25.68
regnetz_b16_evos,347.19,366.83,128,224,9.74
tnt_s_patch16_224,342.71,558.25,192,224,23.76
xception65p,341.43,373.588,128,299,39.82
convnext_base_in22ft1k,339.67,374.996,128,224,88.59
convnext_base,338.68,376.119,128,224,88.59
efficientnet_lite4,338.39,187.783,64,380,13.01
twins_pcpvt_large,333.52,379.633,128,224,60.99
pit_b_224,331.08,385.636,128,224,73.76
pit_b_distilled_224,328.87,388.208,128,224,74.79
xcit_small_24_p16_224_dist,326.41,388.817,128,224,47.67
xcit_small_24_p16_224,326.38,388.806,128,224,47.67
tf_efficientnet_lite4,324.6,195.748,64,380,13.01
eca_nfnet_l0,319.84,1599.745,512,224,24.14
nfnet_l0,319.69,1600.317,512,224,35.07
gluon_seresnext101_64x4d,319.51,398.398,128,224,88.23
repvgg_b3,316.57,1211.922,384,224,123.09
skresnext50_32x4d,315.84,809.121,256,224,27.48
poolformer_m36,315.69,403.496,128,224,56.17
ssl_resnext101_32x8d,313.35,406.924,128,224,88.79
resnext101_32x8d,312.8,407.622,128,224,88.79
swsl_resnext101_32x8d,312.76,407.724,128,224,88.79
ig_resnext101_32x8d,311.13,409.865,128,224,88.79
vit_small_patch16_36x1_224,309.04,411.365,128,224,64.67
regnetx_032,308.88,1241.936,384,224,15.3
vit_small_patch16_18x2_224,306.57,414.654,128,224,64.67
xcit_tiny_12_p8_224,306.37,415.93,128,224,6.71
cait_s24_224,305.74,415.965,128,224,46.92
xcit_tiny_12_p8_224_dist,304.23,418.886,128,224,6.71
swinv2_cr_small_ns_224,300.99,422.86,128,224,49.7
twins_svt_large,300.69,423.548,128,224,99.27
swinv2_cr_small_224,299.81,424.482,128,224,49.7
coat_tiny,298.83,426.275,128,224,5.5
resnest26d,298.23,1286.829,384,224,17.07
nest_small,296.79,321.765,96,224,38.35
jx_nest_small,293.75,325.094,96,224,38.35
swin_s3_small_224,290.56,438.612,128,224,49.74
dpn98,290.11,439.591,128,224,61.57
resnetv2_50d_evob,289.65,330.197,96,224,25.59
seresnet152d,283.9,447.414,128,256,66.84
gluon_xception65,283.18,337.068,96,299,39.92
convnext_tiny_384_in22ft1k,282.39,338.982,96,384,28.59
resnetrs152,282.26,450.046,128,256,86.62
xception65,281.11,339.548,96,299,39.92
swin_base_patch4_window7_224,281.0,453.662,128,224,87.77
hrnet_w48,279.44,682.135,192,224,77.47
mixnet_xxl,278.4,457.833,128,224,23.96
seresnext101_32x8d,278.13,458.033,128,224,93.57
gmlp_b16_224,275.97,346.253,96,224,73.08
seresnext101d_32x8d,270.35,471.144,128,224,93.59
resnet200d,267.1,476.272,128,256,64.69
nfnet_f0,265.61,1926.394,512,192,71.49
regnetz_e8,256.51,247.489,64,256,57.7
xcit_tiny_24_p16_384_dist,255.73,371.975,96,384,12.12
crossvit_base_240,254.6,375.374,96,240,105.03
dm_nfnet_f0,251.38,1526.301,384,192,71.49
hrnet_w40,249.23,765.525,192,224,57.56
vit_base_patch16_plus_240,246.96,517.368,128,240,117.56
efficientnetv2_m,246.55,256.379,64,320,54.14
vit_relpos_base_patch16_plus_240,244.88,521.493,128,240,117.38
seresnextaa101d_32x8d,243.89,522.629,128,224,93.59
tf_efficientnet_b4_ap,242.14,262.218,64,380,19.34
tf_efficientnet_b4,241.83,262.52,64,380,19.34
tf_efficientnet_b4_ns,241.46,263.01,64,380,19.34
xcit_medium_24_p16_224,241.39,526.926,128,224,84.4
xcit_medium_24_p16_224_dist,241.08,527.466,128,224,84.4
xcit_small_12_p16_384_dist,240.61,397.192,96,384,26.25
vit_small_patch16_384,239.06,266.856,64,384,22.2
volo_d2_224,238.89,400.019,96,224,58.68
swinv2_tiny_window16_256,238.79,400.76,96,256,28.35
mobilevitv2_150_384_in22ft1k,238.1,267.77,64,384,10.59
vit_large_r50_s32_224,236.27,403.797,96,224,328.99
tresnet_m,233.24,2192.365,512,224,31.39
hrnet_w44,232.48,820.975,192,224,67.06
poolformer_m48,232.43,410.471,96,224,73.47
densenet264,231.27,411.12,96,224,72.69
convit_base,231.06,552.947,128,224,86.54
nf_regnet_b5,228.54,417.354,96,384,49.74
deit3_small_patch16_384,226.74,281.318,64,384,22.21
deit3_small_patch16_384_in21ft1k,226.44,281.652,64,384,22.21
vit_small_r26_s32_384,226.15,281.726,64,384,36.47
coat_mini,225.14,566.497,128,224,10.34
efficientnetv2_rw_m,224.46,281.565,64,320,53.24
swin_s3_base_224,224.0,425.728,96,224,71.13
tnt_b_patch16_224,223.52,570.669,128,224,65.41
hrnet_w64,223.29,568.417,128,224,128.06
sequencer2d_l,220.03,286.022,64,224,54.3
dpn131,216.53,588.962,128,224,79.25
vit_base_r50_s16_224,215.49,443.851,96,224,98.66
swinv2_cr_base_ns_224,214.73,444.647,96,224,87.88
xception71,214.09,296.77,64,299,42.34
swinv2_cr_base_224,213.21,447.81,96,224,87.88
swinv2_small_window8_256,213.06,448.048,96,256,49.73
nest_base,210.25,302.717,64,224,67.72
jx_nest_base,209.06,304.441,64,224,67.72
seresnet200d,203.53,467.209,96,256,71.86
resnetrs200,201.84,471.293,96,256,93.21
resnest50d,201.6,1268.493,256,224,27.48
ecaresnet200d,201.55,472.938,96,256,64.69
xcit_nano_12_p8_384_dist,201.45,315.854,64,384,3.05
efficientnet_b3_gn,197.65,322.123,64,288,11.73
xcit_tiny_24_p8_224_dist,195.37,488.075,96,224,12.11
xcit_tiny_24_p8_224,195.11,488.622,96,224,12.11
dpn107,194.08,492.913,96,224,86.92
regnetz_c16_evos,193.89,328.188,64,256,13.49
regnety_040,190.14,2017.916,384,224,20.65
mobilevitv2_175_384_in22ft1k,189.6,336.534,64,384,14.25
regnetv_040,188.29,1358.084,256,224,20.64
convnext_large,187.93,509.087,96,224,197.77
convnext_large_in22ft1k,187.83,509.365,96,224,197.77
convmixer_768_32,187.17,511.603,96,224,21.11
regnetx_080,181.41,1409.979,256,224,39.57
resnest50d_4s2x40d,180.44,1417.38,256,224,30.42
xcit_small_12_p8_224,179.5,354.768,64,224,26.21
xcit_small_12_p8_224_dist,179.34,355.047,64,224,26.21
halonet_h1,176.7,360.706,64,256,8.1
tf_efficientnetv2_m_in21ft1k,175.14,270.794,48,384,54.14
mobilevitv2_200_384_in22ft1k,175.13,273.08,48,384,18.45
tf_efficientnetv2_m,173.37,273.617,48,384,54.14
mixer_l16_224,171.41,558.471,96,224,208.2
efficientnet_b3_g8_gn,168.79,377.376,64,288,14.25
repvgg_b1g4,167.59,3053.943,512,224,39.97
vit_large_patch32_384,167.04,573.058,96,384,306.63
convnext_small_384_in22ft1k,165.65,384.557,64,384,50.22
volo_d3_224,162.19,392.021,64,224,86.33
regnetz_d8_evos,155.31,307.002,48,256,23.46
swin_large_patch4_window7_224,153.79,414.289,64,224,196.53
swinv2_base_window8_256,151.21,420.663,64,256,87.92
convmixer_1024_20_ks9_p14,149.3,1713.726,256,224,24.38
resnetv2_50x1_bitm,147.75,215.764,32,448,25.55
seresnet269d,145.59,433.61,64,256,113.67
resnetrs270,144.14,437.83,64,256,129.86
swinv2_small_window16_256,143.52,443.487,64,256,49.73
regnety_040s_gn,142.58,896.132,128,224,20.65
repvgg_b2g4,133.72,3827.892,512,224,61.76
eca_nfnet_l1,133.59,1435.413,192,256,41.41
xcit_large_24_p16_224,132.6,479.222,64,224,189.1
swinv2_cr_tiny_384,131.94,483.86,64,384,28.33
xcit_large_24_p16_224_dist,131.66,482.65,64,224,189.1
xcit_tiny_12_p8_384_dist,131.64,362.75,48,384,6.71
regnetx_064,129.82,1970.916,256,224,26.21
swinv2_cr_large_224,124.15,513.018,64,224,196.68
xcit_small_24_p16_384_dist,120.64,394.328,48,384,47.67
regnety_064,119.44,2141.523,256,224,30.58
regnety_080,117.88,2170.37,256,224,39.18
crossvit_15_dagger_408,117.86,269.618,32,408,28.5
vit_large_patch16_224,117.2,544.512,64,224,304.33
regnetv_064,117.03,1638.944,192,224,30.58
ese_vovnet99b_iabn,117.02,3278.167,384,224,63.2
convnext_xlarge_in22ft1k,116.37,548.167,64,224,350.2
vit_base_patch16_18x2_224,116.0,548.972,64,224,256.73
convnext_base_384_in22ft1k,115.58,413.454,48,384,88.59
efficientnet_b5,113.63,279.129,32,456,30.39
deit3_large_patch16_224_in21ft1k,112.51,567.041,64,224,304.37
deit3_large_patch16_224,112.48,567.139,64,224,304.37
tf_efficientnet_b5,111.42,284.665,32,456,30.39
tf_efficientnet_b5_ap,111.14,285.451,32,456,30.39
tf_efficientnet_b5_ns,111.14,285.33,32,456,30.39
legacy_senet154,110.98,861.567,96,224,115.09
senet154,110.82,862.828,96,224,115.09
gluon_senet154,110.77,863.12,96,224,115.09
beit_large_patch16_224,109.02,584.818,64,224,304.43
repvgg_b3g4,108.77,3529.239,384,224,83.83
regnetx_160,107.6,1783.261,192,224,54.28
nfnet_f1,107.01,1791.907,192,224,132.63
volo_d1_384,105.69,301.347,32,384,26.78
swinv2_base_window16_256,103.88,459.56,48,256,87.92
swinv2_base_window12to16_192to256_22kft1k,103.79,460.002,48,256,87.92
tresnet_l,102.82,4975.916,512,224,55.99
dm_nfnet_f1,101.59,1257.525,128,224,132.63
volo_d4_224,101.08,472.359,48,224,192.96
cait_xxs24_384,99.39,480.268,48,384,12.03
ecaresnet269d,99.06,479.988,48,320,102.09
efficientnetv2_l,98.76,319.521,32,384,118.52
tf_efficientnetv2_l_in21ft1k,98.35,320.759,32,384,118.52
tf_efficientnetv2_l,97.56,323.47,32,384,118.52
deit_base_patch16_384,97.3,328.042,32,384,86.86
vit_base_patch16_384,97.1,328.712,32,384,86.86
resnest101e,96.09,1329.413,128,256,48.28
deit_base_distilled_patch16_384,94.63,337.315,32,384,87.63
regnetx_120,94.03,2721.558,256,224,46.11
deit3_base_patch16_384,93.5,341.294,32,384,86.88
deit3_base_patch16_384_in21ft1k,93.49,341.309,32,384,86.88
xcit_small_24_p8_224_dist,92.61,514.968,48,224,47.63
xcit_small_24_p8_224,92.51,515.466,48,224,47.63
regnety_120,92.07,2083.952,192,224,51.82
tresnet_xl,91.15,4209.119,384,224,78.44
crossvit_18_dagger_408,89.17,356.787,32,408,44.61
resnetv2_152x2_bit_teacher,89.16,356.538,32,224,236.34
vit_large_patch14_224,85.06,562.673,48,224,304.2
resnetv2_101x1_bitm,84.72,187.286,16,448,44.54
resnetrs350,84.14,372.211,32,288,163.96
beit_base_patch16_384,83.87,380.424,32,384,86.74
regnety_160,83.24,2305.144,192,224,83.59
pnasnet5large,83.16,380.801,32,331,86.06
xcit_medium_24_p16_384_dist,82.74,383.266,32,384,84.4
vit_large_r50_s32_384,77.34,411.186,32,384,329.09
nasnetalarge,77.32,408.633,32,331,88.75
swinv2_cr_small_384,76.42,416.277,32,384,49.7
swin_base_patch4_window12_384,74.73,426.327,32,384,87.9
resmlp_big_24_distilled_224,70.88,449.95,32,224,129.14
resmlp_big_24_224_in22ft1k,70.88,449.933,32,224,129.14
resmlp_big_24_224,70.41,452.99,32,224,129.14
regnety_320,66.33,1928.357,128,224,145.05
xcit_tiny_24_p8_384_dist,66.29,479.361,32,384,12.11
cait_xs24_384,66.24,480.525,32,384,26.67
ig_resnext101_32x16d,65.9,1455.165,96,224,194.03
ssl_resnext101_32x16d,65.74,1458.688,96,224,194.03
swsl_resnext101_32x16d,65.74,1458.738,96,224,194.03
volo_d5_224,64.41,493.535,32,224,295.46
cait_xxs36_384,64.34,493.602,32,384,17.37
efficientnet_b6,64.08,246.77,16,528,43.04
xcit_medium_24_p8_224,63.96,496.86,32,224,84.32
xcit_medium_24_p8_224_dist,63.93,497.194,32,224,84.32
convnext_large_384_in22ft1k,63.85,499.388,32,384,197.77
vit_base_patch8_224,63.45,377.425,24,224,86.58
tf_efficientnet_b6_ns,63.1,250.577,16,528,43.04
tf_efficientnet_b6,62.84,251.669,16,528,43.04
tf_efficientnet_b6_ap,62.76,252.073,16,528,43.04
efficientnetv2_xl,62.18,251.438,16,384,208.12
tf_efficientnetv2_xl_in21ft1k,62.14,251.721,16,384,208.12
xcit_small_12_p8_384_dist,61.84,386.224,24,384,26.21
vit_base_r50_s16_384,61.01,391.67,24,384,98.95
vit_base_resnet50_384,60.98,391.903,24,384,98.95
swinv2_large_window12to16_192to256_22kft1k,60.98,391.098,24,256,196.74
eca_nfnet_l2,58.72,1632.112,96,320,56.72
volo_d2_384,58.5,271.766,16,384,58.87
resnetrs420,56.49,415.629,24,320,191.89
swinv2_cr_base_384,55.08,433.269,24,384,87.88
nfnet_f2,54.73,1750.573,96,256,193.78
tresnet_m_448,53.52,3584.333,192,448,31.39
dm_nfnet_f2,51.26,1245.084,64,256,193.78
cait_s24_384,50.14,476.064,24,384,47.06
swinv2_cr_huge_224,49.63,481.092,24,224,657.83
regnetx_320,48.06,2662.024,128,224,107.81
xcit_large_24_p16_384_dist,48.02,496.416,24,384,189.1
swin_large_patch4_window12_384,41.75,381.31,16,384,196.74
convnext_xlarge_384_in22ft1k,40.45,591.485,24,384,350.2
deit3_huge_patch14_224_in21ft1k,38.41,414.036,16,224,632.13
deit3_huge_patch14_224,38.4,414.103,16,224,632.13
efficientnet_b7,37.97,207.232,8,600,66.35
tf_efficientnet_b7_ap,37.27,210.986,8,600,66.35
tf_efficientnet_b7_ns,37.25,211.249,8,600,66.35
tf_efficientnet_b7,37.22,211.329,8,600,66.35
eca_nfnet_l3,35.61,1344.526,48,352,72.04
xcit_large_24_p8_224_dist,35.32,449.68,16,224,188.93
xcit_large_24_p8_224,35.06,452.952,16,224,188.93
resnetv2_50x3_bitm,34.68,460.605,16,448,217.32
swinv2_cr_large_384,32.56,488.949,16,384,196.68
cait_s36_384,32.3,491.641,16,384,68.37
densenet264d_iabn,32.11,3982.48,128,224,72.74
resnetv2_152x2_bit_teacher_384,31.17,382.508,12,384,236.34
xcit_small_24_p8_384_dist,31.12,510.761,16,384,47.63
resnest200e,30.3,1579.149,48,320,70.2
vit_large_patch16_384,29.14,410.147,12,384,304.72
deit3_large_patch16_384,28.26,422.768,12,384,304.76
deit3_large_patch16_384_in21ft1k,28.25,422.94,12,384,304.76
swinv2_base_window12to24_192to384_22kft1k,28.19,423.281,12,384,87.92
nfnet_f3,26.1,1834.868,48,320,254.92
beit_large_patch16_384,25.3,472.219,12,384,305.0
tresnet_l_448,25.28,5060.321,128,448,55.99
volo_d3_448,24.7,321.403,8,448,86.63
dm_nfnet_f3,24.56,1297.967,32,320,254.92
tresnet_xl_448,23.27,4122.323,96,448,78.44
efficientnet_b8,22.93,257.589,6,672,87.41
tf_efficientnet_b8,22.71,260.18,6,672,87.41
tf_efficientnet_b8_ap,22.69,260.555,6,672,87.41
resnetv2_152x2_bitm,22.58,351.774,8,448,236.34
vit_giant_patch14_224,22.32,355.766,8,224,1012.61
ig_resnext101_32x32d,21.03,1519.993,32,224,468.53
xcit_medium_24_p8_384_dist,21.03,376.997,8,384,84.32
convmixer_1536_20,20.83,2303.059,48,224,51.63
resnetv2_101x3_bitm,18.05,441.706,8,448,387.93
volo_d4_448,17.57,338.789,6,448,193.41
swinv2_large_window12to24_192to384_22kft1k,16.62,358.548,6,384,196.74
resnest269e,16.0,1493.085,24,416,110.93
nfnet_f4,14.17,1687.74,24,384,316.07
swinv2_cr_huge_384,13.13,454.627,6,384,657.94
dm_nfnet_f4,12.96,1229.019,16,384,316.07
xcit_large_24_p8_384_dist,12.19,488.758,6,384,188.93
cait_m36_384,11.91,500.148,6,384,271.22
volo_d5_448,11.43,346.654,4,448,295.91
ig_resnext101_32x48d,11.2,1427.437,16,224,828.41
tf_efficientnet_l2_ns_475,10.96,267.912,3,475,480.31
dm_nfnet_f5,9.76,1222.345,12,416,377.21
beit_large_patch16_512,9.42,422.548,4,512,305.67
volo_d5_512,8.0,371.847,3,512,296.09
nfnet_f5,8.0,1992.337,16,416,377.21
dm_nfnet_f6,7.45,1065.231,8,448,438.36
nfnet_f6,5.82,2052.248,12,448,438.36
nfnet_f7,5.73,1387.07,8,480,499.5
resnetv2_152x4_bitm,4.89,406.668,2,480,936.53
cait_m48_448,4.76,414.936,2,448,356.46
efficientnet_l2,3.95,247.515,1,800,480.31
tf_efficientnet_l2_ns,3.93,248.975,1,800,480.31
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/generate_csv_results.py | import numpy as np
import pandas as pd
results = {
'results-imagenet.csv': [
'results-imagenet-real.csv',
'results-imagenetv2-matched-frequency.csv',
'results-sketch.csv'
],
'results-imagenet-a-clean.csv': [
'results-imagenet-a.csv',
],
'results-imagenet-r-clean.csv': [
'results-imagenet-r.csv',
],
}
def diff(base_df, test_csv):
base_models = base_df['model'].values
test_df = pd.read_csv(test_csv)
test_models = test_df['model'].values
rank_diff = np.zeros_like(test_models, dtype='object')
top1_diff = np.zeros_like(test_models, dtype='object')
top5_diff = np.zeros_like(test_models, dtype='object')
for rank, model in enumerate(test_models):
if model in base_models:
base_rank = int(np.where(base_models == model)[0])
top1_d = test_df['top1'][rank] - base_df['top1'][base_rank]
top5_d = test_df['top5'][rank] - base_df['top5'][base_rank]
# rank_diff
if rank == base_rank:
rank_diff[rank] = f'0'
elif rank > base_rank:
rank_diff[rank] = f'-{rank - base_rank}'
else:
rank_diff[rank] = f'+{base_rank - rank}'
# top1_diff
if top1_d >= .0:
top1_diff[rank] = f'+{top1_d:.3f}'
else:
top1_diff[rank] = f'-{abs(top1_d):.3f}'
# top5_diff
if top5_d >= .0:
top5_diff[rank] = f'+{top5_d:.3f}'
else:
top5_diff[rank] = f'-{abs(top5_d):.3f}'
else:
rank_diff[rank] = ''
top1_diff[rank] = ''
top5_diff[rank] = ''
test_df['top1_diff'] = top1_diff
test_df['top5_diff'] = top5_diff
test_df['rank_diff'] = rank_diff
test_df['param_count'] = test_df['param_count'].map('{:,.2f}'.format)
test_df.sort_values(['top1', 'top5', 'model'], ascending=[False, False, True], inplace=True)
test_df.to_csv(test_csv, index=False, float_format='%.3f')
for base_results, test_results in results.items():
base_df = pd.read_csv(base_results)
base_df.sort_values(['top1', 'top5', 'model'], ascending=[False, False, True], inplace=True)
for test_csv in test_results:
diff(base_df, test_csv)
base_df['param_count'] = base_df['param_count'].map('{:,.2f}'.format)
base_df.to_csv(base_results, index=False, float_format='%.3f')
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/model_metadata-in1k.csv | model,pretrain
adv_inception_v3,in1k-adv
bat_resnext26ts,in1k
beit_base_patch16_224,in21k-selfsl
beit_base_patch16_384,in21k-selfsl
beit_large_patch16_224,in21k-selfsl
beit_large_patch16_384,in21k-selfsl
beit_large_patch16_512,in21k-selfsl
botnet26t_256,in1k
cait_m36_384,in1k-dist
cait_m48_448,in1k-dist
cait_s24_224,in1k-dist
cait_s24_384,in1k-dist
cait_s36_384,in1k-dist
cait_xs24_384,in1k-dist
cait_xxs24_224,in1k-dist
cait_xxs24_384,in1k-dist
cait_xxs36_224,in1k-dist
cait_xxs36_384,in1k-dist
coat_lite_mini,in1k
coat_lite_small,in1k
coat_lite_tiny,in1k
coat_mini,in1k
coat_tiny,in1k
convit_base,in1k
convit_small,in1k
convit_tiny,in1k
convmixer_1024_20_ks9_p14,in1k
convmixer_1536_20,in1k
convmixer_768_32,in1k
crossvit_15_240,in1k
crossvit_15_dagger_240,in1k
crossvit_15_dagger_408,in1k
crossvit_18_240,in1k
crossvit_18_dagger_240,in1k
crossvit_18_dagger_408,in1k
crossvit_9_240,in1k
crossvit_9_dagger_240,in1k
crossvit_base_240,in1k
crossvit_small_240,in1k
crossvit_tiny_240,in1k
cspdarknet53,in1k
cspresnet50,in1k
cspresnext50,in1k
deit_base_distilled_patch16_224,in1k-dist
deit_base_distilled_patch16_384,in1k-dist
deit_base_patch16_224,in1k
deit_base_patch16_384,in1k
deit_small_distilled_patch16_224,in1k-dist
deit_small_patch16_224,in1k
deit_tiny_distilled_patch16_224,in1k-dist
deit_tiny_patch16_224,in1k
densenet121,in1k
densenet161,in1k
densenet169,in1k
densenet201,in1k
densenetblur121d,in1k
dla102,in1k
dla102x,in1k
dla102x2,in1k
dla169,in1k
dla34,in1k
dla46_c,in1k
dla46x_c,in1k
dla60,in1k
dla60_res2net,in1k
dla60_res2next,in1k
dla60x,in1k
dla60x_c,in1k
dm_nfnet_f0,in1k
dm_nfnet_f1,in1k
dm_nfnet_f2,in1k
dm_nfnet_f3,in1k
dm_nfnet_f4,in1k
dm_nfnet_f5,in1k
dm_nfnet_f6,in1k
dpn107,in1k
dpn131,in1k
dpn68,in1k
dpn68b,in1k
dpn92,in1k
dpn98,in1k
eca_botnext26ts_256,in1k
eca_halonext26ts,in1k
eca_nfnet_l0,in1k
eca_nfnet_l1,in1k
eca_nfnet_l2,in1k
eca_resnet33ts,in1k
eca_resnext26ts,in1k
ecaresnet101d,in1k
ecaresnet101d_pruned,in1k
ecaresnet269d,in1k
ecaresnet26t,in1k
ecaresnet50d,in1k
ecaresnet50d_pruned,in1k
ecaresnet50t,in1k
ecaresnetlight,in1k
efficientnet_b0,in1k
efficientnet_b1,in1k
efficientnet_b1_pruned,in1k
efficientnet_b2,in1k
efficientnet_b2_pruned,in1k
efficientnet_b3,in1k
efficientnet_b3_pruned,in1k
efficientnet_b4,in1k
efficientnet_el,in1k
efficientnet_el_pruned,in1k
efficientnet_em,in1k
efficientnet_es,in1k
efficientnet_es_pruned,in1k
efficientnet_lite0,in1k
efficientnetv2_rw_m,in1k
efficientnetv2_rw_s,in1k
efficientnetv2_rw_t,in1k
ens_adv_inception_resnet_v2,in1k-adv
ese_vovnet19b_dw,in1k
ese_vovnet39b,in1k
fbnetc_100,in1k
gc_efficientnetv2_rw_t,in1k
gcresnet33ts,in1k
gcresnet50t,in1k
gcresnext26ts,in1k
gcresnext50ts,in1k
gernet_l,in1k
gernet_m,in1k
gernet_s,in1k
ghostnet_100,in1k
gluon_inception_v3,in1k
gluon_resnet101_v1b,in1k
gluon_resnet101_v1c,in1k
gluon_resnet101_v1d,in1k
gluon_resnet101_v1s,in1k
gluon_resnet152_v1b,in1k
gluon_resnet152_v1c,in1k
gluon_resnet152_v1d,in1k
gluon_resnet152_v1s,in1k
gluon_resnet18_v1b,in1k
gluon_resnet34_v1b,in1k
gluon_resnet50_v1b,in1k
gluon_resnet50_v1c,in1k
gluon_resnet50_v1d,in1k
gluon_resnet50_v1s,in1k
gluon_resnext101_32x4d,in1k
gluon_resnext101_64x4d,in1k
gluon_resnext50_32x4d,in1k
gluon_senet154,in1k
gluon_seresnext101_32x4d,in1k
gluon_seresnext101_64x4d,in1k
gluon_seresnext50_32x4d,in1k
gluon_xception65,in1k
gmixer_24_224,in1k
gmlp_s16_224,in1k
halo2botnet50ts_256,in1k
halonet26t,in1k
halonet50ts,in1k
haloregnetz_b,in1k
hardcorenas_a,in1k
hardcorenas_b,in1k
hardcorenas_c,in1k
hardcorenas_d,in1k
hardcorenas_e,in1k
hardcorenas_f,in1k
hrnet_w18,in1k
hrnet_w18_small,in1k
hrnet_w18_small_v2,in1k
hrnet_w30,in1k
hrnet_w32,in1k
hrnet_w40,in1k
hrnet_w44,in1k
hrnet_w48,in1k
hrnet_w64,in1k
ig_resnext101_32x16d,ig1b-wsl
ig_resnext101_32x32d,ig1b-wsl
ig_resnext101_32x48d,ig1b-wsl
ig_resnext101_32x8d,ig1b-wsl
inception_resnet_v2,in1k
inception_v3,in1k
inception_v4,in1k
jx_nest_base,in1k
jx_nest_small,in1k
jx_nest_tiny,in1k
lambda_resnet26rpt_256,in1k
lambda_resnet26t,in1k
lambda_resnet50ts,in1k
lamhalobotnet50ts_256,in1k
legacy_senet154,in1k
legacy_seresnet101,in1k
legacy_seresnet152,in1k
legacy_seresnet18,in1k
legacy_seresnet34,in1k
legacy_seresnet50,in1k
legacy_seresnext101_32x4d,in1k
legacy_seresnext26_32x4d,in1k
legacy_seresnext50_32x4d,in1k
levit_128,in1k-dist
levit_128s,in1k-dist
levit_192,in1k-dist
levit_256,in1k-dist
levit_384,in1k-dist
mixer_b16_224,in1k
mixer_b16_224_miil,in21k
mixer_l16_224,in1k
mixnet_l,in1k
mixnet_m,in1k
mixnet_s,in1k
mixnet_xl,in1k
mnasnet_100,in1k
mobilenetv2_100,in1k
mobilenetv2_110d,in1k
mobilenetv2_120d,in1k
mobilenetv2_140,in1k
mobilenetv3_large_100,in1k
mobilenetv3_large_100_miil,in21k
mobilenetv3_rw,in1k
nasnetalarge,in1k
nf_regnet_b1,in1k
nf_resnet50,in1k
nfnet_l0,in1k
pit_b_224,in1k
pit_b_distilled_224,in1k-dist
pit_s_224,in1k
pit_s_distilled_224,in1k-dist
pit_ti_224,in1k
pit_ti_distilled_224,in1k-dist
pit_xs_224,in1k
pit_xs_distilled_224,in1k-dist
pnasnet5large,in1k
regnetx_002,in1k
regnetx_004,in1k
regnetx_006,in1k
regnetx_008,in1k
regnetx_016,in1k
regnetx_032,in1k
regnetx_040,in1k
regnetx_064,in1k
regnetx_080,in1k
regnetx_120,in1k
regnetx_160,in1k
regnetx_320,in1k
regnety_002,in1k
regnety_004,in1k
regnety_006,in1k
regnety_008,in1k
regnety_016,in1k
regnety_032,in1k
regnety_040,in1k
regnety_064,in1k
regnety_080,in1k
regnety_120,in1k
regnety_160,in1k
regnety_320,in1k
regnetz_b,in1k
regnetz_c,in1k
regnetz_d,in1k
repvgg_a2,in1k
repvgg_b0,in1k
repvgg_b1,in1k
repvgg_b1g4,in1k
repvgg_b2,in1k
repvgg_b2g4,in1k
repvgg_b3,in1k
repvgg_b3g4,in1k
res2net101_26w_4s,in1k
res2net50_14w_8s,in1k
res2net50_26w_4s,in1k
res2net50_26w_6s,in1k
res2net50_26w_8s,in1k
res2net50_48w_2s,in1k
res2next50,in1k
resmlp_12_224,in1k
resmlp_12_distilled_224,in1k-dist
resmlp_24_224,in1k
resmlp_24_distilled_224,in1k-dist
resmlp_36_224,in1k
resmlp_36_distilled_224,in1k-dist
resmlp_big_24_224,in1k
resmlp_big_24_224_in22ft1k,in21k
resmlp_big_24_distilled_224,in1k-dist
resnest101e,in1k
resnest14d,in1k
resnest200e,in1k
resnest269e,in1k
resnest26d,in1k
resnest50d,in1k
resnest50d_1s4x24d,in1k
resnest50d_4s2x40d,in1k
resnet101d,in1k
resnet152d,in1k
resnet18,in1k
resnet18d,in1k
resnet200d,in1k
resnet26,in1k
resnet26d,in1k
resnet26t,in1k
resnet32ts,in1k
resnet33ts,in1k
resnet34,in1k
resnet34d,in1k
resnet50,in1k
resnet50d,in1k
resnet51q,in1k
resnet61q,in1k
resnetblur50,in1k
resnetrs101,in1k
resnetrs152,in1k
resnetrs200,in1k
resnetrs270,in1k
resnetrs350,in1k
resnetrs420,in1k
resnetrs50,in1k
resnetv2_101,in1k
resnetv2_101x1_bitm,in21k
resnetv2_101x3_bitm,in21k
resnetv2_152x2_bit_teacher,in21k
resnetv2_152x2_bit_teacher_384,in21k
resnetv2_152x2_bitm,in21k
resnetv2_152x4_bitm,in21k
resnetv2_50,in1k
resnetv2_50x1_bit_distilled,in1k-dist
resnetv2_50x1_bitm,in21k
resnetv2_50x3_bitm,in21k
resnext101_32x8d,in1k
resnext26ts,in1k
resnext50_32x4d,in1k
resnext50d_32x4d,in1k
rexnet_100,in1k
rexnet_130,in1k
rexnet_150,in1k
rexnet_200,in1k
sehalonet33ts,in1k
selecsls42b,in1k
selecsls60,in1k
selecsls60b,in1k
semnasnet_100,in1k
seresnet152d,in1k
seresnet33ts,in1k
seresnet50,in1k
seresnext26d_32x4d,in1k
seresnext26t_32x4d,in1k
seresnext26ts,in1k
seresnext50_32x4d,in1k
skresnet18,in1k
skresnet34,in1k
skresnext50_32x4d,in1k
spnasnet_100,in1k
ssl_resnet18,yfc-semisl
ssl_resnet50,yfc-semisl
ssl_resnext101_32x16d,yfc-semisl
ssl_resnext101_32x4d,yfc-semisl
ssl_resnext101_32x8d,yfc-semisl
ssl_resnext50_32x4d,yfc-semisl
swin_base_patch4_window12_384,in21k
swin_base_patch4_window7_224,in21k
swin_large_patch4_window12_384,in21k
swin_large_patch4_window7_224,in21k
swin_small_patch4_window7_224,in1k
swin_tiny_patch4_window7_224,in1k
swsl_resnet18,ig1b-swsl
swsl_resnet50,ig1b-swsl
swsl_resnext101_32x16d,ig1b-swsl
swsl_resnext101_32x4d,ig1b-swsl
swsl_resnext101_32x8d,ig1b-swsl
swsl_resnext50_32x4d,ig1b-swsl
tf_efficientnet_b0,in1k
tf_efficientnet_b0_ap,in1k-ap
tf_efficientnet_b0_ns,jft300m-ns
tf_efficientnet_b1,in1k
tf_efficientnet_b1_ap,in1k-ap
tf_efficientnet_b1_ns,jft300m-ns
tf_efficientnet_b2,in1k
tf_efficientnet_b2_ap,in1k-ap
tf_efficientnet_b2_ns,jft300m-ns
tf_efficientnet_b3,in1k
tf_efficientnet_b3_ap,in1k-ap
tf_efficientnet_b3_ns,jft300m-ns
tf_efficientnet_b4,in1k
tf_efficientnet_b4_ap,in1k-ap
tf_efficientnet_b4_ns,jft300m-ns
tf_efficientnet_b5,in1k
tf_efficientnet_b5_ap,in1k-ap
tf_efficientnet_b5_ns,jft300m-ns
tf_efficientnet_b6,in1k
tf_efficientnet_b6_ap,in1k-ap
tf_efficientnet_b6_ns,jft300m-ns
tf_efficientnet_b7,in1k
tf_efficientnet_b7_ap,in1k-ap
tf_efficientnet_b7_ns,jft300m-ns
tf_efficientnet_b8,in1k
tf_efficientnet_b8_ap,in1k-ap
tf_efficientnet_cc_b0_4e,in1k
tf_efficientnet_cc_b0_8e,in1k
tf_efficientnet_cc_b1_8e,in1k
tf_efficientnet_el,in1k
tf_efficientnet_em,in1k
tf_efficientnet_es,in1k
tf_efficientnet_l2_ns,jft300m-ns
tf_efficientnet_l2_ns_475,jft300m-ns
tf_efficientnet_lite0,in1k
tf_efficientnet_lite1,in1k
tf_efficientnet_lite2,in1k
tf_efficientnet_lite3,in1k
tf_efficientnet_lite4,in1k
tf_efficientnetv2_b0,in1k
tf_efficientnetv2_b1,in1k
tf_efficientnetv2_b2,in1k
tf_efficientnetv2_b3,in1k
tf_efficientnetv2_l,in1k
tf_efficientnetv2_l_in21ft1k,in21k
tf_efficientnetv2_m,in1k
tf_efficientnetv2_m_in21ft1k,in21k
tf_efficientnetv2_s,in1k
tf_efficientnetv2_s_in21ft1k,in21k
tf_efficientnetv2_xl_in21ft1k,in21k
tf_inception_v3,in1k
tf_mixnet_l,in1k
tf_mixnet_m,in1k
tf_mixnet_s,in1k
tf_mobilenetv3_large_075,in1k
tf_mobilenetv3_large_100,in1k
tf_mobilenetv3_large_minimal_100,in1k
tf_mobilenetv3_small_075,in1k
tf_mobilenetv3_small_100,in1k
tf_mobilenetv3_small_minimal_100,in1k
tnt_s_patch16_224,in1k
tresnet_l,in1k
tresnet_l_448,in1k
tresnet_m,in21k
tresnet_m_448,in1k
tresnet_xl,in1k
tresnet_xl_448,in1k
tv_densenet121,in1k
tv_resnet101,in1k
tv_resnet152,in1k
tv_resnet34,in1k
tv_resnet50,in1k
tv_resnext50_32x4d,in1k
twins_pcpvt_base,in1k
twins_pcpvt_large,in1k
twins_pcpvt_small,in1k
twins_svt_base,in1k
twins_svt_large,in1k
twins_svt_small,in1k
vgg11,in1k
vgg11_bn,in1k
vgg13,in1k
vgg13_bn,in1k
vgg16,in1k
vgg16_bn,in1k
vgg19,in1k
vgg19_bn,in1k
visformer_small,in1k
vit_base_patch16_224,in21k
vit_base_patch16_224_miil,in21k
vit_base_patch16_384,in21k
vit_base_patch16_224_sam,in1k
vit_base_patch32_224,in21k
vit_base_patch32_384,in21k
vit_base_patch32_224_sam,in1k
vit_base_r50_s16_384,in21k
vit_large_patch16_224,in21k
vit_large_patch16_384,in21k
vit_large_patch32_384,in21k
vit_large_r50_s32_224,in21k
vit_large_r50_s32_384,in21k
vit_small_patch16_224,in21k
vit_small_patch16_384,in21k
vit_small_patch32_224,in21k
vit_small_patch32_384,in21k
vit_small_r26_s32_224,in21k
vit_small_r26_s32_384,in21k
vit_tiny_patch16_224,in21k
vit_tiny_patch16_384,in21k
vit_tiny_r_s16_p8_224,in21k
vit_tiny_r_s16_p8_384,in21k
wide_resnet101_2,in1k
wide_resnet50_2,in1k
xception,in1k
xception41,in1k
xception65,in1k
xception71,in1k
xcit_large_24_p16_224,in1k
xcit_large_24_p16_224_dist,in1k-dist
xcit_large_24_p16_384_dist,in1k-dist
xcit_large_24_p8_224,in1k
xcit_large_24_p8_224_dist,in1k-dist
xcit_large_24_p8_384_dist,in1k-dist
xcit_medium_24_p16_224,in1k
xcit_medium_24_p16_224_dist,in1k-dist
xcit_medium_24_p16_384_dist,in1k-dist
xcit_medium_24_p8_224,in1k
xcit_medium_24_p8_224_dist,in1k-dist
xcit_medium_24_p8_384_dist,in1k-dist
xcit_nano_12_p16_224,in1k
xcit_nano_12_p16_224_dist,in1k-dist
xcit_nano_12_p16_384_dist,in1k-dist
xcit_nano_12_p8_224,in1k
xcit_nano_12_p8_224_dist,in1k-dist
xcit_nano_12_p8_384_dist,in1k-dist
xcit_small_12_p16_224,in1k
xcit_small_12_p16_224_dist,in1k-dist
xcit_small_12_p16_384_dist,in1k-dist
xcit_small_12_p8_224,in1k
xcit_small_12_p8_224_dist,in1k-dist
xcit_small_12_p8_384_dist,in1k-dist
xcit_small_24_p16_224,in1k
xcit_small_24_p16_224_dist,in1k-dist
xcit_small_24_p16_384_dist,in1k-dist
xcit_small_24_p8_224,in1k
xcit_small_24_p8_224_dist,in1k-dist
xcit_small_24_p8_384_dist,in1k-dist
xcit_tiny_12_p16_224,in1k
xcit_tiny_12_p16_224_dist,in1k-dist
xcit_tiny_12_p16_384_dist,in1k-dist
xcit_tiny_12_p8_224,in1k
xcit_tiny_12_p8_224_dist,in1k-dist
xcit_tiny_12_p8_384_dist,in1k-dist
xcit_tiny_24_p16_224,in1k
xcit_tiny_24_p16_224_dist,in1k-dist
xcit_tiny_24_p16_384_dist,in1k-dist
xcit_tiny_24_p8_224,in1k
xcit_tiny_24_p8_224_dist,in1k-dist
xcit_tiny_24_p8_384_dist,in1k-dist
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-a-clean.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,98.930,1.070,99.910,0.090,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,98.850,1.150,99.880,0.120,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_in22k_ft_in1k,98.840,1.160,99.830,0.170,305.08,448,1.000,bicubic
eva_giant_patch14_560.m30m_ft_in22k_in1k,98.830,1.170,99.900,0.100,"1,014.45",560,1.000,bicubic
eva_giant_patch14_336.clip_ft_in1k,98.820,1.180,99.810,0.190,"1,013.01",336,1.000,bicubic
eva_giant_patch14_336.m30m_ft_in22k_in1k,98.810,1.190,99.900,0.100,"1,013.01",336,1.000,bicubic
eva_large_patch14_336.in22k_ft_in22k_in1k,98.740,1.260,99.810,0.190,304.53,336,1.000,bicubic
eva_large_patch14_336.in22k_ft_in1k,98.730,1.270,99.870,0.130,304.53,336,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in1k,98.730,1.270,99.790,0.210,305.08,448,1.000,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_384,98.670,1.330,99.860,0.140,660.29,384,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,98.640,1.360,99.800,0.200,87.12,448,1.000,bicubic
maxvit_base_tf_512.in21k_ft_in1k,98.620,1.380,99.800,0.200,119.88,512,1.000,bicubic
maxvit_xlarge_tf_512.in21k_ft_in1k,98.620,1.380,99.800,0.200,475.77,512,1.000,bicubic
maxvit_large_tf_512.in21k_ft_in1k,98.620,1.380,99.790,0.210,212.33,512,1.000,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_512,98.600,1.400,99.870,0.130,660.29,512,1.000,bicubic
beit_large_patch16_512.in22k_ft_in22k_in1k,98.560,1.440,99.840,0.160,305.67,512,1.000,bicubic
tf_efficientnet_l2.ns_jft_in1k,98.550,1.450,99.820,0.180,480.31,800,0.960,bicubic
beitv2_large_patch16_224.in1k_ft_in22k_in1k,98.540,1.460,99.760,0.240,304.43,224,0.950,bicubic
beit_large_patch16_384.in22k_ft_in22k_in1k,98.520,1.480,99.820,0.180,305.00,384,1.000,bicubic
maxvit_base_tf_384.in21k_ft_in1k,98.520,1.480,99.750,0.250,119.65,384,1.000,bicubic
tf_efficientnet_l2.ns_jft_in1k_475,98.500,1.500,99.830,0.170,480.31,475,0.936,bicubic
maxvit_xlarge_tf_384.in21k_ft_in1k,98.500,1.500,99.780,0.220,475.32,384,1.000,bicubic
maxvit_large_tf_384.in21k_ft_in1k,98.490,1.510,99.750,0.250,212.03,384,1.000,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,98.480,1.520,99.780,0.220,200.13,384,1.000,bicubic
deit3_large_patch16_384.fb_in22k_ft_in1k,98.460,1.540,99.760,0.240,304.76,384,1.000,bicubic
eva_giant_patch14_224.clip_ft_in1k,98.460,1.540,99.750,0.250,"1,012.56",224,0.900,bicubic
regnety_1280.swag_ft_in1k,98.450,1.550,99.870,0.130,644.81,384,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k_384,98.450,1.550,99.810,0.190,98.75,384,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in1k,98.440,1.560,99.820,0.180,87.12,448,1.000,bicubic
convnext_xxlarge.clip_laion2b_soup_ft_in1k,98.440,1.560,99.800,0.200,846.47,256,1.000,bicubic
eva_large_patch14_196.in22k_ft_in22k_in1k,98.430,1.570,99.770,0.230,304.14,196,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k_384,98.420,1.580,99.810,0.190,350.20,384,1.000,bicubic
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,98.420,1.580,99.810,0.190,632.46,336,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k_384,98.400,1.600,99.760,0.240,197.96,384,1.000,bicubic
eva_large_patch14_196.in22k_ft_in1k,98.360,1.640,99.820,0.180,304.14,196,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k_384,98.350,1.650,99.770,0.230,88.72,384,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,98.340,1.660,99.760,0.240,304.53,336,1.000,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,98.300,1.700,99.760,0.240,632.05,224,1.000,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,98.280,1.720,99.770,0.230,200.13,320,1.000,bicubic
vit_large_patch14_clip_336.openai_ft_in12k_in1k,98.270,1.730,99.770,0.230,304.53,336,1.000,bicubic
convformer_b36.sail_in22k_ft_in1k_384,98.260,1.740,99.830,0.170,99.88,384,1.000,bicubic
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,98.260,1.740,99.780,0.220,116.09,384,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,98.250,1.750,99.760,0.240,200.13,384,1.000,bicubic
convnext_large.fb_in22k_ft_in1k_384,98.240,1.760,99.750,0.250,197.77,384,1.000,bicubic
vit_large_patch16_384.augreg_in21k_ft_in1k,98.220,1.780,99.800,0.200,304.72,384,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in12k_in1k,98.220,1.780,99.720,0.280,304.20,224,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in1k,98.220,1.780,99.720,0.280,304.53,336,1.000,bicubic
vit_base_patch16_clip_384.openai_ft_in12k_in1k,98.190,1.810,99.660,0.340,86.86,384,0.950,bicubic
beit_large_patch16_224.in22k_ft_in22k_in1k,98.180,1.820,99.760,0.240,304.43,224,0.900,bicubic
deit3_large_patch16_224.fb_in22k_ft_in1k,98.170,1.830,99.760,0.240,304.37,224,1.000,bicubic
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,98.170,1.830,99.760,0.240,116.14,384,1.000,bicubic
deit3_huge_patch14_224.fb_in22k_ft_in1k,98.170,1.830,99.730,0.270,632.13,224,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in1k,98.170,1.830,99.660,0.340,304.20,224,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k,98.160,1.840,99.780,0.220,98.75,224,1.000,bicubic
caformer_m36.sail_in22k_ft_in1k_384,98.150,1.850,99.750,0.250,56.20,384,1.000,bicubic
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,98.130,1.870,99.710,0.290,196.74,384,1.000,bicubic
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,98.120,1.880,99.780,0.220,87.92,384,1.000,bicubic
convnext_large.fb_in22k_ft_in1k,98.120,1.880,99.740,0.260,197.77,288,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k,98.110,1.890,99.780,0.220,350.20,288,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k,98.090,1.910,99.770,0.230,197.96,288,1.000,bicubic
convnext_base.fb_in22k_ft_in1k_384,98.080,1.920,99.650,0.350,88.59,384,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,98.070,1.930,99.760,0.240,304.20,224,1.000,bicubic
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,98.070,1.930,99.720,0.280,73.88,384,1.000,bicubic
regnety_320.swag_ft_in1k,98.060,1.940,99.860,0.140,145.05,384,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k,98.060,1.940,99.760,0.240,88.72,288,1.000,bicubic
swin_large_patch4_window12_384.ms_in22k_ft_in1k,98.050,1.950,99.690,0.310,196.74,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,98.040,1.960,99.750,0.250,88.59,384,1.000,bicubic
convformer_m36.sail_in22k_ft_in1k_384,98.040,1.960,99.690,0.310,57.05,384,1.000,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in1k,98.020,1.980,99.720,0.280,632.05,224,1.000,bicubic
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,97.990,2.010,99.660,0.340,86.86,384,1.000,bicubic
caformer_s36.sail_in22k_ft_in1k_384,97.970,2.030,99.720,0.280,39.30,384,1.000,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,97.970,2.030,99.700,0.300,93.59,320,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,97.950,2.050,99.710,0.290,200.13,256,1.000,bicubic
convformer_b36.sail_in22k_ft_in1k,97.940,2.060,99.760,0.240,99.88,224,1.000,bicubic
tf_efficientnet_b7.ns_jft_in1k,97.920,2.080,99.720,0.280,66.35,600,0.949,bicubic
beitv2_large_patch16_224.in1k_ft_in1k,97.910,2.090,99.660,0.340,304.43,224,0.950,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k,97.910,2.090,99.660,0.340,93.59,288,1.000,bicubic
swin_base_patch4_window12_384.ms_in22k_ft_in1k,97.900,2.100,99.710,0.290,87.90,384,1.000,bicubic
convnextv2_huge.fcmae_ft_in1k,97.900,2.100,99.670,0.330,660.29,288,1.000,bicubic
tf_efficientnetv2_xl.in21k_ft_in1k,97.900,2.100,99.570,0.430,208.12,512,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in1k,97.890,2.110,99.650,0.350,304.20,224,1.000,bicubic
convnext_base.fb_in22k_ft_in1k,97.860,2.140,99.680,0.320,88.59,288,1.000,bicubic
vit_large_r50_s32_384.augreg_in21k_ft_in1k,97.860,2.140,99.670,0.330,329.09,384,1.000,bicubic
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,97.850,2.150,99.650,0.350,196.74,256,0.900,bicubic
convformer_s36.sail_in22k_ft_in1k_384,97.850,2.150,99.640,0.360,40.01,384,1.000,bicubic
deit3_base_patch16_384.fb_in22k_ft_in1k,97.840,2.160,99.680,0.320,86.88,384,1.000,bicubic
caformer_m36.sail_in22k_ft_in1k,97.840,2.160,99.670,0.330,56.20,224,1.000,bicubic
vit_base_patch16_384.augreg_in21k_ft_in1k,97.840,2.160,99.670,0.330,86.86,384,1.000,bicubic
maxvit_large_tf_512.in1k,97.830,2.170,99.560,0.440,212.33,512,1.000,bicubic
beit_base_patch16_384.in22k_ft_in22k_in1k,97.820,2.180,99.700,0.300,86.74,384,1.000,bicubic
tf_efficientnetv2_m.in21k_ft_in1k,97.820,2.180,99.600,0.400,54.14,480,1.000,bicubic
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,97.810,2.190,99.650,0.350,116.14,224,0.950,bicubic
tf_efficientnetv2_l.in21k_ft_in1k,97.800,2.200,99.770,0.230,118.52,480,1.000,bicubic
convnext_small.in12k_ft_in1k_384,97.800,2.200,99.660,0.340,50.22,384,1.000,bicubic
regnety_160.swag_ft_in1k,97.780,2.220,99.760,0.240,83.59,384,1.000,bicubic
dm_nfnet_f6.dm_in1k,97.780,2.220,99.650,0.350,438.36,576,0.956,bicubic
dm_nfnet_f5.dm_in1k,97.780,2.220,99.600,0.400,377.21,544,0.954,bicubic
volo_d5_512.sail_in1k,97.770,2.230,99.670,0.330,296.09,512,1.150,bicubic
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,97.760,2.240,99.700,0.300,116.09,224,0.950,bicubic
volo_d5_448.sail_in1k,97.750,2.250,99.620,0.380,295.91,448,1.150,bicubic
maxvit_small_tf_512.in1k,97.750,2.250,99.550,0.450,69.13,512,1.000,bicubic
maxvit_base_tf_512.in1k,97.730,2.270,99.610,0.390,119.88,512,1.000,bicubic
vit_base_patch16_clip_384.laion2b_ft_in1k,97.720,2.280,99.630,0.370,86.86,384,1.000,bicubic
beitv2_base_patch16_224.in1k_ft_in22k_in1k,97.690,2.310,99.680,0.320,86.53,224,0.900,bicubic
vit_base_patch8_224.augreg2_in21k_ft_in1k,97.690,2.310,99.650,0.350,86.58,224,0.900,bicubic
volo_d4_448.sail_in1k,97.670,2.330,99.610,0.390,193.41,448,1.150,bicubic
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,97.660,2.340,99.720,0.280,87.92,256,0.900,bicubic
convnextv2_large.fcmae_ft_in1k,97.660,2.340,99.610,0.390,197.96,288,1.000,bicubic
regnety_1280.swag_lc_in1k,97.650,2.350,99.640,0.360,644.81,224,0.965,bicubic
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,97.650,2.350,99.570,0.430,73.88,224,0.950,bicubic
swin_large_patch4_window7_224.ms_in22k_ft_in1k,97.650,2.350,99.570,0.430,196.53,224,0.900,bicubic
dm_nfnet_f4.dm_in1k,97.640,2.360,99.540,0.460,316.07,512,0.951,bicubic
vit_large_patch16_224.augreg_in21k_ft_in1k,97.630,2.370,99.590,0.410,304.33,224,0.900,bicubic
tf_efficientnet_b6.ns_jft_in1k,97.620,2.380,99.580,0.420,43.04,528,0.942,bicubic
convnext_base.clip_laiona_augreg_ft_in1k_384,97.620,2.380,99.550,0.450,88.59,384,1.000,bicubic
convnext_small.fb_in22k_ft_in1k_384,97.610,2.390,99.600,0.400,50.22,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,97.600,2.400,99.720,0.280,88.59,256,1.000,bicubic
convformer_m36.sail_in22k_ft_in1k,97.600,2.400,99.620,0.380,57.05,224,1.000,bicubic
caformer_s36.sail_in22k_ft_in1k,97.600,2.400,99.610,0.390,39.30,224,1.000,bicubic
maxvit_tiny_tf_512.in1k,97.580,2.420,99.560,0.440,31.05,512,1.000,bicubic
vit_base_patch8_224.augreg_in21k_ft_in1k,97.570,2.430,99.670,0.330,86.58,224,0.900,bicubic
maxvit_base_tf_384.in1k,97.570,2.430,99.590,0.410,119.65,384,1.000,bicubic
maxvit_large_tf_384.in1k,97.570,2.430,99.530,0.470,212.03,384,1.000,bicubic
volo_d3_448.sail_in1k,97.550,2.450,99.560,0.440,86.63,448,1.000,bicubic
vit_base_patch16_clip_384.openai_ft_in1k,97.540,2.460,99.660,0.340,86.86,384,1.000,bicubic
convformer_b36.sail_in1k_384,97.530,2.470,99.520,0.480,99.88,384,1.000,bicubic
vit_base_patch16_clip_224.openai_ft_in12k_in1k,97.530,2.470,99.500,0.500,86.57,224,0.950,bicubic
coatnet_2_rw_224.sw_in12k_ft_in1k,97.520,2.480,99.600,0.400,73.87,224,0.950,bicubic
xcit_large_24_p8_384.fb_dist_in1k,97.520,2.480,99.540,0.460,188.93,384,1.000,bicubic
xcit_large_24_p16_384.fb_dist_in1k,97.520,2.480,99.480,0.520,189.10,384,1.000,bicubic
tf_efficientnet_b5.ns_jft_in1k,97.500,2.500,99.640,0.360,30.39,456,0.934,bicubic
caformer_b36.sail_in1k_384,97.500,2.500,99.580,0.420,98.75,384,1.000,bicubic
resnetv2_152x4_bit.goog_in21k_ft_in1k,97.490,2.510,99.610,0.390,936.53,480,1.000,bilinear
deit3_base_patch16_224.fb_in22k_ft_in1k,97.480,2.520,99.600,0.400,86.59,224,1.000,bicubic
cait_m48_448.fb_dist_in1k,97.480,2.520,99.550,0.450,356.46,448,1.000,bicubic
dm_nfnet_f3.dm_in1k,97.470,2.530,99.560,0.440,254.92,416,0.940,bicubic
tf_efficientnetv2_l.in1k,97.470,2.530,99.530,0.470,118.52,480,1.000,bicubic
regnety_160.lion_in12k_ft_in1k,97.450,2.550,99.600,0.400,83.59,288,1.000,bicubic
regnety_160.sw_in12k_ft_in1k,97.450,2.550,99.590,0.410,83.59,288,1.000,bicubic
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,97.450,2.550,99.540,0.460,86.57,224,0.950,bicubic
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,97.440,2.560,99.640,0.360,39.03,384,0.950,bicubic
caformer_m36.sail_in1k_384,97.440,2.560,99.600,0.400,56.20,384,1.000,bicubic
maxvit_small_tf_384.in1k,97.430,2.570,99.510,0.490,69.02,384,1.000,bicubic
deit3_large_patch16_384.fb_in1k,97.420,2.580,99.620,0.380,304.76,384,1.000,bicubic
caformer_s18.sail_in22k_ft_in1k_384,97.420,2.580,99.570,0.430,26.34,384,1.000,bicubic
flexivit_large.1200ep_in1k,97.410,2.590,99.600,0.400,304.36,240,0.950,bicubic
efficientnet_b5.sw_in12k_ft_in1k,97.410,2.590,99.550,0.450,30.39,448,1.000,bicubic
convformer_m36.sail_in1k_384,97.410,2.590,99.470,0.530,57.05,384,1.000,bicubic
cait_m36_384.fb_dist_in1k,97.400,2.600,99.510,0.490,271.22,384,1.000,bicubic
caformer_s36.sail_in1k_384,97.390,2.610,99.540,0.460,39.30,384,1.000,bicubic
volo_d5_224.sail_in1k,97.380,2.620,99.570,0.430,295.46,224,0.960,bicubic
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,97.370,2.630,99.680,0.320,468.53,224,0.875,bilinear
convnext_small.fb_in22k_ft_in1k,97.360,2.640,99.530,0.470,50.22,288,1.000,bicubic
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,97.360,2.640,99.520,0.480,88.30,384,1.000,bicubic
convnext_small.in12k_ft_in1k,97.350,2.650,99.580,0.420,50.22,288,1.000,bicubic
convnext_tiny.in12k_ft_in1k_384,97.340,2.660,99.600,0.400,28.59,384,1.000,bicubic
cait_s36_384.fb_dist_in1k,97.330,2.670,99.540,0.460,68.37,384,1.000,bicubic
volo_d2_384.sail_in1k,97.320,2.680,99.600,0.400,58.87,384,1.000,bicubic
maxvit_tiny_tf_384.in1k,97.310,2.690,99.500,0.500,30.98,384,1.000,bicubic
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,97.310,2.690,99.480,0.520,88.34,448,1.000,bicubic
flexivit_large.600ep_in1k,97.280,2.720,99.590,0.410,304.36,240,0.950,bicubic
swin_base_patch4_window7_224.ms_in22k_ft_in1k,97.280,2.720,99.540,0.460,87.77,224,0.900,bicubic
regnety_120.sw_in12k_ft_in1k,97.280,2.720,99.530,0.470,51.82,288,1.000,bicubic
volo_d4_224.sail_in1k,97.280,2.720,99.520,0.480,192.96,224,0.960,bicubic
xcit_medium_24_p8_384.fb_dist_in1k,97.280,2.720,99.510,0.490,84.32,384,1.000,bicubic
xcit_medium_24_p16_384.fb_dist_in1k,97.280,2.720,99.470,0.530,84.40,384,1.000,bicubic
convformer_s36.sail_in1k_384,97.280,2.720,99.430,0.570,40.01,384,1.000,bicubic
convformer_s18.sail_in22k_ft_in1k_384,97.270,2.730,99.550,0.450,26.77,384,1.000,bicubic
flexivit_large.300ep_in1k,97.250,2.750,99.490,0.510,304.36,240,0.950,bicubic
xcit_small_24_p8_384.fb_dist_in1k,97.240,2.760,99.610,0.390,47.63,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in1k,97.240,2.760,99.550,0.450,88.59,256,1.000,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k_384,97.240,2.760,99.520,0.480,28.64,384,1.000,bicubic
xcit_small_12_p8_384.fb_dist_in1k,97.230,2.770,99.480,0.520,26.21,384,1.000,bicubic
convnextv2_base.fcmae_ft_in1k,97.220,2.780,99.540,0.460,88.72,288,1.000,bicubic
regnety_2560.seer_ft_in1k,97.220,2.780,99.520,0.480,"1,282.60",384,1.000,bicubic
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,97.210,2.790,99.570,0.430,88.79,224,0.875,bilinear
tf_efficientnetv2_m.in1k,97.210,2.790,99.530,0.470,54.14,480,1.000,bicubic
regnetz_e8.ra3_in1k,97.200,2.800,99.500,0.500,57.70,320,1.000,bicubic
tf_efficientnet_b8.ra_in1k,97.200,2.800,99.500,0.500,87.41,672,0.954,bicubic
vit_base_r50_s16_384.orig_in21k_ft_in1k,97.190,2.810,99.560,0.440,98.95,384,1.000,bicubic
tf_efficientnet_b7.ap_in1k,97.190,2.810,99.540,0.460,66.35,600,0.949,bicubic
beitv2_base_patch16_224.in1k_ft_in1k,97.170,2.830,99.470,0.530,86.53,224,0.900,bicubic
regnety_320.swag_lc_in1k,97.160,2.840,99.670,0.330,145.05,224,0.965,bicubic
vit_base_patch16_224.augreg2_in21k_ft_in1k,97.150,2.850,99.540,0.460,86.57,224,0.900,bicubic
coat_lite_medium_384.in1k,97.150,2.850,99.450,0.550,44.57,384,1.000,bicubic
eva02_small_patch14_336.mim_in22k_ft_in1k,97.140,2.860,99.470,0.530,22.13,336,1.000,bicubic
deit3_small_patch16_384.fb_in22k_ft_in1k,97.130,2.870,99.500,0.500,22.21,384,1.000,bicubic
vit_base_patch16_clip_224.laion2b_ft_in1k,97.130,2.870,99.460,0.540,86.57,224,1.000,bicubic
xcit_small_24_p16_384.fb_dist_in1k,97.120,2.880,99.450,0.550,47.67,384,1.000,bicubic
tf_efficientnet_b8.ap_in1k,97.110,2.890,99.660,0.340,87.41,672,0.954,bicubic
vit_base_patch32_clip_384.openai_ft_in12k_in1k,97.110,2.890,99.500,0.500,88.30,384,0.950,bicubic
dm_nfnet_f2.dm_in1k,97.100,2.900,99.510,0.490,193.78,352,0.920,bicubic
convnext_large.fb_in1k,97.100,2.900,99.450,0.550,197.77,288,1.000,bicubic
volo_d3_224.sail_in1k,97.090,2.910,99.460,0.540,86.33,224,0.960,bicubic
beit_base_patch16_224.in22k_ft_in22k_in1k,97.080,2.920,99.610,0.390,86.53,224,0.900,bicubic
tf_efficientnet_b6.ap_in1k,97.080,2.920,99.610,0.390,43.04,528,0.942,bicubic
convformer_s36.sail_in22k_ft_in1k,97.080,2.920,99.560,0.440,40.01,224,1.000,bicubic
convnext_tiny.fb_in22k_ft_in1k_384,97.080,2.920,99.510,0.490,28.59,384,1.000,bicubic
eca_nfnet_l2.ra3_in1k,97.080,2.920,99.510,0.490,56.72,384,1.000,bicubic
vit_base_patch16_clip_224.openai_ft_in1k,97.080,2.920,99.490,0.510,86.57,224,0.900,bicubic
ecaresnet269d.ra2_in1k,97.080,2.920,99.470,0.530,102.09,352,1.000,bicubic
caformer_s18.sail_in1k_384,97.080,2.920,99.420,0.580,26.34,384,1.000,bicubic
cait_s24_384.fb_dist_in1k,97.070,2.930,99.430,0.570,47.06,384,1.000,bicubic
xcit_large_24_p8_224.fb_dist_in1k,97.070,2.930,99.420,0.580,188.93,224,1.000,bicubic
convnext_tiny.in12k_ft_in1k,97.060,2.940,99.550,0.450,28.59,288,1.000,bicubic
convformer_s18.sail_in1k_384,97.040,2.960,99.380,0.620,26.77,384,1.000,bicubic
hrnet_w48_ssld.paddle_in1k,97.030,2.970,99.640,0.360,77.47,288,1.000,bilinear
deit3_base_patch16_384.fb_in1k,97.030,2.970,99.390,0.610,86.88,384,1.000,bicubic
dm_nfnet_f1.dm_in1k,97.030,2.970,99.390,0.610,132.63,320,0.910,bicubic
tf_efficientnet_b7.ra_in1k,97.010,2.990,99.520,0.480,66.35,600,0.949,bicubic
resnetv2_152x2_bit.goog_in21k_ft_in1k,97.000,3.000,99.590,0.410,236.34,448,1.000,bilinear
volo_d2_224.sail_in1k,97.000,3.000,99.390,0.610,58.68,224,0.960,bicubic
caformer_b36.sail_in1k,96.990,3.010,99.340,0.660,98.75,224,1.000,bicubic
efficientnetv2_rw_m.agc_in1k,96.980,3.020,99.530,0.470,53.24,416,1.000,bicubic
resnetv2_101x3_bit.goog_in21k_ft_in1k,96.980,3.020,99.490,0.510,387.93,448,1.000,bilinear
deit3_medium_patch16_224.fb_in22k_ft_in1k,96.970,3.030,99.430,0.570,38.85,224,1.000,bicubic
deit_base_distilled_patch16_384.fb_in1k,96.960,3.040,99.480,0.520,87.63,384,1.000,bicubic
seresnextaa101d_32x8d.ah_in1k,96.960,3.040,99.390,0.610,93.59,288,1.000,bicubic
maxvit_large_tf_224.in1k,96.960,3.040,99.250,0.750,211.79,224,0.950,bicubic
tf_efficientnet_b4.ns_jft_in1k,96.950,3.050,99.580,0.420,19.34,380,0.922,bicubic
maxvit_base_tf_224.in1k,96.950,3.050,99.260,0.740,119.47,224,0.950,bicubic
deit3_large_patch16_224.fb_in1k,96.940,3.060,99.340,0.660,304.37,224,0.900,bicubic
davit_base.msft_in1k,96.940,3.060,99.260,0.740,87.95,224,0.950,bicubic
mvitv2_large.fb_in1k,96.930,3.070,99.400,0.600,217.99,224,0.900,bicubic
xcit_small_12_p16_384.fb_dist_in1k,96.920,3.080,99.400,0.600,26.25,384,1.000,bicubic
xcit_medium_24_p8_224.fb_dist_in1k,96.920,3.080,99.390,0.610,84.32,224,1.000,bicubic
volo_d1_384.sail_in1k,96.910,3.090,99.520,0.480,26.78,384,1.000,bicubic
resnetrs420.tf_in1k,96.910,3.090,99.460,0.540,191.89,416,1.000,bicubic
deit3_huge_patch14_224.fb_in1k,96.900,3.100,99.480,0.520,632.13,224,0.900,bicubic
convformer_b36.sail_in1k,96.900,3.100,99.220,0.780,99.88,224,1.000,bicubic
vit_base_patch16_224.augreg_in21k_ft_in1k,96.890,3.110,99.530,0.470,86.57,224,0.900,bicubic
caformer_m36.sail_in1k,96.890,3.110,99.430,0.570,56.20,224,1.000,bicubic
xcit_small_24_p8_224.fb_dist_in1k,96.870,3.130,99.480,0.520,47.63,224,1.000,bicubic
regnety_1280.seer_ft_in1k,96.860,3.140,99.390,0.610,644.81,384,1.000,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k,96.850,3.150,99.460,0.540,28.64,288,1.000,bicubic
regnety_640.seer_ft_in1k,96.850,3.150,99.420,0.580,281.38,384,1.000,bicubic
rexnetr_300.sw_in12k_ft_in1k,96.840,3.160,99.510,0.490,34.81,288,1.000,bicubic
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,96.830,3.170,99.450,0.550,236.34,384,1.000,bicubic
convnext_base.fb_in1k,96.830,3.170,99.410,0.590,88.59,288,1.000,bicubic
regnety_160.swag_lc_in1k,96.820,3.180,99.650,0.350,83.59,224,0.965,bicubic
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,96.810,3.190,99.600,0.400,194.03,224,0.875,bilinear
maxxvit_rmlp_small_rw_256.sw_in1k,96.800,3.200,99.380,0.620,66.01,256,0.950,bicubic
xcit_large_24_p16_224.fb_dist_in1k,96.800,3.200,99.350,0.650,189.10,224,1.000,bicubic
vit_large_r50_s32_224.augreg_in21k_ft_in1k,96.790,3.210,99.340,0.660,328.99,224,0.900,bicubic
seresnet152d.ra2_in1k,96.770,3.230,99.440,0.560,66.84,320,1.000,bicubic
seresnext101_32x8d.ah_in1k,96.770,3.230,99.350,0.650,93.57,288,1.000,bicubic
mvitv2_base.fb_in1k,96.760,3.240,99.260,0.740,51.47,224,0.900,bicubic
resnetrs350.tf_in1k,96.750,3.250,99.370,0.630,163.96,384,1.000,bicubic
swinv2_base_window16_256.ms_in1k,96.750,3.250,99.350,0.650,87.92,256,0.900,bicubic
flexivit_base.1200ep_in1k,96.740,3.260,99.360,0.640,86.59,240,0.950,bicubic
edgenext_base.in21k_ft_in1k,96.730,3.270,99.420,0.580,18.51,320,1.000,bicubic
tf_efficientnetv2_s.in21k_ft_in1k,96.730,3.270,99.420,0.580,21.46,384,1.000,bicubic
resnet200d.ra2_in1k,96.730,3.270,99.330,0.670,64.69,320,1.000,bicubic
vit_base_patch16_384.orig_in21k_ft_in1k,96.720,3.280,99.500,0.500,86.86,384,1.000,bicubic
regnetz_040.ra3_in1k,96.720,3.280,99.480,0.520,27.12,320,1.000,bicubic
resnetv2_50x3_bit.goog_in21k_ft_in1k,96.710,3.290,99.550,0.450,217.32,448,1.000,bilinear
caformer_s18.sail_in22k_ft_in1k,96.710,3.290,99.480,0.520,26.34,224,1.000,bicubic
regnetz_040_h.ra3_in1k,96.700,3.300,99.500,0.500,28.94,320,1.000,bicubic
vit_small_patch16_384.augreg_in21k_ft_in1k,96.700,3.300,99.480,0.520,22.20,384,1.000,bicubic
edgenext_base.usi_in1k,96.700,3.300,99.430,0.570,18.51,320,1.000,bicubic
xcit_small_12_p8_224.fb_dist_in1k,96.700,3.300,99.380,0.620,26.21,224,1.000,bicubic
resnetrs200.tf_in1k,96.700,3.300,99.370,0.630,93.21,320,1.000,bicubic
resnetaa101d.sw_in12k_ft_in1k,96.700,3.300,99.360,0.640,44.57,288,1.000,bicubic
seresnext101d_32x8d.ah_in1k,96.700,3.300,99.360,0.640,93.59,288,1.000,bicubic
eca_nfnet_l1.ra2_in1k,96.700,3.300,99.290,0.710,41.41,320,1.000,bicubic
caformer_s36.sail_in1k,96.690,3.310,99.360,0.640,39.30,224,1.000,bicubic
maxvit_small_tf_224.in1k,96.690,3.310,99.360,0.640,68.93,224,0.950,bicubic
resnetrs270.tf_in1k,96.690,3.310,99.350,0.650,129.86,352,1.000,bicubic
vit_small_r26_s32_384.augreg_in21k_ft_in1k,96.680,3.320,99.580,0.420,36.47,384,1.000,bicubic
tf_efficientnet_b5.ap_in1k,96.680,3.320,99.460,0.540,30.39,456,0.934,bicubic
convformer_m36.sail_in1k,96.680,3.320,99.080,0.920,57.05,224,1.000,bicubic
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,96.670,3.330,99.490,0.510,38.86,256,0.950,bicubic
tf_efficientnet_b6.aa_in1k,96.670,3.330,99.370,0.630,43.04,528,0.942,bicubic
deit3_small_patch16_224.fb_in22k_ft_in1k,96.660,3.340,99.330,0.670,22.06,224,1.000,bicubic
flexivit_base.600ep_in1k,96.650,3.350,99.330,0.670,86.59,240,0.950,bicubic
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,96.640,3.360,99.160,0.840,41.72,224,0.950,bicubic
davit_small.msft_in1k,96.630,3.370,99.350,0.650,49.75,224,0.950,bicubic
convformer_s18.sail_in22k_ft_in1k,96.630,3.370,99.340,0.660,26.77,224,1.000,bicubic
resmlp_big_24_224.fb_in22k_ft_in1k,96.620,3.380,99.510,0.490,129.14,224,0.875,bicubic
regnetz_d8.ra3_in1k,96.620,3.380,99.450,0.550,23.37,320,1.000,bicubic
flexivit_base.300ep_in1k,96.620,3.380,99.270,0.730,86.59,240,0.950,bicubic
resnest200e.in1k,96.610,3.390,99.350,0.650,70.20,320,0.909,bicubic
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,96.600,3.400,99.530,0.470,194.03,224,0.875,bilinear
xcit_medium_24_p16_224.fb_dist_in1k,96.600,3.400,99.270,0.730,84.40,224,1.000,bicubic
regnetz_d32.ra3_in1k,96.590,3.410,99.380,0.620,27.58,320,0.950,bicubic
swin_base_patch4_window12_384.ms_in1k,96.580,3.420,99.250,0.750,87.90,384,1.000,bicubic
resnetrs152.tf_in1k,96.580,3.420,99.240,0.760,86.62,320,1.000,bicubic
convformer_s36.sail_in1k,96.580,3.420,99.170,0.830,40.01,224,1.000,bicubic
maxvit_rmlp_small_rw_224.sw_in1k,96.580,3.420,99.120,0.880,64.90,224,0.900,bicubic
regnetz_d8_evos.ch_in1k,96.570,3.430,99.460,0.540,23.46,320,1.000,bicubic
gcvit_base.in1k,96.570,3.430,99.230,0.770,90.32,224,0.875,bicubic
focalnet_base_srf.ms_in1k,96.560,3.440,99.140,0.860,88.15,224,0.900,bicubic
cait_xs24_384.fb_dist_in1k,96.540,3.460,99.420,0.580,26.67,384,1.000,bicubic
efficientnetv2_rw_s.ra2_in1k,96.540,3.460,99.360,0.640,23.94,384,1.000,bicubic
tf_efficientnet_b7.aa_in1k,96.540,3.460,99.300,0.700,66.35,600,0.949,bicubic
crossvit_18_dagger_408.in1k,96.540,3.460,99.260,0.740,44.61,408,1.000,bicubic
coatnet_rmlp_2_rw_224.sw_in1k,96.540,3.460,99.100,0.900,73.88,224,0.950,bicubic
xcit_tiny_24_p8_384.fb_dist_in1k,96.530,3.470,99.320,0.680,12.11,384,1.000,bicubic
swinv2_base_window8_256.ms_in1k,96.530,3.470,99.270,0.730,87.92,256,0.900,bicubic
convnext_small.fb_in1k,96.520,3.480,99.340,0.660,50.22,288,1.000,bicubic
regnety_080.ra3_in1k,96.520,3.480,99.320,0.680,39.18,288,1.000,bicubic
resnest269e.in1k,96.510,3.490,99.350,0.650,110.93,416,0.928,bicubic
vit_base_patch32_384.augreg_in21k_ft_in1k,96.490,3.510,99.410,0.590,88.30,384,1.000,bicubic
swin_small_patch4_window7_224.ms_in22k_ft_in1k,96.480,3.520,99.390,0.610,49.61,224,0.900,bicubic
tf_efficientnet_b5.aa_in1k,96.470,3.530,99.240,0.760,30.39,456,0.934,bicubic
swinv2_small_window16_256.ms_in1k,96.470,3.530,99.200,0.800,49.73,256,0.900,bicubic
coat_lite_medium.in1k,96.470,3.530,99.150,0.850,44.57,224,0.900,bicubic
cs3se_edgenet_x.c2ns_in1k,96.450,3.550,99.400,0.600,50.72,320,1.000,bicubic
resmlp_big_24_224.fb_distilled_in1k,96.450,3.550,99.310,0.690,129.14,224,0.875,bicubic
vit_base_patch16_224_miil.in21k_ft_in1k,96.450,3.550,99.300,0.700,86.54,224,0.875,bilinear
focalnet_base_lrf.ms_in1k,96.450,3.550,99.120,0.880,88.75,224,0.900,bicubic
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,96.420,3.580,99.470,0.530,44.18,224,0.875,bilinear
maxvit_rmlp_tiny_rw_256.sw_in1k,96.420,3.580,99.380,0.620,29.15,256,0.950,bicubic
cait_s24_224.fb_dist_in1k,96.420,3.580,99.150,0.850,46.92,224,1.000,bicubic
regnetv_064.ra3_in1k,96.410,3.590,99.360,0.640,30.58,288,1.000,bicubic
xcit_small_24_p8_224.fb_in1k,96.410,3.590,99.150,0.850,47.63,224,1.000,bicubic
xcit_large_24_p8_224.fb_in1k,96.400,3.600,98.990,1.010,188.93,224,1.000,bicubic
resnet152d.ra2_in1k,96.390,3.610,99.390,0.610,60.21,320,1.000,bicubic
tf_efficientnet_b3.ns_jft_in1k,96.390,3.610,99.350,0.650,12.23,300,0.904,bicubic
crossvit_15_dagger_408.in1k,96.390,3.610,99.160,0.840,28.50,408,1.000,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k_384,96.370,3.630,99.400,0.600,15.62,384,1.000,bicubic
mvitv2_small.fb_in1k,96.370,3.630,99.200,0.800,34.87,224,0.900,bicubic
xception65.ra3_in1k,96.360,3.640,99.240,0.760,39.92,299,0.940,bicubic
regnety_064.ra3_in1k,96.360,3.640,99.230,0.770,30.58,288,1.000,bicubic
pvt_v2_b5.in1k,96.360,3.640,99.170,0.830,81.96,224,0.900,bicubic
regnety_160.deit_in1k,96.350,3.650,99.330,0.670,83.59,288,1.000,bicubic
pvt_v2_b4.in1k,96.350,3.650,99.180,0.820,62.56,224,0.900,bicubic
tf_efficientnetv2_s.in1k,96.340,3.660,99.200,0.800,21.46,384,1.000,bicubic
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,96.330,3.670,99.430,0.570,88.79,224,0.875,bilinear
regnety_320.seer_ft_in1k,96.330,3.670,99.350,0.650,145.05,384,1.000,bicubic
tf_efficientnet_b5.ra_in1k,96.330,3.670,99.310,0.690,30.39,456,0.934,bicubic
volo_d1_224.sail_in1k,96.320,3.680,99.310,0.690,26.63,224,0.960,bicubic
dm_nfnet_f0.dm_in1k,96.310,3.690,99.320,0.680,71.49,256,0.900,bicubic
deit3_base_patch16_224.fb_in1k,96.300,3.700,99.180,0.820,86.59,224,0.900,bicubic
resnet101d.ra2_in1k,96.290,3.710,99.230,0.770,44.57,320,1.000,bicubic
gcvit_small.in1k,96.280,3.720,99.140,0.860,51.09,224,0.875,bicubic
swinv2_small_window8_256.ms_in1k,96.270,3.730,99.210,0.790,49.73,256,0.900,bicubic
twins_svt_large.in1k,96.250,3.750,99.170,0.830,99.27,224,0.900,bicubic
nest_base_jx.goog_in1k,96.240,3.760,99.200,0.800,67.72,224,0.875,bicubic
swin_s3_base_224.ms_in1k,96.240,3.760,99.150,0.850,71.13,224,0.900,bicubic
maxvit_tiny_rw_224.sw_in1k,96.240,3.760,99.130,0.870,29.06,224,0.950,bicubic
pit_b_distilled_224.in1k,96.230,3.770,99.110,0.890,74.79,224,0.900,bicubic
swin_s3_small_224.ms_in1k,96.230,3.770,99.080,0.920,49.74,224,0.900,bicubic
ecaresnet101d.miil_in1k,96.220,3.780,99.310,0.690,44.57,288,0.950,bicubic
rexnetr_200.sw_in12k_ft_in1k,96.220,3.780,99.260,0.740,16.52,288,1.000,bicubic
tf_efficientnetv2_b3.in21k_ft_in1k,96.220,3.780,99.230,0.770,14.36,300,0.900,bicubic
xcit_small_24_p16_224.fb_dist_in1k,96.220,3.780,99.210,0.790,47.67,224,1.000,bicubic
xception65p.ra3_in1k,96.210,3.790,99.180,0.820,39.82,299,0.940,bicubic
deit3_small_patch16_384.fb_in1k,96.200,3.800,99.300,0.700,22.21,384,1.000,bicubic
resnet152.a1h_in1k,96.200,3.800,99.220,0.780,60.19,288,1.000,bicubic
regnetv_040.ra3_in1k,96.190,3.810,99.330,0.670,20.64,288,1.000,bicubic
convnextv2_tiny.fcmae_ft_in1k,96.190,3.810,99.250,0.750,28.64,288,1.000,bicubic
gcvit_tiny.in1k,96.180,3.820,99.230,0.770,28.22,224,0.875,bicubic
focalnet_small_lrf.ms_in1k,96.180,3.820,99.190,0.810,50.34,224,0.900,bicubic
swinv2_cr_small_ns_224.sw_in1k,96.180,3.820,99.140,0.860,49.70,224,0.900,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k_384,96.180,3.820,99.120,0.880,14.25,384,1.000,bicubic
tf_efficientnet_b4.ap_in1k,96.160,3.840,99.270,0.730,19.34,380,0.922,bicubic
tresnet_v2_l.miil_in21k_ft_in1k,96.160,3.840,99.240,0.760,46.17,224,0.875,bilinear
deit_base_patch16_384.fb_in1k,96.160,3.840,99.140,0.860,86.86,384,1.000,bicubic
twins_svt_base.in1k,96.160,3.840,99.050,0.950,56.07,224,0.900,bicubic
efficientnet_b4.ra2_in1k,96.150,3.850,99.190,0.810,19.34,384,1.000,bicubic
sequencer2d_l.in1k,96.150,3.850,99.160,0.840,54.30,224,0.875,bicubic
regnetz_c16_evos.ch_in1k,96.140,3.860,99.360,0.640,13.49,320,0.950,bicubic
twins_pcpvt_large.in1k,96.140,3.860,99.170,0.830,60.99,224,0.900,bicubic
caformer_s18.sail_in1k,96.140,3.860,99.000,1.000,26.34,224,1.000,bicubic
resnetv2_50x1_bit.goog_distilled_in1k,96.130,3.870,99.280,0.720,25.55,224,0.875,bicubic
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,96.130,3.870,99.220,0.780,88.22,224,0.900,bicubic
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,96.120,3.880,99.270,0.730,236.34,224,0.875,bicubic
nfnet_l0.ra2_in1k,96.120,3.880,99.240,0.760,35.07,288,1.000,bicubic
swin_base_patch4_window7_224.ms_in1k,96.120,3.880,99.060,0.940,87.77,224,0.900,bicubic
efficientformer_l7.snap_dist_in1k,96.110,3.890,99.270,0.730,82.23,224,0.950,bicubic
xcit_small_12_p8_224.fb_in1k,96.110,3.890,99.160,0.840,26.21,224,1.000,bicubic
resnetv2_101x1_bit.goog_in21k_ft_in1k,96.100,3.900,99.280,0.720,44.54,448,1.000,bilinear
maxvit_tiny_tf_224.in1k,96.100,3.900,99.270,0.730,30.92,224,0.950,bicubic
deit_base_distilled_patch16_224.fb_in1k,96.090,3.910,99.190,0.810,87.34,224,0.900,bicubic
xcit_medium_24_p8_224.fb_in1k,96.090,3.910,98.890,1.110,84.32,224,1.000,bicubic
resnext101_64x4d.c1_in1k,96.080,3.920,99.240,0.760,83.46,288,1.000,bicubic
regnety_320.tv2_in1k,96.080,3.920,99.230,0.770,145.05,224,0.965,bicubic
xcit_tiny_12_p8_384.fb_dist_in1k,96.080,3.920,99.140,0.860,6.71,384,1.000,bicubic
swinv2_cr_small_224.sw_in1k,96.080,3.920,98.860,1.140,49.70,224,0.900,bicubic
tf_efficientnet_b5.in1k,96.070,3.930,99.290,0.710,30.39,456,0.934,bicubic
deit3_medium_patch16_224.fb_in1k,96.070,3.930,99.200,0.800,38.85,224,0.900,bicubic
efficientformerv2_l.snap_dist_in1k,96.070,3.930,99.190,0.810,26.32,224,0.950,bicubic
focalnet_small_srf.ms_in1k,96.070,3.930,99.120,0.880,49.89,224,0.900,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k,96.060,3.940,99.220,0.780,15.62,288,1.000,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k_384,96.060,3.940,99.080,0.920,18.45,384,1.000,bicubic
cs3edgenet_x.c2_in1k,96.050,3.950,99.140,0.860,47.82,288,1.000,bicubic
maxxvit_rmlp_nano_rw_256.sw_in1k,96.040,3.960,99.260,0.740,16.78,256,0.950,bicubic
resnetv2_101.a1h_in1k,96.040,3.960,99.170,0.830,44.54,288,1.000,bicubic
resnext101_64x4d.tv_in1k,96.030,3.970,99.160,0.840,83.46,224,0.875,bilinear
xcit_small_12_p16_224.fb_dist_in1k,96.030,3.970,99.130,0.870,26.25,224,1.000,bicubic
coatnet_1_rw_224.sw_in1k,96.030,3.970,99.060,0.940,41.72,224,0.950,bicubic
regnety_040.ra3_in1k,96.020,3.980,99.190,0.810,20.65,288,1.000,bicubic
resnet101.a1h_in1k,96.020,3.980,99.140,0.860,44.55,288,1.000,bicubic
cs3sedarknet_x.c2ns_in1k,96.020,3.980,99.110,0.890,35.40,288,1.000,bicubic
convnext_tiny_hnf.a2h_in1k,96.020,3.980,99.070,0.930,28.59,288,1.000,bicubic
hrnet_w18_ssld.paddle_in1k,95.990,4.010,99.320,0.680,21.30,288,1.000,bilinear
convnext_nano.in12k_ft_in1k,95.990,4.010,99.310,0.690,15.59,288,1.000,bicubic
pvt_v2_b3.in1k,95.990,4.010,99.190,0.810,45.24,224,0.900,bicubic
regnetx_320.tv2_in1k,95.990,4.010,99.100,0.900,107.81,224,0.965,bicubic
tresnet_xl.miil_in1k_448,95.980,4.020,99.130,0.870,78.44,448,0.875,bilinear
sequencer2d_s.in1k,95.980,4.020,99.050,0.950,27.65,224,0.875,bicubic
regnety_160.tv2_in1k,95.970,4.030,99.150,0.850,83.59,224,0.965,bicubic
nest_small_jx.goog_in1k,95.970,4.030,99.030,0.970,38.35,224,0.875,bicubic
maxvit_rmlp_nano_rw_256.sw_in1k,95.970,4.030,98.970,1.030,15.50,256,0.950,bicubic
regnety_032.ra_in1k,95.960,4.040,99.190,0.810,19.44,288,1.000,bicubic
coatnet_rmlp_1_rw_224.sw_in1k,95.960,4.040,99.160,0.840,41.69,224,0.950,bicubic
resnext101_32x8d.tv2_in1k,95.950,4.050,99.080,0.920,88.79,224,0.965,bilinear
convformer_s18.sail_in1k,95.950,4.050,98.900,1.100,26.77,224,1.000,bicubic
xcit_tiny_24_p16_384.fb_dist_in1k,95.940,4.060,99.220,0.780,12.12,384,1.000,bicubic
eca_nfnet_l0.ra2_in1k,95.940,4.060,99.210,0.790,24.14,288,1.000,bicubic
regnetz_c16.ra3_in1k,95.940,4.060,99.110,0.890,13.46,320,1.000,bicubic
swinv2_tiny_window16_256.ms_in1k,95.930,4.070,99.150,0.850,28.35,256,0.900,bicubic
swin_small_patch4_window7_224.ms_in1k,95.930,4.070,99.020,0.980,49.61,224,0.900,bicubic
maxvit_nano_rw_256.sw_in1k,95.920,4.080,99.010,0.990,15.45,256,0.950,bicubic
coat_small.in1k,95.910,4.090,99.150,0.850,21.69,224,0.900,bicubic
tf_efficientnet_b4.aa_in1k,95.900,4.100,99.170,0.830,19.34,380,0.922,bicubic
maxxvitv2_nano_rw_256.sw_in1k,95.900,4.100,99.050,0.950,23.70,256,0.950,bicubic
regnetx_160.tv2_in1k,95.880,4.120,99.090,0.910,54.28,224,0.965,bicubic
resnet51q.ra2_in1k,95.870,4.130,99.120,0.880,35.70,288,1.000,bilinear
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,95.860,4.140,99.250,0.750,25.03,224,0.875,bilinear
resnest101e.in1k,95.860,4.140,99.200,0.800,48.28,256,0.875,bilinear
tresnet_l.miil_in1k_448,95.860,4.140,99.120,0.880,55.99,448,0.875,bilinear
regnety_080_tv.tv2_in1k,95.860,4.140,99.100,0.900,39.38,224,0.965,bicubic
mvitv2_tiny.fb_in1k,95.860,4.140,99.070,0.930,24.17,224,0.900,bicubic
cs3darknet_x.c2ns_in1k,95.850,4.150,99.170,0.830,35.05,288,1.000,bicubic
rexnet_300.nav_in1k,95.840,4.160,99.130,0.870,34.71,224,0.875,bicubic
resnetaa50d.sw_in12k_ft_in1k,95.830,4.170,99.170,0.830,25.58,288,1.000,bicubic
vit_large_patch32_384.orig_in21k_ft_in1k,95.830,4.170,99.150,0.850,306.63,384,1.000,bicubic
cait_xxs36_384.fb_dist_in1k,95.830,4.170,99.090,0.910,17.37,384,1.000,bicubic
tf_efficientnet_b4.in1k,95.820,4.180,99.050,0.950,19.34,380,0.922,bicubic
xcit_tiny_24_p8_224.fb_dist_in1k,95.810,4.190,99.210,0.790,12.11,224,1.000,bicubic
sequencer2d_m.in1k,95.810,4.190,99.110,0.890,38.31,224,0.875,bicubic
convnextv2_nano.fcmae_ft_in1k,95.800,4.200,99.090,0.910,15.62,288,1.000,bicubic
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,95.790,4.210,99.180,0.820,194.03,224,0.875,bilinear
convnext_tiny.fb_in1k,95.790,4.210,99.160,0.840,28.59,288,1.000,bicubic
resnet61q.ra2_in1k,95.780,4.220,98.990,1.010,36.85,288,1.000,bicubic
twins_pcpvt_base.in1k,95.770,4.230,99.130,0.870,43.83,224,0.900,bicubic
tf_efficientnet_b2.ns_jft_in1k,95.770,4.230,99.120,0.880,9.11,260,0.890,bicubic
vit_relpos_base_patch16_clsgap_224.sw_in1k,95.770,4.230,99.040,0.960,86.43,224,0.900,bicubic
poolformerv2_m48.sail_in1k,95.770,4.230,98.980,1.020,73.35,224,1.000,bicubic
ecaresnet101d_pruned.miil_in1k,95.760,4.240,99.180,0.820,24.88,288,0.950,bicubic
seresnext50_32x4d.racm_in1k,95.740,4.260,99.180,0.820,27.56,288,0.950,bicubic
gc_efficientnetv2_rw_t.agc_in1k,95.740,4.260,99.020,0.980,13.68,288,1.000,bicubic
efficientnet_b3.ra2_in1k,95.720,4.280,99.040,0.960,12.23,320,1.000,bicubic
tresnet_m.miil_in21k_ft_in1k,95.710,4.290,99.030,0.970,31.39,224,0.875,bilinear
pnasnet5large.tf_in1k,95.710,4.290,98.920,1.080,86.06,331,0.911,bicubic
coatnet_bn_0_rw_224.sw_in1k,95.700,4.300,99.050,0.950,27.44,224,0.950,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k_384,95.690,4.310,99.140,0.860,10.59,384,1.000,bicubic
nasnetalarge.tf_in1k,95.690,4.310,98.930,1.070,88.75,331,0.911,bicubic
crossvit_15_dagger_240.in1k,95.680,4.320,98.830,1.170,28.21,240,0.875,bicubic
flexivit_small.600ep_in1k,95.670,4.330,99.060,0.940,22.06,240,0.950,bicubic
xcit_tiny_24_p8_224.fb_in1k,95.660,4.340,99.050,0.950,12.11,224,1.000,bicubic
davit_tiny.msft_in1k,95.660,4.340,99.030,0.970,28.36,224,0.950,bicubic
vit_small_r26_s32_224.augreg_in21k_ft_in1k,95.640,4.360,99.190,0.810,36.43,224,0.900,bicubic
wide_resnet50_2.racm_in1k,95.630,4.370,99.240,0.760,68.88,288,0.950,bicubic
resnetv2_50d_evos.ah_in1k,95.630,4.370,99.110,0.890,25.59,288,1.000,bicubic
poolformer_m48.sail_in1k,95.630,4.370,98.940,1.060,73.47,224,0.950,bicubic
pit_b_224.in1k,95.620,4.380,98.670,1.330,73.76,224,0.900,bicubic
efficientnetv2_rw_t.ra2_in1k,95.600,4.400,99.070,0.930,13.65,288,1.000,bicubic
efficientformer_l3.snap_dist_in1k,95.590,4.410,99.160,0.840,31.41,224,0.950,bicubic
gcvit_xtiny.in1k,95.590,4.410,99.040,0.960,19.98,224,0.875,bicubic
crossvit_18_dagger_240.in1k,95.570,4.430,99.060,0.940,44.27,240,0.875,bicubic
vit_relpos_base_patch16_224.sw_in1k,95.560,4.440,99.030,0.970,86.43,224,0.900,bicubic
pvt_v2_b2_li.in1k,95.560,4.440,98.990,1.010,22.55,224,0.900,bicubic
flexivit_small.1200ep_in1k,95.550,4.450,99.120,0.880,22.06,240,0.950,bicubic
convit_base.fb_in1k,95.550,4.450,98.880,1.120,86.54,224,0.875,bicubic
wide_resnet101_2.tv2_in1k,95.540,4.460,99.080,0.920,126.89,224,0.965,bilinear
coat_lite_small.in1k,95.540,4.460,98.860,1.140,19.84,224,0.900,bicubic
xcit_small_24_p16_224.fb_in1k,95.540,4.460,98.780,1.220,47.67,224,1.000,bicubic
xcit_medium_24_p16_224.fb_in1k,95.540,4.460,98.720,1.280,84.40,224,1.000,bicubic
levit_384.fb_dist_in1k,95.530,4.470,99.060,0.940,39.13,224,0.900,bicubic
levit_conv_384.fb_dist_in1k,95.530,4.470,99.050,0.950,39.13,224,0.900,bicubic
ecaresnet50t.ra2_in1k,95.520,4.480,99.110,0.890,25.57,320,0.950,bicubic
vit_base_patch32_clip_224.laion2b_ft_in1k,95.520,4.480,98.870,1.130,88.22,224,0.900,bicubic
resnet101.a1_in1k,95.520,4.480,98.850,1.150,44.55,288,1.000,bicubic
crossvit_base_240.in1k,95.520,4.480,98.810,1.190,105.03,240,0.875,bicubic
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,95.510,4.490,99.200,0.800,28.29,224,0.900,bicubic
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,95.510,4.490,99.120,0.880,88.79,224,0.875,bilinear
fbnetv3_g.ra2_in1k,95.510,4.490,98.990,1.010,16.62,288,0.950,bilinear
xception41p.ra3_in1k,95.510,4.490,98.910,1.090,26.91,299,0.940,bicubic
focalnet_tiny_srf.ms_in1k,95.500,4.500,99.130,0.870,28.43,224,0.900,bicubic
vit_relpos_medium_patch16_rpn_224.sw_in1k,95.500,4.500,99.080,0.920,38.73,224,0.900,bicubic
flexivit_small.300ep_in1k,95.500,4.500,98.960,1.040,22.06,240,0.950,bicubic
resnet152.tv2_in1k,95.500,4.500,98.960,1.040,60.19,224,0.965,bilinear
resnet152.a1_in1k,95.500,4.500,98.780,1.220,60.19,288,1.000,bicubic
swinv2_tiny_window8_256.ms_in1k,95.490,4.510,99.100,0.900,28.35,256,0.900,bicubic
visformer_small.in1k,95.490,4.510,98.900,1.100,40.22,224,0.900,bicubic
resnet152.a2_in1k,95.490,4.510,98.790,1.210,60.19,288,1.000,bicubic
pvt_v2_b2.in1k,95.480,4.520,99.000,1.000,25.36,224,0.900,bicubic
resnetv2_50d_gn.ah_in1k,95.480,4.520,98.950,1.050,25.57,288,1.000,bicubic
vit_relpos_medium_patch16_cls_224.sw_in1k,95.470,4.530,98.950,1.050,38.76,224,0.900,bicubic
vit_relpos_medium_patch16_224.sw_in1k,95.460,4.540,98.960,1.040,38.75,224,0.900,bicubic
focalnet_tiny_lrf.ms_in1k,95.460,4.540,98.910,1.090,28.65,224,0.900,bicubic
ecaresnet50d.miil_in1k,95.450,4.550,99.090,0.910,25.58,288,0.950,bicubic
deit_base_patch16_224.fb_in1k,95.450,4.550,98.840,1.160,86.57,224,0.900,bicubic
resnext50_32x4d.a1h_in1k,95.450,4.550,98.840,1.160,25.03,288,1.000,bicubic
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,95.440,4.560,99.120,0.880,44.18,224,0.875,bilinear
tresnet_xl.miil_in1k,95.440,4.560,99.050,0.950,78.44,224,0.875,bilinear
crossvit_18_240.in1k,95.440,4.560,98.790,1.210,43.27,240,0.875,bicubic
coatnet_0_rw_224.sw_in1k,95.440,4.560,98.720,1.280,27.44,224,0.950,bicubic
resnetrs101.tf_in1k,95.430,4.570,99.040,0.960,63.62,288,0.940,bicubic
coatnext_nano_rw_224.sw_in1k,95.430,4.570,99.010,0.990,14.70,224,0.900,bicubic
coatnet_rmlp_nano_rw_224.sw_in1k,95.430,4.570,98.990,1.010,15.15,224,0.900,bicubic
xcit_large_24_p16_224.fb_in1k,95.430,4.570,98.630,1.370,189.10,224,1.000,bicubic
halo2botnet50ts_256.a1h_in1k,95.420,4.580,99.020,0.980,22.64,256,0.950,bicubic
xcit_small_12_p16_224.fb_in1k,95.420,4.580,98.840,1.160,26.25,224,1.000,bicubic
ecaresnet50t.a1_in1k,95.410,4.590,99.010,0.990,25.57,288,1.000,bicubic
resnet101.a2_in1k,95.410,4.590,98.940,1.060,44.55,288,1.000,bicubic
resnet50.fb_swsl_ig1b_ft_in1k,95.400,4.600,99.300,0.700,25.56,224,0.875,bilinear
edgenext_small.usi_in1k,95.400,4.600,99.100,0.900,5.59,320,1.000,bicubic
poolformerv2_m36.sail_in1k,95.400,4.600,98.870,1.130,56.08,224,1.000,bicubic
vit_base_patch16_rpn_224.sw_in1k,95.390,4.610,98.940,1.060,86.54,224,0.900,bicubic
poolformer_m36.sail_in1k,95.390,4.610,98.860,1.140,56.17,224,0.950,bicubic
vit_small_patch16_224.augreg_in21k_ft_in1k,95.370,4.630,99.140,0.860,22.05,224,0.900,bicubic
swinv2_cr_tiny_ns_224.sw_in1k,95.370,4.630,98.940,1.060,28.33,224,0.900,bicubic
efficientformerv2_s2.snap_dist_in1k,95.360,4.640,98.930,1.070,12.71,224,0.950,bicubic
ecaresnet50t.a2_in1k,95.350,4.650,98.920,1.080,25.57,288,1.000,bicubic
convnext_nano.d1h_in1k,95.350,4.650,98.860,1.140,15.59,288,1.000,bicubic
vit_base_patch16_224.orig_in21k_ft_in1k,95.340,4.660,99.000,1.000,86.57,224,0.900,bicubic
seresnet50.ra2_in1k,95.330,4.670,99.010,0.990,28.09,288,0.950,bicubic
tf_efficientnet_b3.ap_in1k,95.320,4.680,98.900,1.100,12.23,300,0.904,bicubic
cs3sedarknet_l.c2ns_in1k,95.310,4.690,99.130,0.870,21.91,288,0.950,bicubic
poolformerv2_s36.sail_in1k,95.310,4.690,98.920,1.080,30.79,224,1.000,bicubic
regnety_032.tv2_in1k,95.310,4.690,98.910,1.090,19.44,224,0.965,bicubic
mixer_b16_224.miil_in21k_ft_in1k,95.300,4.700,98.880,1.120,59.88,224,0.875,bilinear
ecaresnetlight.miil_in1k,95.290,4.710,99.030,0.970,30.16,288,0.950,bicubic
vit_small_patch16_384.augreg_in1k,95.290,4.710,99.000,1.000,22.20,384,1.000,bicubic
tresnet_l.miil_in1k,95.280,4.720,99.010,0.990,55.99,224,0.875,bilinear
cait_xxs24_384.fb_dist_in1k,95.280,4.720,98.960,1.040,12.03,384,1.000,bicubic
resnet101.tv2_in1k,95.270,4.730,98.910,1.090,44.55,224,0.965,bilinear
resnet50_gn.a1h_in1k,95.250,4.750,99.000,1.000,25.56,288,0.950,bicubic
vit_srelpos_medium_patch16_224.sw_in1k,95.240,4.760,98.990,1.010,38.74,224,0.900,bicubic
nest_tiny_jx.goog_in1k,95.240,4.760,98.980,1.020,17.06,224,0.875,bicubic
gcresnet50t.ra2_in1k,95.240,4.760,98.910,1.090,25.90,288,1.000,bicubic
coatnet_nano_rw_224.sw_in1k,95.240,4.760,98.870,1.130,15.14,224,0.900,bicubic
convnextv2_pico.fcmae_ft_in1k,95.230,4.770,98.920,1.080,9.07,288,0.950,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k,95.220,4.780,98.800,1.200,14.25,256,0.888,bicubic
convit_small.fb_in1k,95.210,4.790,98.900,1.100,27.78,224,0.875,bicubic
twins_pcpvt_small.in1k,95.210,4.790,98.880,1.120,24.11,224,0.900,bicubic
resnetaa50.a1h_in1k,95.200,4.800,98.920,1.080,25.56,288,1.000,bicubic
twins_svt_small.in1k,95.190,4.810,98.880,1.120,24.06,224,0.900,bicubic
swin_s3_tiny_224.ms_in1k,95.180,4.820,98.950,1.050,28.33,224,0.900,bicubic
tf_efficientnetv2_b3.in1k,95.180,4.820,98.820,1.180,14.36,300,0.904,bicubic
regnetz_b16.ra3_in1k,95.170,4.830,99.080,0.920,9.72,288,1.000,bicubic
tf_efficientnet_b1.ns_jft_in1k,95.160,4.840,99.100,0.900,7.79,240,0.882,bicubic
cs3darknet_focus_l.c2ns_in1k,95.160,4.840,98.960,1.040,21.15,288,0.950,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k,95.160,4.840,98.950,1.050,18.45,256,0.888,bicubic
lamhalobotnet50ts_256.a1h_in1k,95.160,4.840,98.880,1.120,22.57,256,0.950,bicubic
vit_relpos_small_patch16_224.sw_in1k,95.150,4.850,98.960,1.040,21.98,224,0.900,bicubic
crossvit_15_240.in1k,95.150,4.850,98.930,1.070,27.53,240,0.875,bicubic
pit_s_distilled_224.in1k,95.140,4.860,98.890,1.110,24.04,224,0.900,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k,95.140,4.860,98.860,1.140,10.59,256,0.888,bicubic
swin_tiny_patch4_window7_224.ms_in1k,95.140,4.860,98.850,1.150,28.29,224,0.900,bicubic
halonet50ts.a1h_in1k,95.140,4.860,98.780,1.220,22.73,256,0.940,bicubic
convnext_nano_ols.d1h_in1k,95.140,4.860,98.730,1.270,15.65,288,1.000,bicubic
xcit_tiny_12_p16_384.fb_dist_in1k,95.130,4.870,99.020,0.980,6.72,384,1.000,bicubic
cs3darknet_l.c2ns_in1k,95.120,4.880,98.980,1.020,21.16,288,0.950,bicubic
efficientnet_el.ra_in1k,95.120,4.880,98.970,1.030,10.59,300,0.904,bicubic
vit_base_patch32_clip_224.openai_ft_in1k,95.110,4.890,98.980,1.020,88.22,224,0.900,bicubic
ecaresnet50d_pruned.miil_in1k,95.110,4.890,98.930,1.070,19.94,288,0.950,bicubic
gernet_l.idstcv_in1k,95.110,4.890,98.900,1.100,31.08,256,0.875,bilinear
regnetx_080.tv2_in1k,95.100,4.900,98.830,1.170,39.57,224,0.965,bicubic
xcit_tiny_12_p8_224.fb_dist_in1k,95.090,4.910,98.910,1.090,6.71,224,1.000,bicubic
convmixer_1536_20.in1k,95.080,4.920,99.030,0.970,51.63,224,0.960,bicubic
poolformer_s36.sail_in1k,95.080,4.920,98.910,1.090,30.86,224,0.900,bicubic
legacy_senet154.in1k,95.070,4.930,98.830,1.170,115.09,224,0.875,bilinear
seresnet33ts.ra2_in1k,95.040,4.960,98.900,1.100,19.78,288,1.000,bicubic
tnt_s_patch16_224,95.040,4.960,98.830,1.170,23.76,224,0.900,bicubic
vit_small_patch32_384.augreg_in21k_ft_in1k,95.030,4.970,98.990,1.010,22.92,384,1.000,bicubic
vit_srelpos_small_patch16_224.sw_in1k,95.030,4.970,98.950,1.050,21.97,224,0.900,bicubic
resnet152s.gluon_in1k,95.020,4.980,98.930,1.070,60.32,224,0.875,bicubic
levit_256.fb_dist_in1k,95.020,4.980,98.880,1.120,18.89,224,0.900,bicubic
levit_conv_256.fb_dist_in1k,95.020,4.980,98.880,1.120,18.89,224,0.900,bicubic
resnetv2_50x1_bit.goog_in21k_ft_in1k,95.010,4.990,99.060,0.940,25.55,448,1.000,bilinear
vit_base_patch32_224.augreg_in21k_ft_in1k,95.010,4.990,99.030,0.970,88.22,224,0.900,bicubic
resnet50d.ra2_in1k,95.010,4.990,98.980,1.020,25.58,288,0.950,bicubic
tf_efficientnet_b3.aa_in1k,95.000,5.000,98.910,1.090,12.23,300,0.904,bicubic
tresnet_m.miil_in1k_448,94.990,5.010,98.980,1.020,31.39,448,0.875,bilinear
deit3_small_patch16_224.fb_in1k,94.990,5.010,98.460,1.540,22.06,224,0.900,bicubic
resnet50.d_in1k,94.980,5.020,98.840,1.160,25.56,288,1.000,bicubic
resnest50d_4s2x40d.in1k,94.970,5.030,99.080,0.920,30.42,224,0.875,bicubic
coat_mini.in1k,94.970,5.030,98.780,1.220,10.34,224,0.900,bicubic
rexnet_200.nav_in1k,94.940,5.060,99.010,0.990,16.37,224,0.875,bicubic
vit_base_patch16_384.augreg_in1k,94.940,5.060,98.890,1.110,86.86,384,1.000,bicubic
seresnext101_64x4d.gluon_in1k,94.930,5.070,98.830,1.170,88.23,224,0.875,bicubic
gcresnet33ts.ra2_in1k,94.920,5.080,98.810,1.190,19.88,288,1.000,bicubic
resnet50.c2_in1k,94.920,5.080,98.810,1.190,25.56,288,1.000,bicubic
senet154.gluon_in1k,94.920,5.080,98.760,1.240,115.09,224,0.875,bicubic
eva02_tiny_patch14_336.mim_in22k_ft_in1k,94.910,5.090,98.880,1.120,5.76,336,1.000,bicubic
mobilevitv2_175.cvnets_in1k,94.890,5.110,98.860,1.140,14.25,256,0.888,bicubic
resmlp_36_224.fb_distilled_in1k,94.890,5.110,98.860,1.140,44.69,224,0.875,bicubic
seresnext101_32x4d.gluon_in1k,94.890,5.110,98.820,1.180,48.96,224,0.875,bicubic
tf_efficientnet_lite4.in1k,94.880,5.120,99.020,0.980,13.01,380,0.920,bilinear
wide_resnet50_2.tv2_in1k,94.870,5.130,98.940,1.060,68.88,224,0.965,bilinear
ese_vovnet39b.ra_in1k,94.870,5.130,98.910,1.090,24.57,288,0.950,bicubic
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,94.860,5.140,98.870,1.130,25.03,224,0.875,bilinear
gcresnext50ts.ch_in1k,94.860,5.140,98.860,1.140,15.67,288,1.000,bicubic
resnet50.b1k_in1k,94.860,5.140,98.810,1.190,25.56,288,1.000,bicubic
crossvit_small_240.in1k,94.850,5.150,99.010,0.990,26.86,240,0.875,bicubic
resnest50d.in1k,94.850,5.150,98.880,1.120,27.48,224,0.875,bilinear
resnetv2_50.a1h_in1k,94.850,5.150,98.870,1.130,25.55,288,1.000,bicubic
mobilevitv2_200.cvnets_in1k,94.840,5.160,98.710,1.290,18.45,256,0.888,bicubic
convnext_tiny.fb_in22k_ft_in1k,94.840,5.160,98.530,1.470,28.59,288,1.000,bicubic
cspresnext50.ra_in1k,94.830,5.170,98.770,1.230,20.57,256,0.887,bilinear
res2net101d.in1k,94.820,5.180,98.770,1.230,45.23,224,0.875,bilinear
resnext50_32x4d.a1_in1k,94.820,5.180,98.600,1.400,25.03,288,1.000,bicubic
resnet50.a1h_in1k,94.770,5.230,98.690,1.310,25.56,224,1.000,bicubic
lambda_resnet50ts.a1h_in1k,94.770,5.230,98.470,1.530,21.54,256,0.950,bicubic
convnext_pico.d1_in1k,94.760,5.240,98.710,1.290,9.05,288,0.950,bicubic
sehalonet33ts.ra2_in1k,94.760,5.240,98.570,1.430,13.69,256,0.940,bicubic
resnet50.a1_in1k,94.740,5.260,98.710,1.290,25.56,288,1.000,bicubic
resnest50d_1s4x24d.in1k,94.730,5.270,98.980,1.020,25.68,224,0.875,bicubic
resnet50.c1_in1k,94.730,5.270,98.930,1.070,25.56,288,1.000,bicubic
resnet50.b2k_in1k,94.730,5.270,98.820,1.180,25.56,288,1.000,bicubic
resnet152d.gluon_in1k,94.730,5.270,98.750,1.250,60.21,224,0.875,bicubic
resnet152.a3_in1k,94.730,5.270,98.680,1.320,60.19,224,0.950,bicubic
resnet50d.a1_in1k,94.730,5.270,98.490,1.510,25.58,288,1.000,bicubic
resnet101s.gluon_in1k,94.720,5.280,98.820,1.180,44.67,224,0.875,bicubic
deit_small_distilled_patch16_224.fb_in1k,94.710,5.290,99.030,0.970,22.44,224,0.900,bicubic
resnext50_32x4d.ra_in1k,94.700,5.300,98.760,1.240,25.03,288,0.950,bicubic
xcit_tiny_12_p8_224.fb_in1k,94.690,5.310,98.830,1.170,6.71,224,1.000,bicubic
haloregnetz_b.ra3_in1k,94.690,5.310,98.660,1.340,11.68,224,0.940,bicubic
resmlp_big_24_224.fb_in1k,94.680,5.320,98.500,1.500,129.14,224,0.875,bicubic
edgenext_small_rw.sw_in1k,94.670,5.330,98.780,1.220,7.83,320,1.000,bicubic
resnext101_64x4d.gluon_in1k,94.670,5.330,98.660,1.340,83.46,224,0.875,bicubic
regnetx_032.tv2_in1k,94.660,5.340,98.850,1.150,15.30,224,0.965,bicubic
resnet50.tv2_in1k,94.660,5.340,98.790,1.210,25.56,224,0.965,bilinear
seresnet50.a2_in1k,94.660,5.340,98.780,1.220,28.09,288,1.000,bicubic
seresnet50.a1_in1k,94.660,5.340,98.720,1.280,28.09,288,1.000,bicubic
poolformerv2_s24.sail_in1k,94.650,5.350,98.840,1.160,21.34,224,1.000,bicubic
cspdarknet53.ra_in1k,94.650,5.350,98.800,1.200,27.64,256,0.887,bilinear
maxvit_rmlp_pico_rw_256.sw_in1k,94.630,5.370,98.810,1.190,7.52,256,0.950,bicubic
efficientnet_b3_pruned.in1k,94.630,5.370,98.760,1.240,9.86,300,0.904,bicubic
resnet50.a2_in1k,94.630,5.370,98.660,1.340,25.56,288,1.000,bicubic
tresnet_m.miil_in1k,94.630,5.370,98.550,1.450,31.39,224,0.875,bilinear
eca_resnet33ts.ra2_in1k,94.620,5.380,98.910,1.090,19.68,288,1.000,bicubic
darknet53.c2ns_in1k,94.620,5.380,98.900,1.100,41.61,288,1.000,bicubic
gernet_m.idstcv_in1k,94.620,5.380,98.860,1.140,21.14,224,0.875,bilinear
resnext50_32x4d.tv2_in1k,94.620,5.380,98.780,1.220,25.03,224,0.965,bilinear
convnext_pico_ols.d1_in1k,94.620,5.380,98.760,1.240,9.06,288,1.000,bicubic
efficientnet_b2.ra_in1k,94.620,5.380,98.710,1.290,9.11,288,1.000,bicubic
sebotnet33ts_256.a1h_in1k,94.610,5.390,98.510,1.490,13.70,256,0.940,bicubic
inception_resnet_v2.tf_in1k,94.580,5.420,98.790,1.210,55.84,299,0.897,bicubic
resnet50d.a2_in1k,94.580,5.420,98.690,1.310,25.58,288,1.000,bicubic
resnext50_32x4d.a2_in1k,94.580,5.420,98.650,1.350,25.03,288,1.000,bicubic
pit_s_224.in1k,94.570,5.430,98.700,1.300,23.46,224,0.900,bicubic
poolformer_s24.sail_in1k,94.560,5.440,98.900,1.100,21.39,224,0.900,bicubic
repvgg_b3.rvgg_in1k,94.550,5.450,98.780,1.220,123.09,224,0.875,bilinear
mobilevitv2_150.cvnets_in1k,94.550,5.450,98.710,1.290,10.59,256,0.888,bicubic
resnext50d_32x4d.bt_in1k,94.550,5.450,98.690,1.310,25.05,288,0.950,bicubic
regnety_320.pycls_in1k,94.540,5.460,98.850,1.150,145.05,224,0.875,bicubic
tf_efficientnet_b3.in1k,94.540,5.460,98.800,1.200,12.23,300,0.904,bicubic
nf_resnet50.ra2_in1k,94.540,5.460,98.790,1.210,25.56,288,0.940,bicubic
xcit_tiny_24_p16_224.fb_dist_in1k,94.540,5.460,98.780,1.220,12.12,224,1.000,bicubic
resnext101_32x4d.gluon_in1k,94.540,5.460,98.630,1.370,44.18,224,0.875,bicubic
regnety_016.tv2_in1k,94.520,5.480,98.820,1.180,11.20,224,0.965,bicubic
resnet50.ram_in1k,94.520,5.480,98.650,1.350,25.56,288,0.950,bicubic
repvgg_b3g4.rvgg_in1k,94.510,5.490,98.970,1.030,83.83,224,0.875,bilinear
tf_efficientnet_b2.ap_in1k,94.510,5.490,98.620,1.380,9.11,260,0.890,bicubic
convmixer_768_32.in1k,94.500,5.500,98.860,1.140,21.11,224,0.960,bicubic
efficientformer_l1.snap_dist_in1k,94.480,5.520,98.830,1.170,12.29,224,0.950,bicubic
rexnet_150.nav_in1k,94.480,5.520,98.800,1.200,9.73,224,0.875,bicubic
regnety_120.pycls_in1k,94.470,5.530,98.820,1.180,51.82,224,0.875,bicubic
darknetaa53.c2ns_in1k,94.470,5.530,98.760,1.240,36.02,288,1.000,bilinear
resnetblur50.bt_in1k,94.460,5.540,98.840,1.160,25.56,288,0.950,bicubic
resnet50.fb_ssl_yfcc100m_ft_in1k,94.450,5.550,98.920,1.080,25.56,224,0.875,bilinear
resmlp_24_224.fb_distilled_in1k,94.450,5.550,98.770,1.230,30.02,224,0.875,bicubic
regnetx_320.pycls_in1k,94.450,5.550,98.740,1.260,107.81,224,0.875,bicubic
gcvit_xxtiny.in1k,94.420,5.580,98.890,1.110,12.00,224,0.875,bicubic
tf_efficientnetv2_b2.in1k,94.420,5.580,98.580,1.420,10.10,260,0.890,bicubic
efficientnet_el_pruned.in1k,94.400,5.600,98.740,1.260,10.59,300,0.904,bicubic
tf_efficientnet_el.in1k,94.400,5.600,98.710,1.290,10.59,300,0.904,bicubic
tf_efficientnet_b2.aa_in1k,94.380,5.620,98.610,1.390,9.11,260,0.890,bicubic
deit_small_patch16_224.fb_in1k,94.370,5.630,98.700,1.300,22.05,224,0.900,bicubic
inception_v4.tf_in1k,94.370,5.630,98.580,1.420,42.68,299,0.875,bicubic
regnety_160.pycls_in1k,94.360,5.640,98.860,1.140,83.59,224,0.875,bicubic
legacy_seresnext101_32x4d.in1k,94.360,5.640,98.630,1.370,48.96,224,0.875,bilinear
ecaresnet50t.a3_in1k,94.350,5.650,98.670,1.330,25.57,224,0.950,bicubic
dpn107.mx_in1k,94.340,5.660,98.500,1.500,86.92,224,0.875,bicubic
seresnext50_32x4d.gluon_in1k,94.330,5.670,98.610,1.390,27.56,224,0.875,bicubic
ecaresnet26t.ra2_in1k,94.320,5.680,98.720,1.280,16.01,320,0.950,bicubic
resnetrs50.tf_in1k,94.320,5.680,98.640,1.360,35.69,224,0.910,bicubic
res2net50d.in1k,94.320,5.680,98.530,1.470,25.72,224,0.875,bilinear
resnet50.bt_in1k,94.310,5.690,98.640,1.360,25.56,288,0.950,bicubic
xception71.tf_in1k,94.300,5.700,98.650,1.350,42.34,299,0.903,bicubic
dpn92.mx_in1k,94.290,5.710,98.750,1.250,37.67,224,0.875,bicubic
cait_xxs36_224.fb_dist_in1k,94.270,5.730,98.710,1.290,17.30,224,1.000,bicubic
skresnext50_32x4d.ra_in1k,94.240,5.760,98.460,1.540,27.48,224,0.875,bicubic
regnetx_120.pycls_in1k,94.230,5.770,98.670,1.330,46.11,224,0.875,bicubic
resnet101d.gluon_in1k,94.230,5.770,98.540,1.460,44.57,224,0.875,bicubic
resnet50.ra_in1k,94.210,5.790,98.620,1.380,25.56,288,0.950,bicubic
tf_efficientnet_lite3.in1k,94.200,5.800,98.640,1.360,8.20,300,0.904,bilinear
efficientformerv2_s1.snap_dist_in1k,94.200,5.800,98.630,1.370,6.19,224,0.950,bicubic
resmlp_36_224.fb_in1k,94.190,5.810,98.660,1.340,44.69,224,0.875,bicubic
convnextv2_femto.fcmae_ft_in1k,94.190,5.810,98.610,1.390,5.23,288,0.950,bicubic
mixnet_xl.ra_in1k,94.180,5.820,98.330,1.670,11.90,224,0.875,bicubic
regnety_080.pycls_in1k,94.170,5.830,98.680,1.320,39.18,224,0.875,bicubic
inception_resnet_v2.tf_ens_adv_in1k,94.170,5.830,98.620,1.380,55.84,299,0.897,bicubic
levit_192.fb_dist_in1k,94.170,5.830,98.540,1.460,10.95,224,0.900,bicubic
levit_conv_192.fb_dist_in1k,94.170,5.830,98.540,1.460,10.95,224,0.900,bicubic
resnet152c.gluon_in1k,94.160,5.840,98.640,1.360,60.21,224,0.875,bicubic
dpn98.mx_in1k,94.160,5.840,98.590,1.410,61.57,224,0.875,bicubic
vit_base_patch16_224.sam_in1k,94.150,5.850,98.670,1.330,86.57,224,0.900,bicubic
gmlp_s16_224.ra3_in1k,94.150,5.850,98.500,1.500,19.42,224,0.875,bicubic
regnetx_160.pycls_in1k,94.140,5.860,98.750,1.250,54.28,224,0.875,bicubic
regnety_064.pycls_in1k,94.140,5.860,98.710,1.290,30.58,224,0.875,bicubic
efficientnet_b2_pruned.in1k,94.140,5.860,98.520,1.480,8.31,260,0.890,bicubic
regnetx_016.tv2_in1k,94.130,5.870,98.750,1.250,9.19,224,0.965,bicubic
nf_regnet_b1.ra2_in1k,94.110,5.890,98.620,1.380,10.22,288,0.900,bicubic
tf_efficientnet_b2.in1k,94.110,5.890,98.450,1.550,9.11,260,0.890,bicubic
resnet33ts.ra2_in1k,94.100,5.900,98.650,1.350,19.68,288,1.000,bicubic
xcit_tiny_24_p16_224.fb_in1k,94.080,5.920,98.540,1.460,12.12,224,1.000,bicubic
resnet152.gluon_in1k,94.070,5.930,98.460,1.540,60.19,224,0.875,bicubic
dpn131.mx_in1k,94.050,5.950,98.710,1.290,79.25,224,0.875,bicubic
resnet101.a3_in1k,94.040,5.960,98.660,1.340,44.55,224,0.950,bicubic
coat_lite_mini.in1k,94.040,5.960,98.550,1.450,11.01,224,0.900,bicubic
eca_halonext26ts.c1_in1k,94.040,5.960,98.490,1.510,10.76,256,0.940,bicubic
resmlp_24_224.fb_in1k,94.030,5.970,98.330,1.670,30.02,224,0.875,bicubic
hrnet_w64.ms_in1k,94.020,5.980,98.590,1.410,128.06,224,0.875,bilinear
halonet26t.a1h_in1k,94.000,6.000,98.500,1.500,12.48,256,0.950,bicubic
dpn68b.ra_in1k,94.000,6.000,98.340,1.660,12.61,288,1.000,bicubic
fbnetv3_b.ra2_in1k,93.970,6.030,98.630,1.370,8.60,256,0.950,bilinear
resnet50.am_in1k,93.970,6.030,98.520,1.480,25.56,224,0.875,bicubic
dla102x2.in1k,93.970,6.030,98.490,1.510,41.28,224,0.875,bilinear
mobilevitv2_125.cvnets_in1k,93.960,6.040,98.550,1.450,7.48,256,0.888,bicubic
tf_efficientnetv2_b1.in1k,93.950,6.050,98.620,1.380,8.14,240,0.882,bicubic
convnext_femto.d1_in1k,93.930,6.070,98.520,1.480,5.22,288,0.950,bicubic
fbnetv3_d.ra2_in1k,93.920,6.080,98.740,1.260,10.31,256,0.950,bilinear
convnext_femto_ols.d1_in1k,93.920,6.080,98.620,1.380,5.23,288,0.950,bicubic
hrnet_w48.ms_in1k,93.920,6.080,98.610,1.390,77.47,224,0.875,bilinear
tf_efficientnet_cc_b1_8e.in1k,93.920,6.080,98.260,1.740,39.72,240,0.882,bicubic
regnetx_064.pycls_in1k,93.900,6.100,98.640,1.360,26.21,224,0.875,bicubic
rexnet_130.nav_in1k,93.900,6.100,98.400,1.600,7.56,224,0.875,bicubic
vit_small_patch16_224.augreg_in1k,93.890,6.110,98.440,1.560,22.05,224,0.900,bicubic
regnety_040.pycls_in1k,93.880,6.120,98.660,1.340,20.65,224,0.875,bicubic
repvgg_b2g4.rvgg_in1k,93.880,6.120,98.590,1.410,61.76,224,0.875,bilinear
regnetx_080.pycls_in1k,93.880,6.120,98.520,1.480,39.57,224,0.875,bicubic
efficientnet_em.ra2_in1k,93.840,6.160,98.820,1.180,6.90,240,0.882,bicubic
resnet32ts.ra2_in1k,93.830,6.170,98.650,1.350,17.96,288,1.000,bicubic
lambda_resnet26t.c1_in1k,93.830,6.170,98.640,1.360,10.96,256,0.940,bicubic
resnext101_32x8d.tv_in1k,93.820,6.180,98.580,1.420,88.79,224,0.875,bilinear
resnext50_32x4d.gluon_in1k,93.810,6.190,98.420,1.580,25.03,224,0.875,bicubic
pvt_v2_b1.in1k,93.800,6.200,98.660,1.340,14.01,224,0.900,bicubic
xception65.tf_in1k,93.790,6.210,98.370,1.630,39.92,299,0.903,bicubic
eca_botnext26ts_256.c1_in1k,93.780,6.220,98.500,1.500,10.59,256,0.950,bicubic
pit_xs_distilled_224.in1k,93.770,6.230,98.620,1.380,11.00,224,0.900,bicubic
cspresnet50.ra_in1k,93.750,6.250,98.640,1.360,21.62,256,0.887,bilinear
legacy_seresnext50_32x4d.in1k,93.750,6.250,98.580,1.420,27.56,224,0.875,bilinear
resnet50d.gluon_in1k,93.750,6.250,98.390,1.610,25.58,224,0.875,bicubic
resnet101.gluon_in1k,93.750,6.250,98.380,1.620,44.55,224,0.875,bicubic
wide_resnet101_2.tv_in1k,93.740,6.260,98.540,1.460,126.89,224,0.875,bilinear
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,93.740,6.260,98.070,1.930,119.42,256,0.900,bicubic
res2net101_26w_4s.in1k,93.720,6.280,98.320,1.680,45.21,224,0.875,bilinear
lambda_resnet26rpt_256.c1_in1k,93.710,6.290,98.520,1.480,10.99,256,0.940,bicubic
regnety_008_tv.tv2_in1k,93.690,6.310,98.490,1.510,6.43,224,0.965,bicubic
tf_efficientnet_b1.ap_in1k,93.680,6.320,98.360,1.640,7.79,240,0.882,bicubic
resnet101c.gluon_in1k,93.670,6.330,98.420,1.580,44.57,224,0.875,bicubic
resnext50_32x4d.a3_in1k,93.660,6.340,98.520,1.480,25.03,224,0.950,bicubic
vit_tiny_patch16_384.augreg_in21k_ft_in1k,93.650,6.350,98.590,1.410,5.79,384,1.000,bicubic
resnet34d.ra2_in1k,93.640,6.360,98.540,1.460,21.82,288,0.950,bicubic
resnet50s.gluon_in1k,93.640,6.360,98.460,1.540,25.68,224,0.875,bicubic
vit_base_patch32_384.augreg_in1k,93.640,6.360,98.400,1.600,88.30,384,1.000,bicubic
vit_base_patch16_224.augreg_in1k,93.630,6.370,98.240,1.760,86.57,224,0.900,bicubic
tf_efficientnet_b0.ns_jft_in1k,93.620,6.380,98.640,1.360,5.29,224,0.875,bicubic
cait_xxs24_224.fb_dist_in1k,93.610,6.390,98.460,1.540,11.96,224,1.000,bicubic
coat_tiny.in1k,93.580,6.420,98.420,1.580,5.50,224,0.900,bicubic
regnetx_040.pycls_in1k,93.560,6.440,98.530,1.470,22.12,224,0.875,bicubic
visformer_tiny.in1k,93.560,6.440,98.490,1.510,10.32,224,0.900,bicubic
seresnext26t_32x4d.bt_in1k,93.560,6.440,98.390,1.610,16.81,288,0.950,bicubic
hrnet_w44.ms_in1k,93.550,6.450,98.700,1.300,67.06,224,0.875,bilinear
hrnet_w18.ms_aug_in1k,93.550,6.450,98.600,1.400,21.30,224,0.950,bilinear
hrnet_w32.ms_in1k,93.530,6.470,98.460,1.540,41.23,224,0.875,bilinear
xcit_nano_12_p8_384.fb_dist_in1k,93.520,6.480,98.530,1.470,3.05,384,1.000,bicubic
botnet26t_256.c1_in1k,93.510,6.490,98.300,1.700,12.49,256,0.950,bicubic
repvgg_b2.rvgg_in1k,93.500,6.500,98.730,1.270,89.02,224,0.875,bilinear
hrnet_w40.ms_in1k,93.500,6.500,98.570,1.430,57.56,224,0.875,bilinear
dla102x.in1k,93.500,6.500,98.500,1.500,26.31,224,0.875,bilinear
tf_efficientnet_b1.aa_in1k,93.490,6.510,98.360,1.640,7.79,240,0.882,bicubic
inception_v3.gluon_in1k,93.470,6.530,98.570,1.430,23.83,299,0.875,bicubic
resnet50d.a3_in1k,93.470,6.530,98.450,1.550,25.58,224,0.950,bicubic
legacy_xception.tf_in1k,93.460,6.540,98.530,1.470,22.86,299,0.897,bicubic
seresnext26d_32x4d.bt_in1k,93.440,6.560,98.330,1.670,16.81,288,0.950,bicubic
mixnet_l.ft_in1k,93.430,6.570,98.220,1.780,7.33,224,0.875,bicubic
xception41.tf_in1k,93.420,6.580,98.430,1.570,26.97,299,0.903,bicubic
regnety_032.pycls_in1k,93.410,6.590,98.640,1.360,19.44,224,0.875,bicubic
xcit_tiny_12_p16_224.fb_dist_in1k,93.410,6.590,98.510,1.490,6.72,224,1.000,bicubic
res2net50_26w_6s.in1k,93.400,6.600,98.280,1.720,37.05,224,0.875,bilinear
res2net50_26w_8s.in1k,93.390,6.610,98.170,1.830,48.40,224,0.875,bilinear
legacy_seresnet152.in1k,93.370,6.630,98.340,1.660,66.82,224,0.875,bilinear
dla169.in1k,93.360,6.640,98.610,1.390,53.39,224,0.875,bilinear
levit_conv_128.fb_dist_in1k,93.350,6.650,98.370,1.630,9.21,224,0.900,bicubic
cs3darknet_m.c2ns_in1k,93.340,6.660,98.600,1.400,9.31,288,0.950,bicubic
levit_128.fb_dist_in1k,93.340,6.660,98.370,1.630,9.21,224,0.900,bicubic
repvgg_b1.rvgg_in1k,93.330,6.670,98.520,1.480,57.42,224,0.875,bilinear
resnest26d.gluon_in1k,93.320,6.680,98.620,1.380,17.07,224,0.875,bilinear
resnet152.tv_in1k,93.320,6.680,98.380,1.620,60.19,224,0.875,bilinear
bat_resnext26ts.ch_in1k,93.320,6.680,98.360,1.640,10.73,256,0.900,bicubic
inception_v3.tf_in1k,93.320,6.680,98.040,1.960,23.83,299,0.875,bicubic
tf_mixnet_l.in1k,93.320,6.680,98.030,1.970,7.33,224,0.875,bicubic
legacy_seresnet101.in1k,93.310,6.690,98.520,1.480,49.33,224,0.875,bilinear
SelecSls60b.in1k,93.310,6.690,98.290,1.710,32.77,224,0.875,bicubic
mobilevitv2_100.cvnets_in1k,93.290,6.710,98.280,1.720,4.90,256,0.888,bicubic
efficientnet_b1.ft_in1k,93.250,6.750,98.290,1.710,7.79,256,1.000,bicubic
coat_lite_tiny.in1k,93.240,6.760,98.260,1.740,5.72,224,0.900,bicubic
resnet26t.ra2_in1k,93.200,6.800,98.490,1.510,16.01,320,1.000,bicubic
hrnet_w30.ms_in1k,93.200,6.800,98.410,1.590,37.71,224,0.875,bilinear
dla60_res2next.in1k,93.190,6.810,98.400,1.600,17.03,224,0.875,bilinear
mobilevit_s.cvnets_in1k,93.180,6.820,98.440,1.560,5.58,256,0.900,bicubic
dla60_res2net.in1k,93.180,6.820,98.430,1.570,20.85,224,0.875,bilinear
efficientnet_es.ra_in1k,93.160,6.840,98.410,1.590,5.44,224,0.875,bicubic
wide_resnet50_2.tv_in1k,93.160,6.840,98.370,1.630,68.88,224,0.875,bilinear
gcresnext26ts.ch_in1k,93.160,6.840,98.320,1.680,10.48,288,1.000,bicubic
ese_vovnet19b_dw.ra_in1k,93.150,6.850,98.250,1.750,6.54,288,0.950,bicubic
regnetx_032.pycls_in1k,93.110,6.890,98.390,1.610,15.30,224,0.875,bicubic
tf_efficientnetv2_b0.in1k,93.110,6.890,98.390,1.610,7.14,224,0.875,bicubic
resnet34.a1_in1k,93.100,6.900,98.330,1.670,21.80,288,1.000,bicubic
pit_xs_224.in1k,93.100,6.900,98.320,1.680,10.62,224,0.900,bicubic
convnext_atto_ols.a2_in1k,93.090,6.910,98.470,1.530,3.70,288,0.950,bicubic
tf_efficientnet_b1.in1k,93.090,6.910,98.300,1.700,7.79,240,0.882,bicubic
dla60x.in1k,93.080,6.920,98.500,1.500,17.35,224,0.875,bilinear
dla102.in1k,93.060,6.940,98.550,1.450,33.27,224,0.875,bilinear
eca_resnext26ts.ch_in1k,93.060,6.940,98.400,1.600,10.30,288,1.000,bicubic
regnety_016.pycls_in1k,93.040,6.960,98.370,1.630,11.20,224,0.875,bicubic
resnet50c.gluon_in1k,93.030,6.970,98.400,1.600,25.58,224,0.875,bicubic
SelecSls60.in1k,93.020,6.980,98.300,1.700,30.67,224,0.875,bicubic
rexnet_100.nav_in1k,93.020,6.980,98.190,1.810,4.80,224,0.875,bicubic
repvgg_b1g4.rvgg_in1k,93.000,7.000,98.430,1.570,39.97,224,0.875,bilinear
cs3darknet_focus_m.c2ns_in1k,92.970,7.030,98.390,1.610,9.30,288,0.950,bicubic
hardcorenas_f.miil_green_in1k,92.970,7.030,98.160,1.840,8.20,224,0.875,bilinear
convnextv2_atto.fcmae_ft_in1k,92.970,7.030,98.060,1.940,3.71,288,0.950,bicubic
seresnext26ts.ch_in1k,92.960,7.040,98.410,1.590,10.39,288,1.000,bicubic
poolformerv2_s12.sail_in1k,92.960,7.040,98.360,1.640,11.89,224,1.000,bicubic
legacy_seresnet50.in1k,92.960,7.040,98.180,1.820,28.09,224,0.875,bilinear
tf_efficientnet_em.in1k,92.920,7.080,98.210,1.790,6.90,240,0.882,bicubic
crossvit_9_dagger_240.in1k,92.900,7.100,98.240,1.760,8.78,240,0.875,bicubic
inception_v3.tf_adv_in1k,92.900,7.100,98.140,1.860,23.83,299,0.875,bicubic
res2next50.in1k,92.840,7.160,98.180,1.820,24.67,224,0.875,bilinear
tf_efficientnet_cc_b0_8e.in1k,92.840,7.160,98.180,1.820,24.01,224,0.875,bicubic
gmixer_24_224.ra3_in1k,92.830,7.170,97.880,2.120,24.72,224,0.875,bicubic
resmlp_12_224.fb_distilled_in1k,92.820,7.180,98.140,1.860,15.35,224,0.875,bicubic
resnet101.tv_in1k,92.810,7.190,98.250,1.750,44.55,224,0.875,bilinear
dpn68b.mx_in1k,92.790,7.210,98.150,1.850,12.61,224,0.875,bicubic
convnext_atto.d2_in1k,92.790,7.210,98.080,1.920,3.70,288,0.950,bicubic
efficientnet_b1_pruned.in1k,92.780,7.220,98.040,1.960,6.33,240,0.882,bicubic
res2net50_14w_8s.in1k,92.770,7.230,98.170,1.830,25.06,224,0.875,bilinear
resnext50_32x4d.tv_in1k,92.750,7.250,98.270,1.730,25.03,224,0.875,bilinear
inception_v3.tv_in1k,92.750,7.250,97.970,2.030,23.83,299,0.875,bicubic
densenet201.tv_in1k,92.740,7.260,98.230,1.770,20.01,224,0.875,bicubic
resnet50.a3_in1k,92.720,7.280,98.170,1.830,25.56,224,0.950,bicubic
resnet34.a2_in1k,92.720,7.280,98.010,1.990,21.80,288,1.000,bicubic
efficientnet_b0.ra_in1k,92.690,7.310,98.070,1.930,5.29,224,0.875,bicubic
tf_efficientnet_lite2.in1k,92.660,7.340,98.230,1.770,6.09,260,0.890,bicubic
legacy_seresnext26_32x4d.in1k,92.640,7.360,98.120,1.880,16.79,224,0.875,bicubic
densenetblur121d.ra_in1k,92.620,7.380,98.260,1.740,8.00,288,0.950,bicubic
tf_efficientnet_lite1.in1k,92.620,7.380,98.060,1.940,5.42,240,0.882,bicubic
poolformer_s12.sail_in1k,92.610,7.390,98.180,1.820,11.92,224,0.900,bicubic
tf_efficientnet_cc_b0_4e.in1k,92.590,7.410,98.080,1.920,13.31,224,0.875,bicubic
hardcorenas_e.miil_green_in1k,92.580,7.420,98.100,1.900,8.07,224,0.875,bilinear
regnetx_008.tv2_in1k,92.550,7.450,98.180,1.820,7.26,224,0.965,bicubic
res2net50_48w_2s.in1k,92.550,7.450,98.080,1.920,25.29,224,0.875,bilinear
resnet50.gluon_in1k,92.540,7.460,98.170,1.830,25.56,224,0.875,bicubic
densenet121.ra_in1k,92.520,7.480,98.220,1.780,7.98,288,0.950,bicubic
resnet26d.bt_in1k,92.520,7.480,98.210,1.790,16.01,288,0.950,bicubic
xcit_tiny_12_p16_224.fb_in1k,92.510,7.490,98.240,1.760,6.72,224,1.000,bicubic
res2net50_26w_4s.in1k,92.490,7.510,98.030,1.970,25.70,224,0.875,bilinear
densenet161.tv_in1k,92.480,7.520,98.300,1.700,28.68,224,0.875,bicubic
tinynet_a.in1k,92.440,7.560,98.080,1.920,6.19,192,0.875,bicubic
hardcorenas_d.miil_green_in1k,92.410,7.590,98.070,1.930,7.50,224,0.875,bilinear
mixnet_m.ft_in1k,92.410,7.590,97.870,2.130,5.01,224,0.875,bicubic
convmixer_1024_20_ks9_p14.in1k,92.400,7.600,98.270,1.730,24.38,224,0.960,bicubic
resnet34.bt_in1k,92.400,7.600,98.150,1.850,21.80,288,0.950,bicubic
mobilenetv2_120d.ra_in1k,92.400,7.600,98.060,1.940,5.83,224,0.875,bicubic
skresnet34.ra_in1k,92.380,7.620,98.150,1.850,22.28,224,0.875,bicubic
hrnet_w18.ms_in1k,92.320,7.680,98.270,1.730,21.30,224,0.875,bilinear
tf_mixnet_m.in1k,92.300,7.700,97.890,2.110,5.01,224,0.875,bicubic
SelecSls42b.in1k,92.290,7.710,98.110,1.890,32.46,224,0.875,bicubic
mobilenetv3_large_100.miil_in21k_ft_in1k,92.260,7.740,97.630,2.370,5.48,224,0.875,bilinear
tf_efficientnet_b0.aa_in1k,92.250,7.750,98.000,2.000,5.29,224,0.875,bicubic
resmlp_12_224.fb_in1k,92.220,7.780,98.150,1.850,15.35,224,0.875,bicubic
dla60.in1k,92.200,7.800,98.110,1.890,22.04,224,0.875,bilinear
tf_efficientnet_b0.ap_in1k,92.200,7.800,98.020,1.980,5.29,224,0.875,bicubic
regnetx_016.pycls_in1k,92.180,7.820,98.200,1.800,9.19,224,0.875,bicubic
gernet_s.idstcv_in1k,92.130,7.870,98.190,1.810,8.17,224,0.875,bilinear
resnext26ts.ra2_in1k,92.130,7.870,98.030,1.970,10.30,288,1.000,bicubic
xcit_nano_12_p8_224.fb_dist_in1k,92.090,7.910,98.160,1.840,3.05,224,1.000,bicubic
seresnet50.a3_in1k,92.070,7.930,98.040,1.960,28.09,224,0.950,bicubic
tf_efficientnet_b0.in1k,92.070,7.930,97.920,2.080,5.29,224,0.875,bicubic
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,92.040,7.960,98.290,1.710,6.36,384,1.000,bicubic
vit_small_patch32_224.augreg_in21k_ft_in1k,92.040,7.960,98.230,1.770,22.88,224,0.900,bicubic
hardcorenas_c.miil_green_in1k,92.020,7.980,97.840,2.160,5.52,224,0.875,bilinear
resnet26.bt_in1k,91.990,8.010,98.220,1.780,16.00,288,0.950,bicubic
dpn68.mx_in1k,91.990,8.010,98.020,1.980,12.61,224,0.875,bicubic
efficientformerv2_s0.snap_dist_in1k,91.970,8.030,97.890,2.110,3.60,224,0.950,bicubic
tf_efficientnet_es.in1k,91.970,8.030,97.880,2.120,5.44,224,0.875,bicubic
levit_128s.fb_dist_in1k,91.960,8.040,98.060,1.940,7.78,224,0.900,bicubic
levit_conv_128s.fb_dist_in1k,91.960,8.040,98.060,1.940,7.78,224,0.900,bicubic
repvgg_a2.rvgg_in1k,91.940,8.060,98.140,1.860,28.21,224,0.875,bilinear
densenet169.tv_in1k,91.940,8.060,98.100,1.900,14.15,224,0.875,bicubic
mixer_b16_224.goog_in21k_ft_in1k,91.880,8.120,97.260,2.740,59.88,224,0.875,bicubic
resnet50.tv_in1k,91.870,8.130,98.040,1.960,25.56,224,0.875,bilinear
mobilenetv2_140.ra_in1k,91.840,8.160,97.860,2.140,6.11,224,0.875,bicubic
xcit_nano_12_p16_384.fb_dist_in1k,91.830,8.170,98.020,1.980,3.05,384,1.000,bicubic
mixnet_s.ft_in1k,91.830,8.170,97.690,2.310,4.13,224,0.875,bicubic
vit_tiny_patch16_224.augreg_in21k_ft_in1k,91.760,8.240,98.040,1.960,5.72,224,0.900,bicubic
mobilevitv2_075.cvnets_in1k,91.760,8.240,97.860,2.140,2.87,256,0.888,bicubic
hardcorenas_b.miil_green_in1k,91.760,8.240,97.770,2.230,5.18,224,0.875,bilinear
regnety_008.pycls_in1k,91.730,8.270,98.180,1.820,6.26,224,0.875,bicubic
resnest14d.gluon_in1k,91.720,8.280,97.870,2.130,10.61,224,0.875,bilinear
edgenext_x_small.in1k,91.710,8.290,97.600,2.400,2.34,288,1.000,bicubic
regnety_004.tv2_in1k,91.580,8.420,97.890,2.110,4.34,224,0.965,bicubic
tf_mixnet_s.in1k,91.520,8.480,97.620,2.380,4.13,224,0.875,bicubic
repvgg_b0.rvgg_in1k,91.400,8.600,98.000,2.000,15.82,224,0.875,bilinear
regnety_006.pycls_in1k,91.390,8.610,97.700,2.300,6.06,224,0.875,bicubic
hardcorenas_a.miil_green_in1k,91.350,8.650,97.850,2.150,5.26,224,0.875,bilinear
mobilenetv3_large_100.ra_in1k,91.350,8.650,97.710,2.290,5.48,224,0.875,bicubic
semnasnet_100.rmsp_in1k,91.310,8.690,97.560,2.440,3.89,224,0.875,bicubic
tf_mobilenetv3_large_100.in1k,91.240,8.760,97.660,2.340,5.48,224,0.875,bilinear
mobilenetv3_rw.rmsp_in1k,91.210,8.790,97.660,2.340,5.48,224,0.875,bicubic
hrnet_w18_small_v2.ms_in1k,91.200,8.800,97.900,2.100,15.60,224,0.875,bilinear
vit_base_patch32_224.augreg_in1k,91.190,8.810,97.390,2.610,88.22,224,0.900,bicubic
efficientnet_es_pruned.in1k,91.170,8.830,97.740,2.260,5.44,224,0.875,bicubic
efficientnet_lite0.ra_in1k,91.120,8.880,97.630,2.370,4.65,224,0.875,bicubic
regnetx_008.pycls_in1k,91.050,8.950,97.720,2.280,7.26,224,0.875,bicubic
tf_efficientnet_lite0.in1k,91.050,8.950,97.580,2.420,4.65,224,0.875,bicubic
xcit_nano_12_p8_224.fb_in1k,91.010,8.990,97.780,2.220,3.05,224,1.000,bicubic
resnet34.gluon_in1k,90.970,9.030,97.630,2.370,21.80,224,0.875,bicubic
mobilenetv2_110d.ra_in1k,90.970,9.030,97.540,2.460,4.52,224,0.875,bicubic
tinynet_b.in1k,90.920,9.080,97.660,2.340,3.73,188,0.875,bicubic
legacy_seresnet34.in1k,90.900,9.100,97.580,2.420,21.96,224,0.875,bilinear
densenet121.tv_in1k,90.890,9.110,97.710,2.290,7.98,224,0.875,bicubic
mobilevit_xs.cvnets_in1k,90.800,9.200,97.930,2.070,2.32,256,0.900,bicubic
pit_ti_distilled_224.in1k,90.770,9.230,97.610,2.390,5.10,224,0.900,bicubic
dla34.in1k,90.760,9.240,97.650,2.350,15.74,224,0.875,bilinear
fbnetc_100.rmsp_in1k,90.730,9.270,97.210,2.790,5.57,224,0.875,bilinear
deit_tiny_distilled_patch16_224.fb_in1k,90.710,9.290,97.560,2.440,5.91,224,0.900,bicubic
resnet18.fb_swsl_ig1b_ft_in1k,90.700,9.300,97.700,2.300,11.69,224,0.875,bilinear
convit_tiny.fb_in1k,90.660,9.340,97.730,2.270,5.71,224,0.875,bicubic
crossvit_9_240.in1k,90.640,9.360,97.730,2.270,8.55,240,0.875,bicubic
regnetx_004_tv.tv2_in1k,90.640,9.360,97.590,2.410,5.50,224,0.965,bicubic
mnasnet_100.rmsp_in1k,90.500,9.500,97.470,2.530,4.38,224,0.875,bicubic
regnety_004.pycls_in1k,90.490,9.510,97.530,2.470,4.34,224,0.875,bicubic
regnetx_006.pycls_in1k,90.350,9.650,97.430,2.570,6.20,224,0.875,bicubic
spnasnet_100.rmsp_in1k,90.330,9.670,97.190,2.810,4.42,224,0.875,bilinear
resnet18d.ra2_in1k,90.280,9.720,97.560,2.440,11.71,288,0.950,bicubic
crossvit_tiny_240.in1k,90.230,9.770,97.590,2.410,7.01,240,0.875,bicubic
resnet18.fb_ssl_yfcc100m_ft_in1k,90.210,9.790,97.540,2.460,11.69,224,0.875,bilinear
semnasnet_075.rmsp_in1k,90.090,9.910,97.440,2.560,2.91,224,0.875,bicubic
vgg16_bn.tv_in1k,90.090,9.910,97.370,2.630,138.37,224,0.875,bilinear
vgg19_bn.tv_in1k,90.080,9.920,97.580,2.420,143.68,224,0.875,bilinear
ghostnet_100.in1k,90.030,9.970,97.370,2.630,5.18,224,0.875,bilinear
resnet34.tv_in1k,89.950,10.050,97.340,2.660,21.80,224,0.875,bilinear
pit_ti_224.in1k,89.940,10.060,97.450,2.550,4.85,224,0.900,bicubic
resnet34.a3_in1k,89.940,10.060,97.180,2.820,21.80,224,0.950,bicubic
vit_base_patch32_224.sam_in1k,89.740,10.260,97.000,3.000,88.22,224,0.900,bicubic
xcit_nano_12_p16_224.fb_dist_in1k,89.710,10.290,97.100,2.900,3.05,224,1.000,bicubic
resnet18.a1_in1k,89.680,10.320,97.100,2.900,11.69,288,1.000,bicubic
skresnet18.ra_in1k,89.660,10.340,97.230,2.770,11.96,224,0.875,bicubic
tf_mobilenetv3_large_075.in1k,89.660,10.340,97.190,2.810,3.99,224,0.875,bilinear
deit_tiny_patch16_224.fb_in1k,89.650,10.350,97.460,2.540,5.72,224,0.900,bicubic
mobilenetv2_100.ra_in1k,89.580,10.420,97.140,2.860,3.50,224,0.875,bicubic
resnet18.a2_in1k,89.570,10.430,96.960,3.040,11.69,288,1.000,bicubic
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,89.180,10.820,97.220,2.780,6.34,224,0.900,bicubic
hrnet_w18_small.ms_in1k,89.050,10.950,97.120,2.880,13.19,224,0.875,bilinear
vgg19.tv_in1k,89.050,10.950,96.870,3.130,143.67,224,0.875,bilinear
resnet14t.c3_in1k,89.000,11.000,96.730,3.270,10.08,224,0.950,bicubic
tf_mobilenetv3_large_minimal_100.in1k,88.950,11.050,96.860,3.140,3.92,224,0.875,bilinear
regnetx_004.pycls_in1k,88.890,11.110,97.110,2.890,5.16,224,0.875,bicubic
legacy_seresnet18.in1k,88.890,11.110,96.980,3.020,11.78,224,0.875,bicubic
edgenext_xx_small.in1k,88.890,11.110,96.700,3.300,1.33,288,1.000,bicubic
pvt_v2_b0.in1k,88.780,11.220,96.860,3.140,3.67,224,0.900,bicubic
vgg13_bn.tv_in1k,88.750,11.250,96.980,3.020,133.05,224,0.875,bilinear
lcnet_100.ra2_in1k,88.750,11.250,96.720,3.280,2.95,224,0.875,bicubic
xcit_nano_12_p16_224.fb_in1k,88.620,11.380,96.790,3.210,3.05,224,1.000,bicubic
vgg16.tv_in1k,88.560,11.440,96.800,3.200,138.36,224,0.875,bilinear
resnet18.gluon_in1k,88.370,11.630,96.670,3.330,11.69,224,0.875,bicubic
mobilevitv2_050.cvnets_in1k,88.190,11.810,96.990,3.010,1.37,256,0.888,bicubic
tinynet_c.in1k,87.790,12.210,96.370,3.630,2.46,184,0.875,bicubic
vgg11_bn.tv_in1k,87.500,12.500,96.820,3.180,132.87,224,0.875,bilinear
resnet18.tv_in1k,87.380,12.620,96.290,3.710,11.69,224,0.875,bilinear
regnety_002.pycls_in1k,87.370,12.630,96.610,3.390,3.16,224,0.875,bicubic
mobilevit_xxs.cvnets_in1k,87.150,12.850,96.100,3.900,1.27,256,0.900,bicubic
mixer_l16_224.goog_in21k_ft_in1k,87.150,12.850,93.520,6.480,208.20,224,0.875,bicubic
vgg13.tv_in1k,87.040,12.960,96.330,3.670,133.05,224,0.875,bilinear
vgg11.tv_in1k,86.580,13.420,96.280,3.720,132.86,224,0.875,bilinear
resnet18.a3_in1k,86.450,13.550,95.880,4.120,11.69,224,0.950,bicubic
dla60x_c.in1k,86.280,13.720,96.160,3.840,1.32,224,0.875,bilinear
resnet10t.c3_in1k,86.220,13.780,95.740,4.260,5.44,224,0.950,bicubic
regnetx_002.pycls_in1k,86.140,13.860,95.960,4.040,2.68,224,0.875,bicubic
lcnet_075.ra2_in1k,85.970,14.030,95.680,4.320,2.36,224,0.875,bicubic
mobilenetv3_small_100.lamb_in1k,85.220,14.780,95.650,4.350,2.54,224,0.875,bicubic
tf_mobilenetv3_small_100.in1k,85.190,14.810,95.770,4.230,2.54,224,0.875,bilinear
tinynet_d.in1k,84.720,15.280,95.170,4.830,2.34,152,0.875,bicubic
mnasnet_small.lamb_in1k,84.420,15.580,95.190,4.810,2.03,224,0.875,bicubic
dla46x_c.in1k,84.200,15.800,95.270,4.730,1.07,224,0.875,bilinear
mobilenetv2_050.lamb_in1k,83.900,16.100,94.730,5.270,1.97,224,0.875,bicubic
dla46_c.in1k,83.610,16.390,94.950,5.050,1.30,224,0.875,bilinear
tf_mobilenetv3_small_075.in1k,83.500,16.500,94.840,5.160,2.04,224,0.875,bilinear
mobilenetv3_small_075.lamb_in1k,83.030,16.970,94.100,5.900,2.04,224,0.875,bicubic
lcnet_050.ra2_in1k,81.780,18.220,93.740,6.260,1.88,224,0.875,bicubic
tf_mobilenetv3_small_minimal_100.in1k,81.400,18.600,93.680,6.320,2.04,224,0.875,bilinear
tinynet_e.in1k,78.920,21.080,92.540,7.460,2.04,106,0.875,bicubic
mobilenetv3_small_050.lamb_in1k,77.020,22.980,91.300,8.700,1.59,224,0.875,bicubic
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-a.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,88.227,11.773,97.093,2.907,305.08,448,1.000,bicubic,-10.623,-2.787,+1
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,87.920,12.080,96.920,3.080,305.08,448,1.000,bicubic,-11.010,-2.990,-1
eva_giant_patch14_560.m30m_ft_in22k_in1k,87.573,12.427,96.893,3.107,"1,014.45",560,1.000,bicubic,-11.257,-3.007,+1
eva02_large_patch14_448.mim_m38m_ft_in1k,87.107,12.893,96.280,3.720,305.08,448,1.000,bicubic,-11.623,-3.590,+5
eva02_large_patch14_448.mim_in22k_ft_in1k,86.227,13.773,95.787,4.213,305.08,448,1.000,bicubic,-12.613,-4.043,-2
eva_giant_patch14_336.clip_ft_in1k,85.307,14.693,95.720,4.280,"1,013.01",336,1.000,bicubic,-13.513,-4.090,-1
eva_giant_patch14_336.m30m_ft_in22k_in1k,85.147,14.853,96.360,3.640,"1,013.01",336,1.000,bicubic,-13.663,-3.540,-1
tf_efficientnet_l2.ns_jft_in1k,84.747,15.253,96.147,3.853,480.31,800,0.960,bicubic,-13.803,-3.673,+9
regnety_1280.swag_ft_in1k,83.907,16.093,96.200,3.800,644.81,384,1.000,bicubic,-14.543,-3.610,+18
eva_large_patch14_336.in22k_ft_in22k_in1k,83.840,16.160,95.333,4.667,304.53,336,1.000,bicubic,-14.900,-4.477,-3
convnextv2_huge.fcmae_ft_in22k_in1k_512,83.827,16.173,96.173,3.827,660.29,512,1.000,bicubic,-14.773,-3.697,+4
maxvit_xlarge_tf_512.in21k_ft_in1k,83.400,16.600,95.520,4.480,475.77,512,1.000,bicubic,-15.220,-4.270,+1
tf_efficientnet_l2.ns_jft_in1k_475,83.387,16.613,95.453,4.547,480.31,475,0.936,bicubic,-15.113,-4.327,+8
eva_large_patch14_336.in22k_ft_in1k,82.760,17.240,95.507,4.493,304.53,336,1.000,bicubic,-15.970,-4.283,-6
maxvit_large_tf_512.in21k_ft_in1k,81.733,18.267,95.027,4.973,212.33,512,1.000,bicubic,-16.887,-4.773,-1
beit_large_patch16_512.in22k_ft_in22k_in1k,81.600,18.400,94.880,5.120,305.67,512,1.000,bicubic,-16.960,-4.960,0
maxvit_base_tf_512.in21k_ft_in1k,81.360,18.640,94.467,5.533,119.88,512,1.000,bicubic,-17.260,-5.333,-5
eva_giant_patch14_224.clip_ft_in1k,81.213,18.787,94.333,5.667,"1,012.56",224,0.900,bicubic,-17.247,-5.417,+8
maxvit_xlarge_tf_384.in21k_ft_in1k,81.080,18.920,94.640,5.360,475.32,384,1.000,bicubic,-17.420,-5.190,+3
convnextv2_huge.fcmae_ft_in22k_in1k_384,79.893,20.107,94.640,5.360,660.29,384,1.000,bicubic,-18.777,-5.220,-10
deit3_large_patch16_384.fb_in22k_ft_in1k,79.187,20.813,93.613,6.387,304.76,384,1.000,bicubic,-19.273,-6.147,+4
beit_large_patch16_384.in22k_ft_in22k_in1k,79.107,20.893,94.267,5.733,305.00,384,1.000,bicubic,-19.413,-5.553,-3
caformer_b36.sail_in22k_ft_in1k_384,78.360,21.640,93.453,6.547,98.75,384,1.000,bicubic,-20.090,-6.417,+5
maxvit_large_tf_384.in21k_ft_in1k,78.013,21.987,93.267,6.733,212.03,384,1.000,bicubic,-20.477,-6.483,-1
eva02_base_patch14_448.mim_in22k_ft_in1k,77.560,22.440,93.120,6.880,87.12,448,1.000,bicubic,-20.880,-6.680,+4
vit_large_patch14_clip_336.openai_ft_in12k_in1k,77.333,22.667,93.613,6.387,304.53,336,1.000,bicubic,-20.937,-6.157,+14
convnext_xxlarge.clip_laion2b_soup_ft_in1k,77.120,22.880,94.320,5.680,846.47,256,1.000,bicubic,-21.320,-5.500,+3
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,76.893,23.107,92.707,7.293,87.12,448,1.000,bicubic,-21.747,-7.093,-17
maxvit_base_tf_384.in21k_ft_in1k,76.853,23.147,92.600,7.400,119.65,384,1.000,bicubic,-21.667,-7.150,-9
beitv2_large_patch16_224.in1k_ft_in22k_in1k,76.773,23.227,93.173,6.827,304.43,224,0.950,bicubic,-21.767,-6.587,-12
eva_large_patch14_196.in22k_ft_in22k_in1k,75.533,24.467,91.760,8.240,304.14,196,1.000,bicubic,-22.897,-8.010,0
regnety_1280.swag_lc_in1k,74.587,25.413,91.680,8.320,644.81,224,0.965,bicubic,-23.063,-7.890,+77
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,74.267,25.733,90.827,9.173,116.14,384,1.000,bicubic,-23.903,-8.933,+18
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,74.240,25.760,92.253,7.747,632.46,336,1.000,bicubic,-24.180,-7.557,-1
regnety_320.swag_ft_in1k,74.200,25.800,92.960,7.040,145.05,384,1.000,bicubic,-23.860,-6.800,+29
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,73.933,26.067,91.733,8.267,196.74,384,1.000,bicubic,-24.197,-7.977,+20
eva_large_patch14_196.in22k_ft_in1k,73.133,26.867,91.427,8.573,304.14,196,1.000,bicubic,-25.227,-8.393,-2
caformer_m36.sail_in22k_ft_in1k_384,72.973,27.027,90.600,9.400,56.20,384,1.000,bicubic,-25.177,-9.150,+17
vit_large_patch14_clip_224.openai_ft_in12k_in1k,72.293,27.707,90.880,9.120,304.20,224,1.000,bicubic,-25.927,-8.840,+7
convnextv2_large.fcmae_ft_in22k_in1k_384,72.067,27.933,91.013,8.987,197.96,384,1.000,bicubic,-26.333,-8.747,-6
vit_large_patch14_clip_224.openai_ft_in1k,71.813,28.187,91.453,8.547,304.20,224,1.000,bicubic,-26.357,-8.207,+12
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,71.800,28.200,90.227,9.773,304.53,336,1.000,bicubic,-26.540,-9.533,-5
convformer_b36.sail_in22k_ft_in1k_384,71.547,28.453,90.240,9.760,99.88,384,1.000,bicubic,-26.713,-9.590,-2
vit_large_patch16_384.augreg_in21k_ft_in1k,71.227,28.773,89.773,10.227,304.72,384,1.000,bicubic,-26.993,-9.947,+1
deit3_base_patch16_384.fb_in22k_ft_in1k,71.213,28.787,89.933,10.067,86.88,384,1.000,bicubic,-26.627,-9.737,+41
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,71.200,28.800,91.320,8.680,87.92,384,1.000,bicubic,-26.920,-8.420,+11
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,70.720,29.280,90.547,9.453,200.13,384,1.000,bicubic,-27.760,-9.233,-23
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,70.680,29.320,90.400,9.600,632.05,224,1.000,bicubic,-27.620,-9.360,-10
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,70.600,29.400,89.227,10.773,73.88,384,1.000,bicubic,-27.470,-10.533,+14
caformer_s36.sail_in22k_ft_in1k_384,70.333,29.667,90.067,9.933,39.30,384,1.000,bicubic,-27.637,-9.653,+21
deit3_huge_patch14_224.fb_in22k_ft_in1k,70.253,29.747,90.707,9.293,632.13,224,1.000,bicubic,-27.917,-9.053,+1
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,69.880,30.120,90.493,9.507,200.13,320,1.000,bicubic,-28.400,-9.277,-13
swin_large_patch4_window12_384.ms_in22k_ft_in1k,69.640,30.360,89.547,10.453,196.74,384,1.000,bicubic,-28.410,-10.143,+13
volo_d5_512.sail_in1k,69.587,30.413,90.427,9.573,296.09,512,1.150,bicubic,-28.183,-9.243,+44
convnext_xlarge.fb_in22k_ft_in1k_384,69.320,30.680,89.307,10.693,350.20,384,1.000,bicubic,-29.100,-10.503,-23
caformer_b36.sail_in22k_ft_in1k,69.133,30.867,89.600,10.400,98.75,224,1.000,bicubic,-29.027,-10.180,-2
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,69.067,30.933,89.880,10.120,116.09,384,1.000,bicubic,-29.193,-9.900,-15
deit3_large_patch16_224.fb_in22k_ft_in1k,68.693,31.307,89.973,10.027,304.37,224,1.000,bicubic,-29.477,-9.757,-8
beit_large_patch16_224.in22k_ft_in22k_in1k,68.467,31.533,89.573,10.427,304.43,224,0.900,bicubic,-29.713,-10.187,-10
regnety_160.swag_ft_in1k,68.093,31.907,90.720,9.280,83.59,384,1.000,bicubic,-29.687,-8.880,+34
convnextv2_large.fcmae_ft_in22k_in1k,68.093,31.907,89.720,10.280,197.96,288,1.000,bicubic,-29.997,-10.050,0
volo_d5_448.sail_in1k,68.080,31.920,89.720,10.280,295.91,448,1.150,bicubic,-29.670,-9.830,+38
maxvit_base_tf_512.in1k,67.933,32.067,88.493,11.507,119.88,512,1.000,bicubic,-29.797,-11.117,+39
maxvit_large_tf_512.in1k,67.880,32.120,87.653,12.347,212.33,512,1.000,bicubic,-29.950,-11.907,+25
tf_efficientnetv2_xl.in21k_ft_in1k,67.800,32.200,87.347,12.653,208.12,512,1.000,bicubic,-30.100,-12.223,+15
beitv2_large_patch16_224.in1k_ft_in1k,67.640,32.360,88.667,11.333,304.43,224,0.950,bicubic,-30.270,-10.993,+10
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,67.320,32.680,88.000,12.000,196.74,256,0.900,bicubic,-30.530,-11.640,+17
vit_large_patch14_clip_336.laion2b_ft_in1k,67.093,32.907,89.493,10.507,304.53,336,1.000,bicubic,-31.127,-10.307,-21
tf_efficientnet_b7.ns_jft_in1k,67.027,32.973,88.667,11.333,66.35,600,0.949,bicubic,-30.893,-11.053,+6
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,67.013,32.987,87.973,12.027,304.20,224,1.000,bicubic,-31.057,-11.747,-8
convnext_xlarge.fb_in22k_ft_in1k,66.960,33.040,88.947,11.053,350.20,288,1.000,bicubic,-31.150,-10.833,-12
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,66.880,33.120,89.280,10.720,200.13,384,1.000,bicubic,-31.370,-10.480,-29
convformer_m36.sail_in22k_ft_in1k_384,66.840,33.160,87.813,12.187,57.05,384,1.000,bicubic,-31.200,-11.937,-5
convnextv2_base.fcmae_ft_in22k_in1k_384,66.787,33.213,88.893,11.107,88.72,384,1.000,bicubic,-31.563,-10.877,-38
volo_d4_448.sail_in1k,66.667,33.333,88.987,11.013,193.41,448,1.150,bicubic,-31.003,-10.623,+31
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,65.987,34.013,87.880,12.120,93.59,320,1.000,bicubic,-31.983,-11.820,-4
beit_base_patch16_384.in22k_ft_in22k_in1k,65.920,34.080,88.507,11.493,86.74,384,1.000,bicubic,-31.900,-11.193,+13
regnety_320.swag_lc_in1k,65.587,34.413,88.080,11.920,145.05,224,0.965,bicubic,-31.573,-11.590,+105
convnext_large.fb_in22k_ft_in1k_384,65.533,34.467,87.467,12.533,197.77,384,1.000,bicubic,-32.707,-12.283,-35
vit_huge_patch14_clip_224.laion2b_ft_in1k,65.493,34.507,87.720,12.280,632.05,224,1.000,bicubic,-32.527,-12.000,-11
volo_d3_448.sail_in1k,65.400,34.600,87.573,12.427,86.63,448,1.000,bicubic,-32.150,-11.987,+43
convnext_large.fb_in22k_ft_in1k,65.000,35.000,87.947,12.053,197.77,288,1.000,bicubic,-33.120,-11.833,-24
tf_efficientnetv2_l.in21k_ft_in1k,64.947,35.053,87.840,12.160,118.52,480,1.000,bicubic,-32.853,-11.820,+10
swin_base_patch4_window12_384.ms_in22k_ft_in1k,64.467,35.533,87.520,12.480,87.90,384,1.000,bicubic,-33.433,-12.150,-6
convnextv2_huge.fcmae_ft_in1k,64.440,35.560,87.080,12.920,660.29,288,1.000,bicubic,-33.460,-12.630,-6
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,64.187,35.813,85.520,14.480,116.14,224,0.950,bicubic,-33.623,-14.130,+6
vit_base_patch16_384.augreg_in21k_ft_in1k,63.693,36.307,86.693,13.307,86.86,384,1.000,bicubic,-34.147,-12.977,+1
maxvit_large_tf_384.in1k,63.507,36.493,85.107,14.893,212.03,384,1.000,bicubic,-34.063,-14.563,+35
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,63.307,36.693,87.507,12.493,87.92,256,0.900,bicubic,-34.353,-12.103,+18
convnextv2_base.fcmae_ft_in22k_in1k,62.893,37.107,87.667,12.333,88.72,288,1.000,bicubic,-35.167,-12.193,-25
maxvit_small_tf_512.in1k,62.853,37.147,86.307,13.693,69.13,512,1.000,bicubic,-34.897,-13.313,+10
maxvit_base_tf_384.in1k,62.613,37.387,85.187,14.813,119.65,384,1.000,bicubic,-34.957,-14.343,+30
convformer_b36.sail_in22k_ft_in1k,62.600,37.400,85.467,14.533,99.88,224,1.000,bicubic,-35.340,-14.293,-19
cait_m48_448.fb_dist_in1k,62.373,37.627,86.453,13.547,356.46,448,1.000,bicubic,-35.107,-13.147,+41
convnext_base.fb_in22k_ft_in1k_384,62.360,37.640,86.240,13.760,88.59,384,1.000,bicubic,-35.720,-13.410,-34
tf_efficientnet_b6.ns_jft_in1k,62.227,37.773,85.160,14.840,43.04,528,0.942,bicubic,-35.393,-14.390,+18
caformer_b36.sail_in1k_384,62.160,37.840,84.493,15.507,98.75,384,1.000,bicubic,-35.340,-15.147,+35
vit_base_patch8_224.augreg2_in21k_ft_in1k,62.027,37.973,85.840,14.160,86.58,224,0.900,bicubic,-35.663,-13.810,+7
convformer_s36.sail_in22k_ft_in1k_384,61.920,38.080,85.960,14.040,40.01,384,1.000,bicubic,-35.930,-13.690,-14
beitv2_base_patch16_224.in1k_ft_in22k_in1k,61.667,38.333,85.480,14.520,86.53,224,0.900,bicubic,-36.023,-14.200,+4
vit_large_r50_s32_384.augreg_in21k_ft_in1k,61.493,38.507,84.027,15.973,329.09,384,1.000,bicubic,-36.367,-15.643,-18
seresnextaa101d_32x8d.sw_in12k_ft_in1k,61.080,38.920,85.920,14.080,93.59,288,1.000,bicubic,-36.830,-13.740,-26
caformer_m36.sail_in22k_ft_in1k,61.080,38.920,84.893,15.107,56.20,224,1.000,bicubic,-36.760,-14.787,-15
swin_large_patch4_window7_224.ms_in22k_ft_in1k,61.000,39.000,85.867,14.133,196.53,224,0.900,bicubic,-36.650,-13.703,+7
convnext_base.fb_in22k_ft_in1k,60.893,39.107,86.147,13.853,88.59,288,1.000,bicubic,-36.967,-13.533,-23
resnetv2_152x4_bit.goog_in21k_ft_in1k,60.773,39.227,83.560,16.440,936.53,480,1.000,bilinear,-36.717,-16.050,+27
convnext_small.in12k_ft_in1k_384,60.733,39.267,84.960,15.040,50.22,384,1.000,bicubic,-37.067,-14.810,-13
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,60.547,39.453,86.293,13.707,200.13,256,1.000,bicubic,-37.403,-13.417,-35
deit3_large_patch16_384.fb_in1k,60.533,39.467,85.720,14.280,304.76,384,1.000,bicubic,-36.887,-13.850,+34
caformer_m36.sail_in1k_384,60.533,39.467,84.760,15.240,56.20,384,1.000,bicubic,-36.907,-14.880,+33
tf_efficientnet_b5.ns_jft_in1k,60.320,39.680,84.453,15.547,30.39,456,0.934,bicubic,-37.180,-15.127,+20
vit_base_patch16_clip_384.openai_ft_in12k_in1k,60.240,39.760,84.613,15.387,86.86,384,0.950,bicubic,-37.950,-15.047,-64
regnety_160.swag_lc_in1k,60.133,39.867,85.773,14.227,83.59,224,0.965,bicubic,-36.687,-13.877,+127
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,59.973,40.027,83.760,16.240,73.88,224,0.950,bicubic,-37.677,-15.880,-4
vit_large_patch14_clip_224.laion2b_ft_in1k,59.947,40.053,85.667,14.333,304.20,224,1.000,bicubic,-37.943,-13.983,-34
xcit_large_24_p8_384.fb_dist_in1k,59.947,40.053,85.467,14.533,188.93,384,1.000,bicubic,-37.573,-14.073,+13
coatnet_2_rw_224.sw_in12k_ft_in1k,59.520,40.480,84.227,15.773,73.87,224,0.950,bicubic,-38.000,-15.373,+11
tf_efficientnetv2_m.in21k_ft_in1k,59.413,40.587,84.560,15.440,54.14,480,1.000,bicubic,-38.407,-15.040,-27
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,59.160,40.840,84.480,15.520,116.09,224,0.950,bicubic,-38.600,-15.220,-20
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,59.147,40.853,83.280,16.720,86.86,384,1.000,bicubic,-38.843,-16.380,-50
vit_base_patch8_224.augreg_in21k_ft_in1k,58.973,41.027,82.733,17.267,86.58,224,0.900,bicubic,-38.597,-16.857,0
maxvit_tiny_tf_512.in1k,58.800,41.200,84.573,15.427,31.05,512,1.000,bicubic,-38.780,-14.987,-2
caformer_s18.sail_in22k_ft_in1k_384,58.640,41.360,85.347,14.653,26.34,384,1.000,bicubic,-38.780,-14.273,+22
volo_d2_384.sail_in1k,58.600,41.400,84.253,15.747,58.87,384,1.000,bicubic,-38.720,-15.347,+34
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,58.253,41.747,85.480,14.520,88.59,384,1.000,bicubic,-39.787,-14.210,-58
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,58.040,41.960,80.640,19.360,468.53,224,0.875,bilinear,-39.330,-19.040,+26
cait_m36_384.fb_dist_in1k,57.853,42.147,84.840,15.160,271.22,384,1.000,bicubic,-39.547,-14.670,+22
dm_nfnet_f5.dm_in1k,57.640,42.360,82.280,17.720,377.21,544,0.954,bicubic,-40.140,-17.480,-31
dm_nfnet_f6.dm_in1k,57.560,42.440,82.360,17.640,438.36,576,0.956,bicubic,-40.220,-17.290,-33
caformer_s36.sail_in1k_384,57.347,42.653,82.787,17.213,39.30,384,1.000,bicubic,-40.043,-16.753,+20
deit3_base_patch16_224.fb_in22k_ft_in1k,57.267,42.733,83.520,16.480,86.59,224,1.000,bicubic,-40.213,-16.030,+3
volo_d5_224.sail_in1k,57.107,42.893,82.733,17.267,295.46,224,0.960,bicubic,-40.273,-16.837,+19
deit3_small_patch16_384.fb_in22k_ft_in1k,57.093,42.907,83.080,16.920,22.21,384,1.000,bicubic,-40.037,-16.420,+54
convnext_base.clip_laiona_augreg_ft_in1k_384,57.080,42.920,84.773,15.227,88.59,384,1.000,bicubic,-40.540,-14.807,-19
convnextv2_large.fcmae_ft_in1k,56.880,43.120,83.467,16.533,197.96,288,1.000,bicubic,-40.780,-16.253,-27
regnety_160.lion_in12k_ft_in1k,56.733,43.267,83.440,16.560,83.59,288,1.000,bicubic,-40.717,-16.160,+2
dm_nfnet_f4.dm_in1k,56.707,43.293,81.760,18.240,316.07,512,0.951,bicubic,-40.933,-17.780,-25
xcit_medium_24_p8_384.fb_dist_in1k,56.693,43.307,83.453,16.547,84.32,384,1.000,bicubic,-40.587,-16.067,+27
maxvit_small_tf_384.in1k,56.587,43.413,82.280,17.720,69.02,384,1.000,bicubic,-40.843,-17.230,+4
regnety_160.sw_in12k_ft_in1k,56.253,43.747,82.853,17.147,83.59,288,1.000,bicubic,-41.197,-16.737,-1
convformer_m36.sail_in22k_ft_in1k,55.867,44.133,81.813,18.187,57.05,224,1.000,bicubic,-41.733,-17.807,-23
caformer_s36.sail_in22k_ft_in1k,55.813,44.187,82.120,17.880,39.30,224,1.000,bicubic,-41.787,-17.600,-23
vit_large_patch16_224.augreg_in21k_ft_in1k,55.560,44.440,80.080,19.920,304.33,224,0.900,bicubic,-42.070,-19.510,-30
convformer_b36.sail_in1k_384,55.453,44.547,81.293,18.707,99.88,384,1.000,bicubic,-42.077,-18.227,-18
vit_base_patch16_clip_384.openai_ft_in1k,54.973,45.027,82.587,17.413,86.86,384,1.000,bicubic,-42.567,-17.073,-20
vit_base_r50_s16_384.orig_in21k_ft_in1k,54.627,45.373,81.213,18.787,98.95,384,1.000,bicubic,-42.563,-18.327,+34
cait_s36_384.fb_dist_in1k,54.360,45.640,81.373,18.627,68.37,384,1.000,bicubic,-42.970,-18.167,+10
volo_d1_384.sail_in1k,54.333,45.667,81.000,19.000,26.78,384,1.000,bicubic,-42.577,-18.460,+79
deit3_huge_patch14_224.fb_in1k,54.320,45.680,82.093,17.907,632.13,224,0.900,bicubic,-42.580,-17.127,+80
xcit_small_24_p8_384.fb_dist_in1k,54.253,45.747,81.547,18.453,47.63,384,1.000,bicubic,-42.987,-18.003,+19
vit_base_patch16_clip_384.laion2b_ft_in1k,54.253,45.747,80.880,19.120,86.86,384,1.000,bicubic,-43.467,-18.750,-47
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,54.187,45.813,81.640,18.360,39.03,384,0.950,bicubic,-43.253,-17.960,-11
resnetv2_101x3_bit.goog_in21k_ft_in1k,54.027,45.973,81.027,18.973,387.93,448,1.000,bilinear,-42.953,-18.463,+62
resnetv2_152x2_bit.goog_in21k_ft_in1k,54.013,45.987,82.000,18.000,236.34,448,1.000,bilinear,-42.987,-17.590,+57
beitv2_base_patch16_224.in1k_ft_in1k,53.813,46.187,81.853,18.147,86.53,224,0.900,bicubic,-43.357,-17.617,+27
dm_nfnet_f3.dm_in1k,53.773,46.227,79.840,20.160,254.92,416,0.940,bicubic,-43.697,-19.720,-20
convformer_m36.sail_in1k_384,53.547,46.453,80.733,19.267,57.05,384,1.000,bicubic,-43.863,-18.867,-9
deit3_base_patch16_384.fb_in1k,53.413,46.587,80.533,19.467,86.88,384,1.000,bicubic,-43.617,-18.857,+50
convnext_small.in12k_ft_in1k,53.240,46.760,81.400,18.600,50.22,288,1.000,bicubic,-44.110,-18.180,-4
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,53.067,46.933,76.907,23.093,194.03,224,0.875,bilinear,-43.743,-22.693,+81
volo_d4_224.sail_in1k,52.987,47.013,80.427,19.573,192.96,224,0.960,bicubic,-44.293,-19.113,+3
xcit_large_24_p16_384.fb_dist_in1k,52.853,47.147,81.827,18.173,189.10,384,1.000,bicubic,-44.667,-17.653,-32
convnext_small.fb_in22k_ft_in1k_384,52.427,47.573,80.813,19.187,50.22,384,1.000,bicubic,-45.183,-18.787,-47
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,52.320,47.680,82.480,17.520,88.59,256,1.000,bicubic,-45.280,-17.130,-47
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,52.320,47.680,79.733,20.267,88.34,448,1.000,bicubic,-44.990,-19.747,-5
maxvit_tiny_tf_384.in1k,52.080,47.920,79.813,20.187,30.98,384,1.000,bicubic,-45.230,-19.687,-7
regnety_120.sw_in12k_ft_in1k,51.813,48.187,80.787,19.213,51.82,288,1.000,bicubic,-45.467,-18.743,-4
swin_base_patch4_window7_224.ms_in22k_ft_in1k,51.440,48.560,80.080,19.920,87.77,224,0.900,bicubic,-45.840,-19.510,-6
tf_efficientnet_b4.ns_jft_in1k,51.267,48.733,79.173,20.827,19.34,380,0.922,bicubic,-45.683,-20.087,+51
efficientnet_b5.sw_in12k_ft_in1k,51.227,48.773,78.880,21.120,30.39,448,1.000,bicubic,-46.183,-20.670,-23
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,51.213,48.787,78.240,21.760,88.79,224,0.875,bilinear,-45.997,-21.330,+5
flexivit_large.1200ep_in1k,51.200,48.800,80.640,19.360,304.36,240,0.950,bicubic,-46.210,-18.830,-27
eva02_small_patch14_336.mim_in22k_ft_in1k,51.200,48.800,79.120,20.880,22.13,336,1.000,bicubic,-45.940,-20.350,+14
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,51.120,48.880,78.533,21.467,236.34,384,1.000,bicubic,-45.710,-20.877,+64
convnext_small.fb_in22k_ft_in1k,51.067,48.933,80.920,19.080,50.22,288,1.000,bicubic,-46.293,-18.610,-22
mvitv2_large.fb_in1k,50.867,49.133,78.493,21.507,217.99,224,0.900,bicubic,-46.063,-20.907,+48
beit_base_patch16_224.in22k_ft_in22k_in1k,50.720,49.280,79.733,20.267,86.53,224,0.900,bicubic,-46.360,-19.877,+18
tf_efficientnetv2_l.in1k,50.693,49.307,77.613,22.387,118.52,480,1.000,bicubic,-46.777,-21.917,-41
vit_base_patch16_384.orig_in21k_ft_in1k,50.640,49.360,78.200,21.800,86.86,384,1.000,bicubic,-46.080,-21.280,+75
xcit_small_12_p8_384.fb_dist_in1k,50.587,49.413,79.573,20.427,26.21,384,1.000,bicubic,-46.643,-19.907,-7
convformer_s36.sail_in1k_384,50.333,49.667,78.893,21.107,40.01,384,1.000,bicubic,-46.947,-20.577,-14
convnext_tiny.in12k_ft_in1k_384,50.320,49.680,79.800,20.200,28.59,384,1.000,bicubic,-47.020,-19.800,-26
volo_d3_224.sail_in1k,50.293,49.707,78.213,21.787,86.33,224,0.960,bicubic,-46.797,-21.247,+11
flexivit_large.600ep_in1k,50.253,49.747,80.027,19.973,304.36,240,0.950,bicubic,-47.027,-19.403,-23
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,50.120,49.880,78.040,21.960,86.57,224,0.950,bicubic,-47.330,-21.500,-45
convformer_s18.sail_in22k_ft_in1k_384,50.067,49.933,80.973,19.027,26.77,384,1.000,bicubic,-47.203,-18.577,-18
vit_base_patch16_224.augreg2_in21k_ft_in1k,49.813,50.187,78.960,21.040,86.57,224,0.900,bicubic,-47.337,-20.490,-3
vit_base_patch16_clip_224.openai_ft_in12k_in1k,49.813,50.187,77.133,22.867,86.57,224,0.950,bicubic,-47.717,-22.367,-61
cait_s24_384.fb_dist_in1k,49.733,50.267,78.760,21.240,47.06,384,1.000,bicubic,-47.337,-20.670,+14
deit_base_distilled_patch16_384.fb_in1k,49.347,50.653,79.240,20.760,87.63,384,1.000,bicubic,-47.613,-20.240,+27
xcit_medium_24_p16_384.fb_dist_in1k,49.333,50.667,79.867,20.133,84.40,384,1.000,bicubic,-47.947,-19.643,-25
caformer_s18.sail_in1k_384,49.133,50.867,78.693,21.307,26.34,384,1.000,bicubic,-47.947,-20.797,+10
coat_lite_medium_384.in1k,49.093,50.907,78.600,21.400,44.57,384,1.000,bicubic,-48.057,-20.940,-8
tf_efficientnet_b8.ra_in1k,48.920,51.080,77.227,22.773,87.41,672,0.954,bicubic,-48.280,-22.273,-15
convnextv2_base.fcmae_ft_in1k,48.693,51.307,78.827,21.173,88.72,288,1.000,bicubic,-48.527,-20.713,-21
deit3_large_patch16_224.fb_in1k,48.627,51.373,78.120,21.880,304.37,224,0.900,bicubic,-48.313,-21.140,+26
caformer_b36.sail_in1k,48.533,51.467,75.667,24.333,98.75,224,1.000,bicubic,-48.457,-23.673,+16
flexivit_large.300ep_in1k,48.520,51.480,78.653,21.347,304.36,240,0.950,bicubic,-48.730,-20.837,-29
convnext_base.clip_laion2b_augreg_ft_in1k,48.453,51.547,79.827,20.173,88.59,256,1.000,bicubic,-48.787,-19.693,-28
tf_efficientnetv2_s.in21k_ft_in1k,48.427,51.573,77.867,22.133,21.46,384,1.000,bicubic,-48.303,-21.463,+52
resnest269e.in1k,48.213,51.787,74.333,25.667,110.93,416,0.928,bicubic,-48.297,-25.017,+101
deit3_medium_patch16_224.fb_in22k_ft_in1k,48.173,51.827,77.040,22.960,38.85,224,1.000,bicubic,-48.797,-22.390,+14
xcit_large_24_p8_224.fb_dist_in1k,48.160,51.840,79.093,20.907,188.93,224,1.000,bicubic,-48.910,-20.327,+1
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,47.973,52.027,76.853,23.147,88.30,384,1.000,bicubic,-49.387,-22.667,-50
regnety_2560.seer_ft_in1k,47.920,52.080,76.787,23.213,"1,282.60",384,1.000,bicubic,-49.300,-22.733,-30
regnetz_e8.ra3_in1k,47.800,52.200,76.200,23.800,57.70,320,1.000,bicubic,-49.400,-23.300,-28
convnext_tiny.in12k_ft_in1k,47.507,52.493,78.747,21.253,28.59,288,1.000,bicubic,-49.553,-20.803,-2
convformer_s36.sail_in22k_ft_in1k,47.440,52.560,77.107,22.893,40.01,224,1.000,bicubic,-49.640,-22.453,-11
resnetv2_50x3_bit.goog_in21k_ft_in1k,47.307,52.693,77.333,22.667,217.32,448,1.000,bilinear,-49.403,-22.147,+47
vit_base_patch16_clip_224.openai_ft_in1k,47.200,52.800,77.547,22.453,86.57,224,0.900,bicubic,-49.880,-21.923,-10
xcit_large_24_p8_224.fb_in1k,47.160,52.840,74.480,25.520,188.93,224,1.000,bicubic,-49.240,-24.510,+106
xcit_small_24_p16_384.fb_dist_in1k,46.987,53.013,77.160,22.840,47.67,384,1.000,bicubic,-50.133,-22.290,-23
tf_efficientnet_b8.ap_in1k,46.893,53.107,76.533,23.467,87.41,672,0.954,bicubic,-50.217,-23.127,-23
convnext_large.fb_in1k,46.813,53.187,76.627,23.373,197.77,288,1.000,bicubic,-50.287,-22.883,-21
dm_nfnet_f2.dm_in1k,46.653,53.347,74.813,25.187,193.78,352,0.920,bicubic,-50.447,-24.637,-23
efficientnetv2_rw_m.agc_in1k,46.293,53.707,75.720,24.280,53.24,416,1.000,bicubic,-50.687,-23.810,-2
swinv2_base_window16_256.ms_in1k,46.213,53.787,75.173,24.827,87.92,256,0.900,bicubic,-50.537,-24.177,+32
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,46.120,53.880,72.240,27.760,194.03,224,0.875,bilinear,-50.480,-27.290,+65
caformer_m36.sail_in1k,46.080,53.920,74.533,25.467,56.20,224,1.000,bicubic,-50.810,-24.997,+13
volo_d2_224.sail_in1k,46.067,53.933,75.253,24.747,58.68,224,0.960,bicubic,-50.933,-24.137,-8
vit_small_patch16_384.augreg_in21k_ft_in1k,45.880,54.120,76.747,23.253,22.20,384,1.000,bicubic,-50.820,-22.683,+38
ecaresnet269d.ra2_in1k,45.853,54.147,75.067,24.933,102.09,352,1.000,bicubic,-51.227,-24.543,-21
convnextv2_tiny.fcmae_ft_in22k_in1k_384,45.773,54.227,76.973,23.027,28.64,384,1.000,bicubic,-51.467,-22.637,-51
vit_small_r26_s32_384.augreg_in21k_ft_in1k,45.707,54.293,76.040,23.960,36.47,384,1.000,bicubic,-50.973,-23.040,+45
tf_efficientnet_b7.ap_in1k,45.387,54.613,74.200,25.800,66.35,600,0.949,bicubic,-51.803,-25.360,-44
swin_base_patch4_window12_384.ms_in1k,45.333,54.667,74.360,25.640,87.90,384,1.000,bicubic,-51.247,-24.810,+60
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,45.320,54.680,70.907,29.093,88.79,224,0.875,bilinear,-51.010,-28.443,+102
xcit_medium_24_p8_224.fb_dist_in1k,45.227,54.773,76.760,23.240,84.32,224,1.000,bicubic,-51.693,-22.640,-2
eca_nfnet_l2.ra3_in1k,44.987,55.013,75.880,24.120,56.72,384,1.000,bicubic,-52.093,-23.630,-30
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,44.787,55.213,73.747,26.253,41.72,224,0.950,bicubic,-51.853,-25.413,+46
maxxvit_rmlp_small_rw_256.sw_in1k,44.600,55.400,75.040,24.960,66.01,256,0.950,bicubic,-52.200,-24.340,+11
crossvit_18_dagger_408.in1k,44.280,55.720,73.813,26.187,44.61,408,1.000,bicubic,-52.260,-25.547,+64
resnest200e.in1k,44.133,55.867,73.493,26.507,70.20,320,0.909,bicubic,-52.477,-25.857,+49
seresnextaa101d_32x8d.ah_in1k,43.973,56.027,73.347,26.653,93.59,288,1.000,bicubic,-52.987,-25.903,-16
cait_xs24_384.fb_dist_in1k,43.947,56.053,75.160,24.840,26.67,384,1.000,bicubic,-52.593,-24.260,+58
mvitv2_base.fb_in1k,43.747,56.253,74.507,25.493,51.47,224,0.900,bicubic,-53.013,-24.753,+11
resnetrs200.tf_in1k,43.733,56.267,72.827,27.173,93.21,320,1.000,bicubic,-52.967,-26.543,+25
rexnetr_300.sw_in12k_ft_in1k,43.653,56.347,76.547,23.453,34.81,288,1.000,bicubic,-53.187,-22.963,-1
tresnet_xl.miil_in1k_448,43.493,56.507,72.413,27.587,78.44,448,0.875,bilinear,-52.487,-26.637,+162
caformer_s18.sail_in22k_ft_in1k,43.333,56.667,74.947,25.053,26.34,224,1.000,bicubic,-53.377,-24.603,+17
xcit_small_12_p16_384.fb_dist_in1k,43.307,56.693,73.933,26.067,26.25,384,1.000,bicubic,-53.613,-25.457,-16
vit_base_patch16_224.augreg_in21k_ft_in1k,43.280,56.720,72.920,27.080,86.57,224,0.900,bicubic,-53.610,-26.510,-11
swin_small_patch4_window7_224.ms_in22k_ft_in1k,43.227,56.773,76.187,23.813,49.61,224,0.900,bicubic,-53.253,-23.203,+61
resnetrs420.tf_in1k,43.147,56.853,70.453,29.547,191.89,416,1.000,bicubic,-53.763,-29.067,-16
vit_base_patch32_clip_384.openai_ft_in12k_in1k,43.080,56.920,73.253,26.747,88.30,384,0.950,bicubic,-54.030,-26.247,-54
edgenext_base.in21k_ft_in1k,43.040,56.960,75.440,24.560,18.51,320,1.000,bicubic,-53.690,-23.980,+4
coatnet_rmlp_2_rw_224.sw_in1k,43.040,56.960,71.707,28.293,73.88,224,0.950,bicubic,-53.500,-27.593,+51
xcit_medium_24_p8_224.fb_in1k,43.040,56.960,70.320,29.680,84.32,224,1.000,bicubic,-53.050,-28.570,+128
tf_efficientnet_b7.ra_in1k,42.987,57.013,73.173,26.827,66.35,600,0.949,bicubic,-54.023,-26.347,-39
tf_efficientnetv2_m.in1k,42.800,57.200,72.600,27.400,54.14,480,1.000,bicubic,-54.410,-26.930,-73
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,42.693,57.307,74.360,25.640,38.86,256,0.950,bicubic,-53.977,-25.010,+21
hrnet_w48_ssld.paddle_in1k,42.600,57.400,72.133,27.867,77.47,288,1.000,bilinear,-54.430,-27.257,-45
dm_nfnet_f1.dm_in1k,42.547,57.453,71.560,28.440,132.63,320,0.910,bicubic,-54.483,-28.080,-44
xcit_tiny_24_p8_384.fb_dist_in1k,42.453,57.547,72.853,27.147,12.11,384,1.000,bicubic,-54.077,-26.417,+44
gcvit_base.in1k,42.440,57.560,73.773,26.227,90.32,224,0.875,bicubic,-54.130,-25.687,+36
swinv2_small_window16_256.ms_in1k,42.360,57.640,72.867,27.133,49.73,256,0.900,bicubic,-54.110,-26.333,+50
maxvit_rmlp_small_rw_224.sw_in1k,42.227,57.773,72.373,27.627,64.90,224,0.900,bicubic,-54.353,-26.877,+32
convformer_s18.sail_in1k_384,42.120,57.880,74.453,25.547,26.77,384,1.000,bicubic,-54.920,-24.927,-52
convnextv2_tiny.fcmae_ft_in22k_in1k,42.093,57.907,75.747,24.253,28.64,288,1.000,bicubic,-54.757,-23.713,-24
maxvit_base_tf_224.in1k,41.987,58.013,70.093,29.907,119.47,224,0.950,bicubic,-54.963,-29.487,-39
convnext_base.fb_in1k,41.947,58.053,73.987,26.013,88.59,288,1.000,bicubic,-54.883,-25.463,-22
xcit_small_24_p8_224.fb_dist_in1k,41.907,58.093,73.653,26.347,47.63,224,1.000,bicubic,-54.963,-25.827,-30
crossvit_15_dagger_408.in1k,41.907,58.093,72.027,27.973,28.50,408,1.000,bicubic,-54.483,-27.323,+58
resnetaa101d.sw_in12k_ft_in1k,41.893,58.107,72.387,27.613,44.57,288,1.000,bicubic,-54.807,-26.973,-1
maxvit_large_tf_224.in1k,41.880,58.120,68.653,31.347,211.79,224,0.950,bicubic,-55.080,-30.737,-46
xcit_small_24_p8_224.fb_in1k,41.800,58.200,71.093,28.907,47.63,224,1.000,bicubic,-54.610,-28.057,+50
vit_large_r50_s32_224.augreg_in21k_ft_in1k,41.640,58.360,70.240,29.760,328.99,224,0.900,bicubic,-55.150,-29.100,-23
vit_base_patch16_clip_224.laion2b_ft_in1k,41.613,58.387,73.613,26.387,86.57,224,1.000,bicubic,-55.517,-25.847,-80
swinv2_base_window8_256.ms_in1k,41.547,58.453,72.427,27.573,87.92,256,0.900,bicubic,-54.983,-26.893,+30
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,41.533,58.467,71.707,28.293,44.18,224,0.875,bilinear,-54.887,-27.443,+42
deit3_small_patch16_224.fb_in22k_ft_in1k,41.227,58.773,71.893,28.107,22.06,224,1.000,bicubic,-55.433,-27.437,+3
maxvit_rmlp_tiny_rw_256.sw_in1k,41.173,58.827,71.187,28.813,29.15,256,0.950,bicubic,-55.247,-28.193,+41
regnety_1280.seer_ft_in1k,41.147,58.853,71.200,28.800,644.81,384,1.000,bicubic,-55.713,-28.190,-39
seresnext101d_32x8d.ah_in1k,41.147,58.853,70.920,29.080,93.59,288,1.000,bicubic,-55.553,-28.560,-10
convnext_tiny.fb_in22k_ft_in1k_384,41.053,58.947,72.533,27.467,28.59,384,1.000,bicubic,-56.027,-26.977,-77
caformer_s36.sail_in1k,41.040,58.960,70.880,29.120,39.30,224,1.000,bicubic,-55.650,-28.480,-10
davit_base.msft_in1k,40.880,59.120,72.747,27.253,87.95,224,0.950,bicubic,-56.060,-26.593,-54
tf_efficientnet_b6.ap_in1k,40.800,59.200,71.640,28.360,43.04,528,0.942,bicubic,-56.280,-27.780,-82
flexivit_base.1200ep_in1k,40.613,59.387,72.320,27.680,86.59,240,0.950,bicubic,-56.127,-27.040,-29
convformer_b36.sail_in1k,40.453,59.547,69.440,30.560,99.88,224,1.000,bicubic,-56.447,-30.040,-50
resmlp_big_24_224.fb_in22k_ft_in1k,40.373,59.627,74.800,25.200,129.14,224,0.875,bicubic,-56.247,-24.470,-2
deit3_small_patch16_384.fb_in1k,40.347,59.653,70.333,29.667,22.21,384,1.000,bicubic,-55.853,-28.967,+67
tresnet_l.miil_in1k_448,40.213,59.787,69.920,30.080,55.99,448,0.875,bilinear,-55.647,-29.280,+140
deit_base_patch16_384.fb_in1k,40.200,59.800,70.800,29.200,86.86,384,1.000,bicubic,-55.960,-28.440,+75
regnetz_040_h.ra3_in1k,40.000,60.000,71.320,28.680,28.94,320,1.000,bicubic,-56.700,-27.970,-27
resnetrs350.tf_in1k,39.973,60.027,68.907,31.093,163.96,384,1.000,bicubic,-56.777,-30.463,-38
flexivit_base.600ep_in1k,39.933,60.067,71.880,28.120,86.59,240,0.950,bicubic,-56.717,-27.450,-12
regnetz_d8.ra3_in1k,39.907,60.093,71.707,28.293,23.37,320,1.000,bicubic,-56.713,-27.743,-8
swin_s3_base_224.ms_in1k,39.853,60.147,70.467,29.533,71.13,224,0.900,bicubic,-56.387,-28.733,+51
flexivit_base.300ep_in1k,39.533,60.467,71.000,29.000,86.59,240,0.950,bicubic,-57.087,-28.510,-9
seresnext101_32x8d.ah_in1k,39.520,60.480,69.480,30.520,93.57,288,1.000,bicubic,-57.250,-29.870,-45
gcvit_small.in1k,39.413,60.587,70.480,29.520,51.09,224,0.875,bicubic,-56.867,-28.660,+44
regnetz_d8_evos.ch_in1k,39.360,60.640,71.427,28.573,23.46,320,1.000,bicubic,-57.210,-27.803,-3
deit3_base_patch16_224.fb_in1k,39.173,60.827,70.933,29.067,86.59,224,0.900,bicubic,-57.127,-28.247,+40
volo_d1_224.sail_in1k,39.013,60.987,70.267,29.733,26.63,224,0.960,bicubic,-57.307,-29.043,+37
vit_large_patch32_384.orig_in21k_ft_in1k,38.920,61.080,68.933,31.067,306.63,384,1.000,bicubic,-56.910,-30.237,+133
resnetv2_101x1_bit.goog_in21k_ft_in1k,38.907,61.093,71.040,28.960,44.54,448,1.000,bilinear,-57.193,-28.230,+76
mvitv2_small.fb_in1k,38.773,61.227,70.413,29.587,34.87,224,0.900,bicubic,-57.597,-28.787,+24
regnetz_040.ra3_in1k,38.747,61.253,70.440,29.560,27.12,320,1.000,bicubic,-57.973,-29.060,-44
coat_lite_medium.in1k,38.600,61.400,71.093,28.907,44.57,224,0.900,bicubic,-57.870,-28.147,+7
xcit_small_12_p8_224.fb_dist_in1k,38.280,61.720,71.373,28.627,26.21,224,1.000,bicubic,-58.420,-27.987,-40
tf_efficientnet_b7.aa_in1k,38.133,61.867,69.333,30.667,66.35,600,0.949,bicubic,-58.407,-29.927,-8
resnet200d.ra2_in1k,38.133,61.867,68.573,31.427,64.69,320,1.000,bicubic,-58.597,-30.847,-49
davit_small.msft_in1k,38.120,61.880,70.747,29.253,49.75,224,0.950,bicubic,-58.510,-28.593,-27
focalnet_base_srf.ms_in1k,37.827,62.173,69.733,30.267,88.15,224,0.900,bicubic,-58.733,-29.407,-13
focalnet_base_lrf.ms_in1k,37.787,62.213,68.573,31.427,88.75,224,0.900,bicubic,-58.663,-30.727,+5
convformer_m36.sail_in1k,37.760,62.240,67.293,32.707,57.05,224,1.000,bicubic,-58.920,-32.287,-36
swinv2_small_window8_256.ms_in1k,37.733,62.267,69.853,30.147,49.73,256,0.900,bicubic,-58.537,-29.357,+29
xcit_large_24_p16_224.fb_dist_in1k,37.653,62.347,71.613,28.387,189.10,224,1.000,bicubic,-59.147,-27.737,-67
seresnet152d.ra2_in1k,37.653,62.347,69.493,30.507,66.84,320,1.000,bicubic,-59.117,-29.947,-64
maxvit_small_tf_224.in1k,37.560,62.440,67.947,32.053,68.93,224,0.950,bicubic,-59.130,-31.413,-44
xcit_small_12_p8_224.fb_in1k,37.547,62.453,68.173,31.827,26.21,224,1.000,bicubic,-58.563,-30.987,+60
eca_nfnet_l1.ra2_in1k,37.533,62.467,70.933,29.067,41.41,320,1.000,bicubic,-59.167,-28.447,-48
twins_svt_large.in1k,37.213,62.787,69.213,30.787,99.27,224,0.900,bicubic,-59.037,-29.957,+24
regnetz_d32.ra3_in1k,37.160,62.840,70.480,29.520,27.58,320,0.950,bicubic,-59.430,-28.900,-30
vit_base_patch32_384.augreg_in21k_ft_in1k,37.107,62.893,69.800,30.200,88.30,384,1.000,bicubic,-59.383,-29.610,-13
regnety_064.ra3_in1k,37.013,62.987,68.107,31.893,30.58,288,1.000,bicubic,-59.347,-31.123,+7
swin_s3_small_224.ms_in1k,36.933,63.067,68.253,31.747,49.74,224,0.900,bicubic,-59.297,-30.827,+25
efficientnetv2_rw_s.ra2_in1k,36.840,63.160,68.320,31.680,23.94,384,1.000,bicubic,-59.700,-30.780,-25
resnext101_64x4d.c1_in1k,36.840,63.160,66.627,33.373,83.46,288,1.000,bicubic,-59.240,-32.603,+57
regnety_160.deit_in1k,36.827,63.173,69.120,30.880,83.59,288,1.000,bicubic,-59.523,-30.060,+5
pit_b_distilled_224.in1k,36.720,63.280,68.093,31.907,74.79,224,0.900,bicubic,-59.510,-31.017,+20
convnext_small.fb_in1k,36.707,63.293,71.067,28.933,50.22,288,1.000,bicubic,-59.813,-28.273,-23
pvt_v2_b4.in1k,36.627,63.373,68.653,31.347,62.56,224,0.900,bicubic,-59.723,-30.677,+3
pvt_v2_b5.in1k,36.280,63.720,68.427,31.573,81.96,224,0.900,bicubic,-60.080,-30.813,0
cait_xxs36_384.fb_dist_in1k,36.267,63.733,67.733,32.267,17.37,384,1.000,bicubic,-59.563,-31.417,+104
regnety_640.seer_ft_in1k,36.120,63.880,68.307,31.693,281.38,384,1.000,bicubic,-60.730,-31.113,-91
nest_base_jx.goog_in1k,36.053,63.947,66.747,33.253,67.72,224,0.875,bicubic,-60.187,-32.383,+11
coatnet_1_rw_224.sw_in1k,35.973,64.027,67.133,32.867,41.72,224,0.950,bicubic,-60.057,-31.997,+63
maxvit_tiny_rw_224.sw_in1k,35.960,64.040,65.573,34.427,29.06,224,0.950,bicubic,-60.280,-33.577,+11
cs3se_edgenet_x.c2ns_in1k,35.653,64.347,67.827,32.173,50.72,320,1.000,bicubic,-60.797,-31.573,-23
sequencer2d_l.in1k,35.600,64.400,67.360,32.640,54.30,224,0.875,bicubic,-60.550,-31.800,+30
swin_base_patch4_window7_224.ms_in1k,35.560,64.440,68.160,31.840,87.77,224,0.900,bicubic,-60.560,-30.900,+37
regnety_080.ra3_in1k,35.547,64.453,67.213,32.787,39.18,288,1.000,bicubic,-60.973,-32.107,-33
tf_efficientnet_b3.ns_jft_in1k,35.520,64.480,67.773,32.227,12.23,300,0.904,bicubic,-60.870,-31.617,-16
tf_efficientnet_b6.aa_in1k,35.133,64.867,67.747,32.253,43.04,528,0.942,bicubic,-61.537,-31.743,-63
resnetrs270.tf_in1k,34.960,65.040,65.453,34.547,129.86,352,1.000,bicubic,-61.730,-33.897,-69
gcvit_tiny.in1k,34.947,65.053,66.893,33.107,28.22,224,0.875,bicubic,-61.233,-32.297,+15
tf_efficientnet_b5.ap_in1k,34.813,65.187,67.480,32.520,30.39,456,0.934,bicubic,-61.867,-31.980,-69
xcit_tiny_12_p8_384.fb_dist_in1k,34.653,65.347,66.293,33.707,6.71,384,1.000,bicubic,-61.427,-32.567,+39
vit_base_patch16_224_miil.in21k_ft_in1k,34.533,65.467,64.973,35.027,86.54,224,0.875,bilinear,-61.917,-34.337,-31
xcit_medium_24_p16_224.fb_dist_in1k,34.333,65.667,67.880,32.120,84.40,224,1.000,bicubic,-62.267,-31.390,-58
resnet152d.ra2_in1k,34.280,65.720,65.947,34.053,60.21,320,1.000,bicubic,-62.110,-33.213,-25
deit3_medium_patch16_224.fb_in1k,34.253,65.747,66.027,33.973,38.85,224,0.900,bicubic,-61.817,-33.163,+38
coat_small.in1k,34.187,65.813,66.133,33.867,21.69,224,0.900,bicubic,-61.723,-33.017,+71
tresnet_m.miil_in1k_448,34.093,65.907,64.507,35.493,31.39,448,0.875,bilinear,-60.897,-33.953,+233
resmlp_big_24_224.fb_distilled_in1k,34.053,65.947,69.573,30.427,129.14,224,0.875,bicubic,-62.397,-29.547,-38
regnetv_064.ra3_in1k,34.000,66.000,67.880,32.120,30.58,288,1.000,bicubic,-62.410,-31.480,-33
xcit_tiny_24_p16_384.fb_dist_in1k,33.827,66.173,65.400,34.600,12.12,384,1.000,bicubic,-62.113,-33.810,+61
caformer_s18.sail_in1k,33.707,66.293,65.400,34.600,26.34,224,1.000,bicubic,-62.433,-33.770,+15
pvt_v2_b3.in1k,33.640,66.360,67.680,32.320,45.24,224,0.900,bicubic,-62.350,-31.510,+48
focalnet_small_srf.ms_in1k,33.520,66.480,65.907,34.093,49.89,224,0.900,bicubic,-62.550,-33.383,+32
coatnet_rmlp_1_rw_224.sw_in1k,33.507,66.493,65.573,34.427,41.69,224,0.950,bicubic,-62.453,-33.617,+54
convformer_s18.sail_in22k_ft_in1k,33.453,66.547,68.280,31.720,26.77,224,1.000,bicubic,-63.177,-31.070,-76
resnet152.a1h_in1k,33.427,66.573,63.453,36.547,60.19,288,1.000,bicubic,-62.773,-35.767,-5
twins_pcpvt_large.in1k,33.373,66.627,67.933,32.067,60.99,224,0.900,bicubic,-62.767,-31.427,+8
convformer_s36.sail_in1k,33.333,66.667,63.840,36.160,40.01,224,1.000,bicubic,-63.247,-35.400,-69
focalnet_small_lrf.ms_in1k,33.227,66.773,67.067,32.933,50.34,224,0.900,bicubic,-62.953,-32.163,-4
twins_svt_base.in1k,33.200,66.800,65.720,34.280,56.07,224,0.900,bicubic,-62.960,-33.330,+1
pit_b_224.in1k,33.160,66.840,62.360,37.640,73.76,224,0.900,bicubic,-62.460,-36.310,+99
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,33.027,66.973,65.120,34.880,25.03,224,0.875,bilinear,-62.833,-33.950,+59
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,33.027,66.973,64.240,35.760,236.34,224,0.875,bicubic,-63.093,-35.000,+7
mobilevitv2_200.cvnets_in22k_ft_in1k_384,32.987,67.013,65.493,34.507,18.45,384,1.000,bicubic,-63.073,-33.587,+23
convnextv2_nano.fcmae_ft_in22k_in1k_384,32.947,67.053,67.173,32.827,15.62,384,1.000,bicubic,-63.423,-32.227,-43
regnety_320.seer_ft_in1k,32.947,67.053,66.347,33.653,145.05,384,1.000,bicubic,-63.383,-33.083,-35
swinv2_cr_small_ns_224.sw_in1k,32.893,67.107,65.947,34.053,49.70,224,0.900,bicubic,-63.287,-33.173,-11
xception65.ra3_in1k,32.733,67.267,62.987,37.013,39.92,299,0.940,bicubic,-63.627,-36.183,-44
xcit_large_24_p16_224.fb_in1k,32.733,67.267,62.067,37.933,189.10,224,1.000,bicubic,-62.697,-36.563,+137
ecaresnet101d.miil_in1k,32.707,67.293,65.973,34.027,44.57,288,0.950,bicubic,-63.513,-33.337,-25
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,32.640,67.360,63.987,36.013,194.03,224,0.875,bilinear,-63.150,-35.173,+65
swin_small_patch4_window7_224.ms_in1k,32.573,67.427,65.347,34.653,49.61,224,0.900,bicubic,-63.357,-33.803,+43
mobilevitv2_175.cvnets_in22k_ft_in1k_384,32.480,67.520,64.747,35.253,14.25,384,1.000,bicubic,-63.700,-34.393,-16
tf_efficientnetv2_b3.in21k_ft_in1k,32.333,67.667,66.133,33.867,14.36,300,0.900,bicubic,-63.887,-33.097,-27
nest_small_jx.goog_in1k,32.280,67.720,63.747,36.253,38.35,224,0.875,bicubic,-63.690,-35.283,+30
convnext_tiny_hnf.a2h_in1k,32.227,67.773,62.893,37.107,28.59,288,1.000,bicubic,-63.793,-36.247,+21
resnext101_64x4d.tv_in1k,32.160,67.840,64.227,35.773,83.46,224,0.875,bilinear,-63.870,-34.833,+14
vit_base_patch16_224.orig_in21k_ft_in1k,32.053,67.947,61.627,38.373,86.57,224,0.900,bicubic,-63.287,-37.373,+143
convnextv2_tiny.fcmae_ft_in1k,32.027,67.973,67.067,32.933,28.64,288,1.000,bicubic,-64.163,-32.263,-26
maxvit_nano_rw_256.sw_in1k,31.933,68.067,64.187,35.813,15.45,256,0.950,bicubic,-63.987,-34.823,+36
tf_efficientnet_b5.ra_in1k,31.813,68.187,65.280,34.720,30.39,456,0.934,bicubic,-64.517,-34.030,-49
swinv2_cr_small_224.sw_in1k,31.733,68.267,62.547,37.453,49.70,224,0.900,bicubic,-64.347,-36.593,-1
rexnetr_200.sw_in12k_ft_in1k,31.720,68.280,67.640,32.360,16.52,288,1.000,bicubic,-64.500,-31.620,-37
swinv2_tiny_window16_256.ms_in1k,31.707,68.293,65.667,34.333,28.35,256,0.900,bicubic,-64.223,-33.353,+30
regnetz_c16_evos.ch_in1k,31.507,68.493,66.320,33.680,13.49,320,0.950,bicubic,-64.633,-32.680,-21
resnest101e.in1k,31.427,68.573,64.333,35.667,48.28,256,0.875,bilinear,-64.433,-34.767,+37
maxvit_rmlp_nano_rw_256.sw_in1k,31.400,68.600,63.413,36.587,15.50,256,0.950,bicubic,-64.570,-35.737,+19
regnety_320.tv2_in1k,31.360,68.640,64.827,35.173,145.05,224,0.965,bicubic,-64.720,-34.413,-9
maxxvit_rmlp_nano_rw_256.sw_in1k,31.333,68.667,64.360,35.640,16.78,256,0.950,bicubic,-64.707,-34.900,0
regnetv_040.ra3_in1k,31.293,68.707,64.693,35.307,20.64,288,1.000,bicubic,-64.897,-34.557,-38
crossvit_base_240.in1k,31.267,68.733,61.293,38.707,105.03,240,0.875,bicubic,-64.253,-37.577,+87
cait_s24_224.fb_dist_in1k,31.187,68.813,64.573,35.427,46.92,224,1.000,bicubic,-65.233,-34.897,-77
convnext_nano.in12k_ft_in1k,31.107,68.893,67.333,32.667,15.59,288,1.000,bicubic,-64.883,-31.987,+6
efficientnet_b4.ra2_in1k,30.880,69.120,64.667,35.333,19.34,384,1.000,bicubic,-65.270,-34.523,-32
regnety_040.ra3_in1k,30.627,69.373,63.827,36.173,20.65,288,1.000,bicubic,-65.393,-35.243,-1
maxvit_tiny_tf_224.in1k,30.587,69.413,62.773,37.227,30.92,224,0.950,bicubic,-65.513,-36.507,-22
crossvit_18_240.in1k,30.587,69.413,61.920,38.080,43.27,240,0.875,bicubic,-64.853,-37.200,+105
sequencer2d_m.in1k,30.560,69.440,62.987,37.013,38.31,224,0.875,bicubic,-65.250,-36.223,+36
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,30.560,69.440,62.053,37.947,88.22,224,0.900,bicubic,-65.570,-37.167,-31
xcit_small_24_p16_224.fb_dist_in1k,30.547,69.453,64.733,35.267,47.67,224,1.000,bicubic,-65.673,-34.477,-52
crossvit_18_dagger_240.in1k,30.520,69.480,61.800,38.200,44.27,240,0.875,bicubic,-65.050,-37.260,+63
maxxvitv2_nano_rw_256.sw_in1k,30.440,69.560,63.707,36.293,23.70,256,0.950,bicubic,-65.460,-35.463,+17
mvitv2_tiny.fb_in1k,30.173,69.827,64.320,35.680,24.17,224,0.900,bicubic,-65.687,-34.800,+23
xcit_medium_24_p16_224.fb_in1k,30.173,69.827,59.307,40.693,84.40,224,1.000,bicubic,-65.367,-39.473,+68
rexnet_300.nav_in1k,30.013,69.987,63.920,36.080,34.71,224,0.875,bicubic,-65.827,-35.210,+22
cait_xxs24_384.fb_dist_in1k,30.013,69.987,63.907,36.093,12.03,384,1.000,bicubic,-65.267,-35.103,+126
tf_efficientnet_b5.aa_in1k,29.973,70.027,63.080,36.920,30.39,456,0.934,bicubic,-66.497,-36.070,-101
convnext_tiny.fb_in1k,29.960,70.040,65.120,34.880,28.59,288,1.000,bicubic,-65.830,-34.060,+29
twins_pcpvt_base.in1k,29.933,70.067,64.627,35.373,43.83,224,0.900,bicubic,-65.837,-34.353,+29
cs3sedarknet_x.c2ns_in1k,29.933,70.067,62.000,38.000,35.40,288,1.000,bicubic,-66.087,-37.190,-13
resnet50.fb_swsl_ig1b_ft_in1k,29.827,70.173,63.853,36.147,25.56,224,0.875,bilinear,-65.573,-35.247,+100
mobilevitv2_150.cvnets_in22k_ft_in1k_384,29.800,70.200,62.147,37.853,10.59,384,1.000,bicubic,-65.890,-36.993,+38
vit_relpos_base_patch16_clsgap_224.sw_in1k,29.733,70.267,62.880,37.120,86.43,224,0.900,bicubic,-66.037,-36.250,+28
resnet152.a1_in1k,29.720,70.280,57.280,42.720,60.19,288,1.000,bicubic,-65.780,-41.800,+73
convnextv2_nano.fcmae_ft_in22k_in1k,29.693,70.307,65.920,34.080,15.62,288,1.000,bicubic,-66.367,-33.300,-29
deit_base_distilled_patch16_224.fb_in1k,29.587,70.413,64.387,35.613,87.34,224,0.900,bicubic,-66.503,-34.803,-40
convit_base.fb_in1k,29.520,70.480,61.747,38.253,86.54,224,0.875,bicubic,-66.030,-37.373,+51
vit_relpos_medium_patch16_cls_224.sw_in1k,29.307,70.693,60.587,39.413,38.76,224,0.900,bicubic,-66.163,-38.363,+75
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,29.293,70.707,66.107,33.893,28.29,224,0.900,bicubic,-66.217,-32.883,+60
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,29.120,70.880,61.000,39.000,88.79,224,0.875,bilinear,-66.390,-38.120,+60
davit_tiny.msft_in1k,29.107,70.893,63.573,36.427,28.36,224,0.950,bicubic,-66.553,-35.477,+34
tf_efficientnetv2_s.in1k,29.040,70.960,61.200,38.800,21.46,384,1.000,bicubic,-67.300,-38.000,-93
edgenext_base.usi_in1k,29.027,70.973,64.907,35.093,18.51,320,1.000,bicubic,-67.673,-34.593,-162
xception65p.ra3_in1k,28.987,71.013,59.907,40.093,39.82,299,0.940,bicubic,-67.223,-39.273,-76
convnext_tiny.fb_in22k_ft_in1k,28.987,71.013,55.600,44.400,28.59,288,1.000,bicubic,-65.853,-43.110,+181
resnet101d.ra2_in1k,28.973,71.027,62.080,37.920,44.57,320,1.000,bicubic,-67.317,-37.150,-90
resnetv2_101.a1h_in1k,28.933,71.067,59.867,40.133,44.54,288,1.000,bicubic,-67.107,-39.303,-37
resnetrs152.tf_in1k,28.893,71.107,60.507,39.493,86.62,320,1.000,bicubic,-67.687,-38.613,-140
regnety_160.tv2_in1k,28.867,71.133,61.613,38.387,83.59,224,0.965,bicubic,-67.103,-37.357,-25
vit_relpos_medium_patch16_224.sw_in1k,28.853,71.147,61.973,38.027,38.75,224,0.900,bicubic,-66.607,-36.937,+64
xcit_tiny_24_p8_224.fb_dist_in1k,28.707,71.293,61.373,38.627,12.11,224,1.000,bicubic,-67.103,-37.737,+2
xcit_tiny_24_p8_224.fb_in1k,28.653,71.347,60.440,39.560,12.11,224,1.000,bicubic,-67.007,-38.590,+22
crossvit_15_dagger_240.in1k,28.547,71.453,60.293,39.707,28.21,240,0.875,bicubic,-67.133,-38.537,+19
cs3edgenet_x.c2_in1k,28.520,71.480,61.147,38.853,47.82,288,1.000,bicubic,-67.530,-37.993,-46
pvt_v2_b2_li.in1k,28.480,71.520,62.000,38.000,22.55,224,0.900,bicubic,-67.080,-37.030,+31
xcit_small_24_p16_224.fb_in1k,28.333,71.667,58.707,41.293,47.67,224,1.000,bicubic,-67.207,-40.013,+35
efficientformerv2_l.snap_dist_in1k,28.253,71.747,61.907,38.093,26.32,224,0.950,bicubic,-67.817,-37.213,-53
resnet101.a1h_in1k,28.160,71.840,59.387,40.613,44.55,288,1.000,bicubic,-67.860,-39.723,-43
efficientformer_l7.snap_dist_in1k,28.000,72.000,63.013,36.987,82.23,224,0.950,bicubic,-68.110,-36.257,-67
flexivit_small.1200ep_in1k,27.813,72.187,58.627,41.373,22.06,240,0.950,bicubic,-67.737,-40.253,+27
resnetaa50d.sw_in12k_ft_in1k,27.800,72.200,62.253,37.747,25.58,288,1.000,bicubic,-68.030,-36.837,-12
pvt_v2_b2.in1k,27.613,72.387,60.733,39.267,25.36,224,0.900,bicubic,-67.867,-38.267,+49
regnetz_c16.ra3_in1k,27.573,72.427,62.720,37.280,13.46,320,1.000,bicubic,-68.367,-36.500,-30
resnext101_32x8d.tv2_in1k,27.573,72.427,59.827,40.173,88.79,224,0.965,bilinear,-68.377,-39.073,-35
vit_base_patch16_384.augreg_in1k,27.560,72.440,57.253,42.747,86.86,384,1.000,bicubic,-67.380,-41.637,+141
coat_lite_small.in1k,27.507,72.493,58.507,41.493,19.84,224,0.900,bicubic,-68.033,-40.573,+24
deit_base_patch16_224.fb_in1k,27.400,72.600,58.880,41.120,86.57,224,0.900,bicubic,-68.050,-40.210,+50
vit_relpos_base_patch16_224.sw_in1k,27.320,72.680,61.147,38.853,86.43,224,0.900,bicubic,-68.240,-37.843,+17
resnetv2_50x1_bit.goog_in21k_ft_in1k,27.307,72.693,62.853,37.147,25.55,448,1.000,bilinear,-67.703,-36.127,+127
regnety_080_tv.tv2_in1k,27.267,72.733,61.533,38.467,39.38,224,0.965,bicubic,-68.593,-37.717,-25
coatnet_bn_0_rw_224.sw_in1k,27.213,72.787,61.280,38.720,27.44,224,0.950,bicubic,-68.487,-37.770,-2
xcit_small_12_p16_224.fb_dist_in1k,27.147,72.853,59.800,40.200,26.25,224,1.000,bicubic,-68.883,-39.360,-60
dm_nfnet_f0.dm_in1k,27.080,72.920,58.307,41.693,71.49,256,0.900,bicubic,-69.230,-41.013,-119
vit_small_patch16_224.augreg_in21k_ft_in1k,27.040,72.960,59.240,40.760,22.05,224,0.900,bicubic,-68.330,-39.700,+62
coatnet_0_rw_224.sw_in1k,27.027,72.973,59.387,40.613,27.44,224,0.950,bicubic,-68.413,-39.663,+47
sequencer2d_s.in1k,26.867,73.133,60.640,39.360,27.65,224,0.875,bicubic,-69.113,-38.490,-53
flexivit_small.600ep_in1k,26.853,73.147,57.267,42.733,22.06,240,0.950,bicubic,-68.817,-41.793,-4
tresnet_v2_l.miil_in21k_ft_in1k,26.707,73.293,59.827,40.173,46.17,224,0.875,bilinear,-69.453,-39.443,-99
gcvit_xtiny.in1k,26.693,73.307,60.907,39.093,19.98,224,0.875,bicubic,-68.897,-38.133,+4
mobilevitv2_200.cvnets_in22k_ft_in1k,26.653,73.347,59.400,40.600,18.45,256,0.888,bicubic,-68.507,-39.550,+89
swin_s3_tiny_224.ms_in1k,26.520,73.480,60.347,39.653,28.33,224,0.900,bicubic,-68.660,-38.603,+83
coatnet_rmlp_nano_rw_224.sw_in1k,26.467,73.533,60.547,39.453,15.15,224,0.900,bicubic,-68.963,-38.493,+43
swinv2_tiny_window8_256.ms_in1k,26.387,73.613,60.480,39.520,28.35,256,0.900,bicubic,-69.103,-38.310,+25
tf_efficientnet_b4.aa_in1k,26.293,73.707,60.093,39.907,19.34,380,0.922,bicubic,-69.607,-38.957,-46
regnetx_320.tv2_in1k,26.267,73.733,58.133,41.867,107.81,224,0.965,bicubic,-69.723,-40.967,-64
deit3_small_patch16_224.fb_in1k,26.240,73.760,54.493,45.507,22.06,224,0.900,bicubic,-68.750,-44.487,+115
nfnet_l0.ra2_in1k,26.227,73.773,61.760,38.240,35.07,288,1.000,bicubic,-69.893,-37.510,-98
tf_efficientnet_b4.ap_in1k,26.227,73.773,60.240,39.760,19.34,380,0.922,bicubic,-69.933,-38.900,-111
coatnext_nano_rw_224.sw_in1k,26.227,73.773,59.613,40.387,14.70,224,0.900,bicubic,-69.203,-39.397,+37
regnety_032.ra_in1k,26.173,73.827,61.013,38.987,19.44,288,1.000,bicubic,-69.787,-38.147,-63
ecaresnet50t.ra2_in1k,26.120,73.880,60.013,39.987,25.57,320,0.950,bicubic,-69.400,-38.797,+4
fbnetv3_g.ra2_in1k,26.093,73.907,61.080,38.920,16.62,288,0.950,bilinear,-69.417,-38.120,+9
mobilevitv2_175.cvnets_in22k_ft_in1k,26.013,73.987,58.520,41.480,14.25,256,0.888,bicubic,-69.207,-40.280,+66
flexivit_small.300ep_in1k,25.893,74.107,57.067,42.933,22.06,240,0.950,bicubic,-69.607,-41.713,+11
visformer_small.in1k,25.853,74.147,58.933,41.067,40.22,224,0.900,bicubic,-69.637,-40.167,+14
vit_small_patch16_384.augreg_in1k,25.773,74.227,57.613,42.387,22.20,384,1.000,bicubic,-69.517,-41.387,+53
convformer_s18.sail_in1k,25.680,74.320,57.960,42.040,26.77,224,1.000,bicubic,-70.270,-41.120,-67
halo2botnet50ts_256.a1h_in1k,25.573,74.427,56.827,43.173,22.64,256,0.950,bicubic,-69.847,-42.193,+29
vit_relpos_medium_patch16_rpn_224.sw_in1k,25.520,74.480,58.667,41.333,38.73,224,0.900,bicubic,-69.980,-40.463,+5
coat_mini.in1k,25.507,74.493,57.707,42.293,10.34,224,0.900,bicubic,-69.463,-41.373,+104
crossvit_15_240.in1k,25.453,74.547,57.600,42.400,27.53,240,0.875,bicubic,-69.697,-41.360,+71
vit_srelpos_medium_patch16_224.sw_in1k,25.373,74.627,58.427,41.573,38.74,224,0.900,bicubic,-69.867,-40.443,+52
resnet101.a1_in1k,25.253,74.747,55.120,44.880,44.55,288,1.000,bicubic,-70.267,-43.730,-6
resnetv2_50x1_bit.goog_distilled_in1k,25.200,74.800,59.653,40.347,25.55,224,0.875,bicubic,-70.930,-39.627,-117
convit_small.fb_in1k,25.147,74.853,57.293,42.707,27.78,224,0.875,bicubic,-70.063,-41.607,+55
xcit_small_12_p16_224.fb_in1k,25.107,74.893,56.080,43.920,26.25,224,1.000,bicubic,-70.313,-42.760,+22
vit_base_patch16_rpn_224.sw_in1k,25.080,74.920,58.693,41.307,86.54,224,0.900,bicubic,-70.310,-40.167,+27
gc_efficientnetv2_rw_t.agc_in1k,25.067,74.933,57.720,42.280,13.68,288,1.000,bicubic,-70.673,-41.460,-43
resnet152.a2_in1k,25.053,74.947,54.307,45.693,60.19,288,1.000,bicubic,-70.437,-44.593,+1
eca_nfnet_l0.ra2_in1k,24.813,75.187,60.053,39.947,24.14,288,1.000,bicubic,-71.127,-39.057,-78
xception41p.ra3_in1k,24.787,75.213,55.253,44.747,26.91,299,0.940,bicubic,-70.723,-43.657,-9
tnt_s_patch16_224,24.707,75.293,58.173,41.827,23.76,224,0.900,bicubic,-70.333,-40.657,+78
convnext_nano_ols.d1h_in1k,24.547,75.453,57.027,42.973,15.65,288,1.000,bicubic,-70.593,-41.823,+64
xcit_tiny_12_p16_384.fb_dist_in1k,24.453,75.547,57.080,42.920,6.72,384,1.000,bicubic,-70.677,-41.940,+64
cs3darknet_x.c2ns_in1k,24.387,75.613,57.760,42.240,35.05,288,1.000,bicubic,-71.463,-41.410,-68
efficientnetv2_rw_t.ra2_in1k,24.293,75.707,57.387,42.613,13.65,288,1.000,bicubic,-71.307,-41.683,-35
swinv2_cr_tiny_ns_224.sw_in1k,24.147,75.853,58.187,41.813,28.33,224,0.900,bicubic,-71.223,-40.953,+20
eva02_tiny_patch14_336.mim_in22k_ft_in1k,24.133,75.867,55.427,44.573,5.76,336,1.000,bicubic,-70.777,-43.453,+93
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,24.120,75.880,57.387,42.613,44.18,224,0.875,bilinear,-71.320,-41.333,0
twins_svt_small.in1k,24.093,75.907,57.160,42.840,24.06,224,0.900,bicubic,-71.097,-41.720,+42
coatnet_nano_rw_224.sw_in1k,24.093,75.907,57.147,42.853,15.14,224,0.900,bicubic,-71.147,-41.843,+37
tf_efficientnet_b5.in1k,24.080,75.920,58.333,41.667,30.39,456,0.934,bicubic,-71.990,-40.867,-120
vit_small_r26_s32_224.augreg_in21k_ft_in1k,24.067,75.933,56.213,43.787,36.43,224,0.900,bicubic,-71.573,-42.977,-47
tf_efficientnet_b2.ns_jft_in1k,24.040,75.960,57.320,42.680,9.11,260,0.890,bicubic,-71.730,-41.800,-64
mobilevitv2_150.cvnets_in22k_ft_in1k,24.013,75.987,55.920,44.080,10.59,256,0.888,bicubic,-71.127,-42.860,+49
vit_relpos_small_patch16_224.sw_in1k,24.000,76.000,58.160,41.840,21.98,224,0.900,bicubic,-71.150,-40.770,+44
convnext_nano.d1h_in1k,24.000,76.000,56.200,43.800,15.59,288,1.000,bicubic,-71.350,-42.720,+14
cs3sedarknet_l.c2ns_in1k,23.960,76.040,58.760,41.240,21.91,288,0.950,bicubic,-71.350,-40.370,+16
ecaresnet50d.miil_in1k,23.933,76.067,58.760,41.240,25.58,288,0.950,bicubic,-71.517,-40.080,-13
poolformer_m48.sail_in1k,23.867,76.133,57.160,42.840,73.47,224,0.950,bicubic,-71.763,-42.080,-51
vit_small_patch32_384.augreg_in21k_ft_in1k,23.773,76.227,57.267,42.733,22.92,384,1.000,bicubic,-71.257,-41.723,+60
lamhalobotnet50ts_256.a1h_in1k,23.613,76.387,55.320,44.680,22.57,256,0.950,bicubic,-71.547,-43.780,+38
nasnetalarge.tf_in1k,23.493,76.507,55.000,45.000,88.75,331,0.911,bicubic,-72.197,-43.930,-62
crossvit_small_240.in1k,23.427,76.573,56.813,43.187,26.86,240,0.875,bicubic,-71.423,-42.197,+87
levit_conv_384.fb_dist_in1k,23.387,76.613,56.413,43.587,39.13,224,0.900,bicubic,-72.143,-42.637,-42
levit_384.fb_dist_in1k,23.387,76.613,56.387,43.613,39.13,224,0.900,bicubic,-72.143,-42.673,-42
seresnext50_32x4d.racm_in1k,23.360,76.640,57.453,42.547,27.56,288,0.950,bicubic,-72.380,-41.567,-73
pnasnet5large.tf_in1k,23.333,76.667,53.627,46.373,86.06,331,0.911,bicubic,-72.377,-45.403,-70
focalnet_tiny_srf.ms_in1k,23.267,76.733,58.333,41.667,28.43,224,0.900,bicubic,-72.233,-40.627,-36
efficientnet_b3.ra2_in1k,23.200,76.800,55.987,44.013,12.23,320,1.000,bicubic,-72.520,-43.053,-74
wide_resnet50_2.racm_in1k,23.187,76.813,56.000,44.000,68.88,288,0.950,bicubic,-72.443,-42.940,-64
pit_s_distilled_224.in1k,23.160,76.840,57.107,42.893,24.04,224,0.900,bicubic,-71.980,-41.623,+31
hrnet_w18_ssld.paddle_in1k,23.147,76.853,55.160,44.840,21.30,288,1.000,bilinear,-72.843,-44.150,-125
efficientformer_l3.snap_dist_in1k,23.133,76.867,57.147,42.853,31.41,224,0.950,bicubic,-72.457,-42.013,-62
nest_tiny_jx.goog_in1k,23.107,76.893,56.200,43.800,17.06,224,0.875,bicubic,-72.133,-42.710,+10
focalnet_tiny_lrf.ms_in1k,23.080,76.920,58.560,41.440,28.65,224,0.900,bicubic,-72.380,-40.400,-31
resnet61q.ra2_in1k,23.013,76.987,55.760,44.240,36.85,288,1.000,bicubic,-72.767,-43.230,-89
regnetx_160.tv2_in1k,22.987,77.013,56.333,43.667,54.28,224,0.965,bicubic,-72.893,-42.757,-108
resmlp_big_24_224.fb_in1k,22.893,77.107,54.280,45.720,129.14,224,0.875,bicubic,-71.787,-44.220,+96
vit_srelpos_small_patch16_224.sw_in1k,22.880,77.120,55.747,44.253,21.97,224,0.900,bicubic,-72.150,-43.203,+42
halonet50ts.a1h_in1k,22.867,77.133,54.013,45.987,22.73,256,0.940,bicubic,-72.273,-44.877,+25
resnetv2_50d_evos.ah_in1k,22.853,77.147,55.173,44.827,25.59,288,1.000,bicubic,-72.777,-43.937,-74
vit_base_patch32_clip_224.laion2b_ft_in1k,22.853,77.147,55.013,44.987,88.22,224,0.900,bicubic,-72.667,-44.097,-57
tf_efficientnet_b4.in1k,22.773,77.227,57.080,42.920,19.34,380,0.922,bicubic,-73.047,-41.970,-102
poolformerv2_m48.sail_in1k,22.760,77.240,55.733,44.267,73.35,224,1.000,bicubic,-73.010,-43.307,-93
twins_pcpvt_small.in1k,22.707,77.293,56.827,43.173,24.11,224,0.900,bicubic,-72.503,-42.053,+5
convnextv2_nano.fcmae_ft_in1k,22.653,77.347,58.307,41.693,15.62,288,1.000,bicubic,-73.147,-40.783,-102
poolformer_m36.sail_in1k,22.587,77.413,55.373,44.627,56.17,224,0.950,bicubic,-72.803,-43.567,-23
vit_base_patch32_clip_224.openai_ft_in1k,22.560,77.440,55.333,44.667,88.22,224,0.900,bicubic,-72.550,-43.597,+22
vit_base_patch32_224.augreg_in21k_ft_in1k,22.427,77.573,54.027,45.973,88.22,224,0.900,bicubic,-72.583,-45.033,+37
resnetv2_50d_gn.ah_in1k,22.307,77.693,55.000,45.000,25.57,288,1.000,bicubic,-73.173,-43.950,-49
ecaresnet101d_pruned.miil_in1k,22.267,77.733,56.213,43.787,24.88,288,0.950,bicubic,-73.493,-42.967,-99
wide_resnet101_2.tv2_in1k,22.160,77.840,54.973,45.027,126.89,224,0.965,bilinear,-73.380,-43.887,-74
ecaresnet50t.a1_in1k,22.093,77.907,53.680,46.320,25.57,288,1.000,bicubic,-73.317,-45.330,-35
xcit_tiny_12_p8_224.fb_dist_in1k,22.067,77.933,54.267,45.733,6.71,224,1.000,bicubic,-73.023,-44.643,+20
resnext50_32x4d.a1h_in1k,22.027,77.973,54.080,45.920,25.03,288,1.000,bicubic,-73.423,-44.760,-48
res2net101d.in1k,21.853,78.147,51.680,48.320,45.23,224,0.875,bilinear,-72.967,-47.090,+60
tresnet_m.miil_in21k_ft_in1k,21.667,78.333,53.853,46.147,31.39,224,0.875,bilinear,-74.043,-45.067,-101
efficientformerv2_s2.snap_dist_in1k,21.533,78.467,54.240,45.760,12.71,224,0.950,bicubic,-73.827,-44.690,-31
resnet50_gn.a1h_in1k,21.400,78.600,54.067,45.933,25.56,288,0.950,bicubic,-73.850,-44.933,-17
maxvit_rmlp_pico_rw_256.sw_in1k,21.240,78.760,51.920,48.080,7.52,256,0.950,bicubic,-73.390,-46.840,+83
convmixer_1536_20.in1k,21.213,78.787,55.507,44.493,51.63,224,0.960,bicubic,-73.867,-43.523,+14
swin_tiny_patch4_window7_224.ms_in1k,21.173,78.827,55.933,44.067,28.29,224,0.900,bicubic,-73.967,-42.927,+1
resnet101.a2_in1k,21.173,78.827,51.920,48.080,44.55,288,1.000,bicubic,-74.237,-47.020,-43
pit_s_224.in1k,21.080,78.920,53.627,46.373,23.46,224,0.900,bicubic,-73.490,-45.073,+93
xcit_tiny_12_p8_224.fb_in1k,20.960,79.040,52.467,47.533,6.71,224,1.000,bicubic,-73.730,-46.193,+67
resnet51q.ra2_in1k,20.920,79.080,55.680,44.320,35.70,288,1.000,bilinear,-74.950,-43.440,-137
poolformerv2_m36.sail_in1k,20.920,79.080,53.120,46.880,56.08,224,1.000,bicubic,-74.480,-46.180,-44
resnetrs101.tf_in1k,20.867,79.133,52.827,47.173,63.62,288,0.940,bicubic,-74.563,-46.163,-56
deit_small_distilled_patch16_224.fb_in1k,20.733,79.267,55.133,44.867,22.44,224,0.900,bicubic,-73.977,-43.897,+61
sebotnet33ts_256.a1h_in1k,20.720,79.280,48.733,51.267,13.70,256,0.940,bicubic,-73.890,-49.777,+83
regnety_032.tv2_in1k,20.560,79.440,54.360,45.640,19.44,224,0.965,bicubic,-74.750,-44.550,-36
resnet152.tv2_in1k,20.493,79.507,52.333,47.667,60.19,224,0.965,bilinear,-75.007,-46.627,-77
ecaresnetlight.miil_in1k,20.467,79.533,53.413,46.587,30.16,288,0.950,bicubic,-74.823,-45.617,-36
resnest50d_4s2x40d.in1k,20.387,79.613,52.773,47.227,30.42,224,0.875,bicubic,-74.583,-46.007,+18
resnetaa50.a1h_in1k,20.067,79.933,51.947,48.053,25.56,288,1.000,bicubic,-75.133,-46.973,-24
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,20.040,79.960,53.560,46.440,25.03,224,0.875,bilinear,-74.820,-45.300,+31
haloregnetz_b.ra3_in1k,20.013,79.987,49.987,50.013,11.68,224,0.940,bicubic,-74.677,-48.843,+56
regnetz_b16.ra3_in1k,19.813,80.187,52.853,47.147,9.72,288,1.000,bicubic,-75.357,-46.227,-23
xcit_nano_12_p8_384.fb_dist_in1k,19.787,80.213,50.587,49.413,3.05,384,1.000,bicubic,-73.733,-47.943,+205
resnext50_32x4d.a1_in1k,19.733,80.267,50.173,49.827,25.03,288,1.000,bicubic,-75.087,-48.427,+37
tresnet_xl.miil_in1k,19.653,80.347,53.160,46.840,78.44,224,0.875,bilinear,-75.787,-45.630,-72
senet154.gluon_in1k,19.320,80.680,47.600,52.400,115.09,224,0.875,bicubic,-75.600,-51.160,+17
rexnet_200.nav_in1k,19.227,80.773,52.627,47.373,16.37,224,0.875,bicubic,-75.713,-46.383,+11
levit_conv_256.fb_dist_in1k,19.187,80.813,50.067,49.933,18.89,224,0.900,bicubic,-75.833,-48.863,0
levit_256.fb_dist_in1k,19.173,80.827,50.107,49.893,18.89,224,0.900,bicubic,-75.847,-48.773,-2
lambda_resnet50ts.a1h_in1k,19.147,80.853,49.240,50.760,21.54,256,0.950,bicubic,-75.623,-49.450,+33
repvgg_b3.rvgg_in1k,19.080,80.920,50.307,49.693,123.09,224,0.875,bilinear,-75.470,-48.403,+72
mixer_b16_224.miil_in21k_ft_in1k,19.040,80.960,51.213,48.787,59.88,224,0.875,bilinear,-76.260,-47.667,-53
legacy_senet154.in1k,19.040,80.960,47.960,52.040,115.09,224,0.875,bilinear,-76.030,-50.870,-11
resnet50d.a1_in1k,18.960,81.040,48.813,51.187,25.58,288,1.000,bicubic,-75.770,-49.937,+38
seresnext101_64x4d.gluon_in1k,18.933,81.067,49.213,50.787,88.23,224,0.875,bicubic,-75.997,-49.617,+5
deit_small_patch16_224.fb_in1k,18.893,81.107,51.387,48.613,22.05,224,0.900,bicubic,-75.477,-47.313,+93
mobilevitv2_200.cvnets_in1k,18.893,81.107,50.520,49.480,18.45,256,0.888,bicubic,-75.947,-48.010,+20
gcvit_xxtiny.in1k,18.787,81.213,53.307,46.693,12.00,224,0.875,bicubic,-75.633,-45.583,+86
edgenext_small.usi_in1k,18.680,81.320,53.600,46.400,5.59,320,1.000,bicubic,-76.720,-45.270,-74
tf_efficientnet_b1.ns_jft_in1k,18.640,81.360,51.760,48.240,7.79,240,0.882,bicubic,-76.520,-47.200,-40
regnetx_080.tv2_in1k,18.573,81.427,50.333,49.667,39.57,224,0.965,bicubic,-76.527,-48.497,-24
ecaresnet50t.a2_in1k,18.533,81.467,48.773,51.227,25.57,288,1.000,bicubic,-76.817,-50.087,-70
poolformer_s36.sail_in1k,18.493,81.507,52.187,47.813,30.86,224,0.900,bicubic,-76.587,-46.723,-23
wide_resnet50_2.tv2_in1k,18.467,81.533,52.453,47.547,68.88,224,0.965,bilinear,-76.403,-46.457,+5
cait_xxs36_224.fb_dist_in1k,18.307,81.693,49.480,50.520,17.30,224,1.000,bicubic,-75.963,-49.230,+97
cs3darknet_l.c2ns_in1k,18.293,81.707,51.840,48.160,21.16,288,0.950,bicubic,-76.827,-47.140,-34
seresnet50.ra2_in1k,18.293,81.707,51.253,48.747,28.09,288,0.950,bicubic,-77.037,-47.757,-72
sehalonet33ts.ra2_in1k,18.227,81.773,47.733,52.267,13.69,256,0.940,bicubic,-76.533,-50.837,+17
ese_vovnet39b.ra_in1k,18.200,81.800,49.880,50.120,24.57,288,0.950,bicubic,-76.670,-49.060,+1
tf_efficientnet_lite4.in1k,18.133,81.867,50.707,49.293,13.01,380,0.920,bilinear,-76.747,-48.313,-2
vit_tiny_patch16_384.augreg_in21k_ft_in1k,18.013,81.987,50.320,49.680,5.79,384,1.000,bicubic,-75.637,-48.270,+163
gcresnet50t.ra2_in1k,17.880,82.120,49.413,50.587,25.90,288,1.000,bicubic,-77.360,-49.567,-63
resnet50d.ra2_in1k,17.853,82.147,50.240,49.760,25.58,288,0.950,bicubic,-77.157,-48.790,-22
mobilevitv2_175.cvnets_in1k,17.787,82.213,49.747,50.253,14.25,256,0.888,bicubic,-77.103,-49.113,-9
resnest50d_1s4x24d.in1k,17.640,82.360,49.760,50.240,25.68,224,0.875,bicubic,-77.090,-49.220,+12
resnetv2_50.a1h_in1k,17.573,82.427,49.813,50.187,25.55,288,1.000,bicubic,-77.277,-49.057,0
resnet50.c2_in1k,17.533,82.467,49.760,50.240,25.56,288,1.000,bicubic,-77.387,-49.050,-15
resnest50d.in1k,17.360,82.640,50.720,49.280,27.48,224,0.875,bilinear,-77.490,-48.160,-3
convnext_pico.d1_in1k,17.347,82.653,50.200,49.800,9.05,288,0.950,bicubic,-77.413,-48.510,+5
seresnext101_32x4d.gluon_in1k,17.333,82.667,46.373,53.627,48.96,224,0.875,bicubic,-77.557,-52.447,-13
efficientnet_el.ra_in1k,17.320,82.680,50.027,49.973,10.59,300,0.904,bicubic,-77.800,-48.943,-48
inception_v4.tf_in1k,17.293,82.707,45.867,54.133,42.68,299,0.875,bicubic,-77.077,-52.713,+68
tf_efficientnet_b3.ap_in1k,17.227,82.773,49.693,50.307,12.23,300,0.904,bicubic,-78.093,-49.207,-87
gcresnext50ts.ch_in1k,17.173,82.827,48.107,51.893,15.67,288,1.000,bicubic,-77.687,-50.703,-12
xcit_tiny_24_p16_224.fb_dist_in1k,17.160,82.840,47.507,52.493,12.12,224,1.000,bicubic,-77.380,-51.293,+44
regnetx_032.tv2_in1k,17.067,82.933,48.107,51.893,15.30,224,0.965,bicubic,-77.593,-50.743,+15
xception71.tf_in1k,17.027,82.973,45.560,54.440,42.34,299,0.903,bicubic,-77.273,-53.090,+73
regnety_016.tv2_in1k,16.973,83.027,49.827,50.173,11.20,224,0.965,bicubic,-77.547,-48.993,+43
tf_efficientnet_b3.aa_in1k,16.973,83.027,49.293,50.707,12.23,300,0.904,bicubic,-78.027,-49.617,-37
cs3darknet_focus_l.c2ns_in1k,16.960,83.040,50.493,49.507,21.15,288,0.950,bicubic,-78.200,-48.387,-69
convnextv2_pico.fcmae_ft_in1k,16.907,83.093,50.307,49.693,9.07,288,0.950,bicubic,-78.323,-48.613,-80
resmlp_36_224.fb_distilled_in1k,16.880,83.120,51.493,48.507,44.69,224,0.875,bicubic,-78.010,-47.367,-26
resnext101_64x4d.gluon_in1k,16.867,83.133,44.147,55.853,83.46,224,0.875,bicubic,-77.803,-54.513,+7
poolformerv2_s36.sail_in1k,16.680,83.320,49.600,50.400,30.79,224,1.000,bicubic,-78.630,-49.320,-96
resnet152d.gluon_in1k,16.627,83.373,44.213,55.787,60.21,224,0.875,bicubic,-78.103,-54.277,-5
tf_efficientnetv2_b3.in1k,16.600,83.400,48.680,51.320,14.36,300,0.904,bicubic,-78.580,-50.140,-78
gmlp_s16_224.ra3_in1k,16.547,83.453,45.080,54.920,19.42,224,0.875,bicubic,-77.603,-53.590,+82
resnet152s.gluon_in1k,16.547,83.453,44.480,55.520,60.32,224,0.875,bicubic,-78.473,-54.400,-52
tresnet_l.miil_in1k,16.520,83.480,49.893,50.107,55.99,224,0.875,bilinear,-78.760,-49.067,-97
inception_resnet_v2.tf_in1k,16.520,83.480,44.960,55.040,55.84,299,0.897,bicubic,-78.060,-53.830,+19
convnext_pico_ols.d1_in1k,16.493,83.507,49.707,50.293,9.06,288,1.000,bicubic,-78.127,-49.153,+14
seresnet50.a1_in1k,16.493,83.507,46.760,53.240,28.09,288,1.000,bicubic,-78.167,-52.020,+2
resmlp_24_224.fb_distilled_in1k,16.453,83.547,50.347,49.653,30.02,224,0.875,bicubic,-77.997,-48.423,+38
mobilevitv2_150.cvnets_in1k,16.453,83.547,48.453,51.547,10.59,256,0.888,bicubic,-78.097,-50.327,+21
resnet101.tv2_in1k,16.413,83.587,48.720,51.280,44.55,224,0.965,bilinear,-78.857,-50.190,-100
gernet_l.idstcv_in1k,16.320,83.680,47.227,52.773,31.08,256,0.875,bilinear,-78.790,-51.753,-70
inception_resnet_v2.tf_ens_adv_in1k,16.280,83.720,43.627,56.373,55.84,299,0.897,bicubic,-77.890,-54.913,+66
repvgg_b3g4.rvgg_in1k,16.227,83.773,47.653,52.347,83.83,224,0.875,bilinear,-78.283,-51.317,+25
xcit_tiny_24_p16_224.fb_in1k,16.227,83.773,46.000,54.000,12.12,224,1.000,bicubic,-77.853,-52.540,+78
resnet50.a1_in1k,16.040,83.960,45.773,54.227,25.56,288,1.000,bicubic,-78.700,-52.937,-24
xception65.tf_in1k,16.040,83.960,43.773,56.227,39.92,299,0.903,bicubic,-77.750,-54.597,+108
resnet50.c1_in1k,16.000,84.000,47.387,52.613,25.56,288,1.000,bicubic,-78.730,-51.433,-24
ecaresnet50d_pruned.miil_in1k,15.987,84.013,49.707,50.293,19.94,288,0.950,bicubic,-79.123,-49.193,-78
edgenext_small_rw.sw_in1k,15.987,84.013,49.667,50.333,7.83,320,1.000,bicubic,-78.683,-49.113,-15
resnet50.fb_ssl_yfcc100m_ft_in1k,15.933,84.067,49.413,50.587,25.56,224,0.875,bilinear,-78.517,-49.327,+26
resnext50_32x4d.ra_in1k,15.880,84.120,47.227,52.773,25.03,288,0.950,bicubic,-78.820,-51.533,-21
resnet50.d_in1k,15.640,84.360,45.160,54.840,25.56,288,1.000,bicubic,-79.340,-53.680,-62
regnety_320.pycls_in1k,15.627,84.373,44.760,55.240,145.05,224,0.875,bicubic,-78.913,-54.030,+8
vit_base_patch32_384.augreg_in1k,15.600,84.400,44.147,55.853,88.30,384,1.000,bicubic,-78.040,-54.253,+118
convmixer_768_32.in1k,15.507,84.493,47.907,52.093,21.11,224,0.960,bicubic,-78.993,-50.953,+15
ecaresnet26t.ra2_in1k,15.453,84.547,47.960,52.040,16.01,320,0.950,bicubic,-78.867,-50.760,+35
resnext50d_32x4d.bt_in1k,15.413,84.587,46.200,53.800,25.05,288,0.950,bicubic,-79.137,-52.490,+3
coat_tiny.in1k,15.400,84.600,45.587,54.413,5.50,224,0.900,bicubic,-78.180,-52.833,+118
resnet50d.a2_in1k,15.387,84.613,44.813,55.187,25.58,288,1.000,bicubic,-79.193,-53.877,-5
skresnext50_32x4d.ra_in1k,15.387,84.613,44.560,55.440,27.48,224,0.875,bicubic,-78.853,-53.900,+38
resnext50_32x4d.a2_in1k,15.293,84.707,45.253,54.747,25.03,288,1.000,bicubic,-79.287,-53.397,-6
seresnet33ts.ra2_in1k,15.267,84.733,47.360,52.640,19.78,288,1.000,bicubic,-79.773,-51.540,-85
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,15.240,84.760,42.640,57.360,119.42,256,0.900,bicubic,-78.500,-55.900,+99
cait_xxs24_224.fb_dist_in1k,15.160,84.840,44.893,55.107,11.96,224,1.000,bicubic,-78.450,-53.567,+111
eca_resnet33ts.ra2_in1k,15.053,84.947,49.000,51.000,19.68,288,1.000,bicubic,-79.567,-49.760,-19
vit_base_patch16_224.augreg_in1k,15.000,85.000,42.013,57.987,86.57,224,0.900,bicubic,-78.630,-56.227,+107
levit_conv_192.fb_dist_in1k,14.907,85.093,44.933,55.067,10.95,224,0.900,bicubic,-79.263,-53.747,+43
levit_192.fb_dist_in1k,14.880,85.120,44.933,55.067,10.95,224,0.900,bicubic,-79.290,-53.607,+41
seresnet50.a2_in1k,14.840,85.160,44.400,55.600,28.09,288,1.000,bicubic,-79.820,-54.320,-31
poolformerv2_s24.sail_in1k,14.800,85.200,45.920,54.080,21.34,224,1.000,bicubic,-79.850,-52.880,-30
rexnet_150.nav_in1k,14.720,85.280,46.880,53.120,9.73,224,0.875,bicubic,-79.760,-51.920,+1
darknet53.c2ns_in1k,14.693,85.307,47.107,52.893,41.61,288,1.000,bicubic,-79.927,-51.793,-25
gcresnet33ts.ra2_in1k,14.667,85.333,46.320,53.680,19.88,288,1.000,bicubic,-80.253,-52.490,-77
resnet50.tv2_in1k,14.640,85.360,46.973,53.027,25.56,224,0.965,bilinear,-80.020,-51.817,-37
res2net50d.in1k,14.627,85.373,44.480,55.520,25.72,224,0.875,bilinear,-79.693,-54.160,+18
darknetaa53.c2ns_in1k,14.560,85.440,45.413,54.587,36.02,288,1.000,bilinear,-79.910,-53.407,-3
coat_lite_mini.in1k,14.560,85.440,44.493,55.507,11.01,224,0.900,bicubic,-79.480,-53.997,+50
efficientnet_el_pruned.in1k,14.493,85.507,46.120,53.880,10.59,300,0.904,bicubic,-79.907,-52.620,+3
efficientnet_b2.ra_in1k,14.440,85.560,46.053,53.947,9.11,288,1.000,bicubic,-80.180,-52.727,-28
poolformer_s24.sail_in1k,14.293,85.707,47.347,52.653,21.39,224,0.900,bicubic,-80.267,-51.553,-23
legacy_seresnext101_32x4d.in1k,14.173,85.827,43.000,57.000,48.96,224,0.875,bilinear,-80.187,-55.860,+6
fbnetv3_d.ra2_in1k,14.093,85.907,46.467,53.533,10.31,256,0.950,bilinear,-79.827,-52.153,+56
gernet_m.idstcv_in1k,14.080,85.920,46.080,53.920,21.14,224,0.875,bilinear,-80.540,-52.830,-35
pvt_v2_b1.in1k,14.027,85.973,47.747,52.253,14.01,224,0.900,bicubic,-79.773,-50.913,+69
mobilevitv2_125.cvnets_in1k,14.013,85.987,45.027,54.973,7.48,256,0.888,bicubic,-79.947,-53.523,+50
dpn68b.ra_in1k,13.893,86.107,40.280,59.720,12.61,288,1.000,bicubic,-80.107,-58.220,+45
resnext101_32x4d.gluon_in1k,13.853,86.147,41.640,58.360,44.18,224,0.875,bicubic,-80.687,-57.140,-22
seresnext50_32x4d.gluon_in1k,13.653,86.347,43.733,56.267,27.56,224,0.875,bicubic,-80.677,-54.877,+2
pit_xs_distilled_224.in1k,13.573,86.427,45.160,54.840,11.00,224,0.900,bicubic,-80.197,-53.460,+67
resnet152.a3_in1k,13.547,86.453,43.400,56.600,60.19,224,0.950,bicubic,-81.183,-55.280,-64
resmlp_36_224.fb_in1k,13.467,86.533,46.667,53.333,44.69,224,0.875,bicubic,-80.723,-51.943,+12
repvgg_b2g4.rvgg_in1k,13.467,86.533,43.853,56.147,61.76,224,0.875,bilinear,-80.413,-54.807,+55
efficientformerv2_s1.snap_dist_in1k,13.440,86.560,42.947,57.053,6.19,224,0.950,bicubic,-80.760,-55.693,+10
vit_small_patch16_224.augreg_in1k,13.387,86.613,41.440,58.560,22.05,224,0.900,bicubic,-80.503,-57.000,+50
eca_botnext26ts_256.c1_in1k,13.320,86.680,42.133,57.867,10.59,256,0.950,bicubic,-80.460,-56.367,+60
visformer_tiny.in1k,13.307,86.693,43.933,56.067,10.32,224,0.900,bicubic,-80.253,-54.457,+82
regnetx_320.pycls_in1k,13.293,86.707,40.747,59.253,107.81,224,0.875,bicubic,-81.157,-58.173,-19
resnet101d.gluon_in1k,13.187,86.813,41.560,58.440,44.57,224,0.875,bicubic,-81.043,-56.980,+2
efficientnet_b3_pruned.in1k,13.160,86.840,45.200,54.800,9.86,300,0.904,bicubic,-81.470,-53.610,-56
resnet50.b1k_in1k,13.093,86.907,43.947,56.053,25.56,288,1.000,bicubic,-81.767,-54.923,-92
mixnet_xl.ra_in1k,13.080,86.920,43.213,56.787,11.90,224,0.875,bicubic,-81.100,-55.117,+5
cspresnext50.ra_in1k,13.053,86.947,44.973,55.027,20.57,256,0.887,bilinear,-81.777,-53.797,-88
efficientformer_l1.snap_dist_in1k,13.013,86.987,45.600,54.400,12.29,224,0.950,bicubic,-81.467,-53.230,-32
regnetx_016.tv2_in1k,13.000,87.000,45.427,54.573,9.19,224,0.965,bicubic,-81.130,-53.323,+14
nf_regnet_b1.ra2_in1k,12.947,87.053,44.400,55.600,10.22,288,0.900,bicubic,-81.163,-54.220,+13
eca_halonext26ts.c1_in1k,12.947,87.053,42.827,57.173,10.76,256,0.940,bicubic,-81.093,-55.833,+22
mobilevit_s.cvnets_in1k,12.907,87.093,40.747,59.253,5.58,256,0.900,bicubic,-80.273,-57.683,+111
pit_xs_224.in1k,12.800,87.200,42.813,57.187,10.62,224,0.900,bicubic,-80.300,-55.517,+119
tf_efficientnet_b3.in1k,12.787,87.213,43.640,56.360,12.23,300,0.904,bicubic,-81.753,-55.210,-47
resnet50.b2k_in1k,12.760,87.240,44.133,55.867,25.56,288,1.000,bicubic,-81.970,-54.797,-86
resnext50_32x4d.tv2_in1k,12.680,87.320,43.120,56.880,25.03,224,0.965,bilinear,-81.940,-55.590,-62
inception_v3.gluon_in1k,12.627,87.373,40.427,59.573,23.83,299,0.875,bicubic,-80.843,-58.143,+77
tresnet_m.miil_in1k,12.613,87.387,41.907,58.093,31.39,224,0.875,bilinear,-82.017,-56.643,-68
resnet50.a1h_in1k,12.600,87.400,44.227,55.773,25.56,224,1.000,bicubic,-82.170,-54.243,-97
crossvit_9_dagger_240.in1k,12.560,87.440,41.733,58.267,8.78,240,0.875,bicubic,-80.340,-56.507,+130
resnetblur50.bt_in1k,12.493,87.507,44.147,55.853,25.56,288,0.950,bicubic,-81.967,-54.693,-42
resmlp_24_224.fb_in1k,12.493,87.507,43.413,56.587,30.02,224,0.875,bicubic,-81.537,-54.917,+12
convnext_femto_ols.d1_in1k,12.480,87.520,43.933,56.067,5.23,288,0.950,bicubic,-81.440,-54.807,+21
coat_lite_tiny.in1k,12.467,87.533,41.053,58.947,5.72,224,0.900,bicubic,-80.773,-57.207,+95
efficientnet_em.ra2_in1k,12.413,87.587,43.933,56.067,6.90,240,0.882,bicubic,-81.427,-54.887,+28
regnety_120.pycls_in1k,12.400,87.600,42.173,57.827,51.82,224,0.875,bicubic,-82.070,-56.587,-48
regnety_160.pycls_in1k,12.227,87.773,41.387,58.613,83.59,224,0.875,bicubic,-82.133,-57.243,-36
resnet50.a2_in1k,12.173,87.827,40.453,59.547,25.56,288,1.000,bicubic,-82.457,-58.207,-79
ecaresnet50t.a3_in1k,12.147,87.853,41.573,58.427,25.57,224,0.950,bicubic,-82.203,-57.097,-36
hrnet_w64.ms_in1k,12.000,88.000,40.840,59.160,128.06,224,0.875,bilinear,-82.020,-57.750,+4
cspdarknet53.ra_in1k,11.960,88.040,43.267,56.733,27.64,256,0.887,bilinear,-82.690,-55.573,-85
xcit_tiny_12_p16_224.fb_dist_in1k,11.933,88.067,40.120,59.880,6.72,224,1.000,bicubic,-81.477,-58.390,+69
resnet101s.gluon_in1k,11.893,88.107,40.960,59.040,44.67,224,0.875,bicubic,-82.827,-57.860,-100
resnet101.a3_in1k,11.867,88.133,40.827,59.173,44.55,224,0.950,bicubic,-82.173,-57.723,-5
gmixer_24_224.ra3_in1k,11.867,88.133,37.800,62.200,24.72,224,0.875,bicubic,-80.963,-60.080,+120
nf_resnet50.ra2_in1k,11.773,88.227,45.907,54.093,25.56,288,0.940,bicubic,-82.767,-52.723,-68
fbnetv3_b.ra2_in1k,11.733,88.267,44.400,55.600,8.60,256,0.950,bilinear,-82.237,-54.090,0
dpn92.mx_in1k,11.640,88.360,40.160,59.840,37.67,224,0.875,bicubic,-82.650,-58.590,-37
botnet26t_256.c1_in1k,11.613,88.387,40.107,59.893,12.49,256,0.950,bicubic,-81.897,-58.193,+50
convnextv2_femto.fcmae_ft_in1k,11.600,88.400,40.800,59.200,5.23,288,0.950,bicubic,-82.590,-57.860,-30
xception41.tf_in1k,11.573,88.427,39.107,60.893,26.97,299,0.903,bicubic,-81.847,-59.323,+58
dla102x2.in1k,11.560,88.440,41.293,58.707,41.28,224,0.875,bilinear,-82.410,-57.227,-3
vit_small_patch32_224.augreg_in21k_ft_in1k,11.507,88.493,39.547,60.453,22.88,224,0.900,bicubic,-80.533,-58.743,+161
levit_128.fb_dist_in1k,11.440,88.560,40.187,59.813,9.21,224,0.900,bicubic,-81.900,-58.183,+64
levit_conv_128.fb_dist_in1k,11.440,88.560,40.173,59.827,9.21,224,0.900,bicubic,-81.910,-58.197,+61
lambda_resnet26t.c1_in1k,11.387,88.613,40.187,59.813,10.96,256,0.940,bicubic,-82.443,-58.463,+9
seresnext26t_32x4d.bt_in1k,11.373,88.627,41.107,58.893,16.81,288,0.950,bicubic,-82.187,-57.383,+36
regnety_080.pycls_in1k,11.373,88.627,40.613,59.387,39.18,224,0.875,bicubic,-82.797,-58.007,-35
tf_efficientnet_el.in1k,11.347,88.653,42.040,57.960,10.59,300,0.904,bicubic,-83.053,-56.670,-62
efficientnet_b2_pruned.in1k,11.333,88.667,42.040,57.960,8.31,260,0.890,bicubic,-82.807,-56.670,-28
resnet50.ra_in1k,11.333,88.667,41.013,58.987,25.56,288,0.950,bicubic,-82.877,-57.607,-45
convnext_femto.d1_in1k,11.213,88.787,42.773,57.227,5.22,288,0.950,bicubic,-82.717,-55.747,-10
xcit_nano_12_p16_384.fb_dist_in1k,11.213,88.787,39.827,60.173,3.05,384,1.000,bicubic,-80.617,-57.863,+164
resnet152c.gluon_in1k,11.120,88.880,37.133,62.867,60.21,224,0.875,bicubic,-83.040,-61.457,-38
hrnet_w48.ms_in1k,11.093,88.907,40.280,59.720,77.47,224,0.875,bilinear,-82.827,-58.330,-11
halonet26t.a1h_in1k,11.093,88.907,38.800,61.200,12.48,256,0.950,bicubic,-82.907,-59.540,-20
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,11.080,88.920,39.933,60.067,6.36,384,1.000,bicubic,-80.960,-58.297,+146
mobilevitv2_100.cvnets_in1k,11.067,88.933,40.613,59.387,4.90,256,0.888,bicubic,-82.223,-57.667,+59
inception_v3.tf_adv_in1k,11.013,88.987,36.733,63.267,23.83,299,0.875,bicubic,-81.887,-61.407,+92
tf_efficientnet_b0.ns_jft_in1k,11.000,89.000,40.093,59.907,5.29,224,0.875,bicubic,-82.620,-58.547,+19
tf_efficientnetv2_b2.in1k,11.000,89.000,39.747,60.253,10.10,260,0.890,bicubic,-83.420,-58.833,-76
seresnext26d_32x4d.bt_in1k,10.987,89.013,41.347,58.653,16.81,288,0.950,bicubic,-82.453,-56.983,+35
xcit_tiny_12_p16_224.fb_in1k,10.987,89.013,37.067,62.933,6.72,224,1.000,bicubic,-81.523,-61.173,+116
regnety_008_tv.tv2_in1k,10.840,89.160,40.547,59.453,6.43,224,0.965,bicubic,-82.850,-57.943,+6
resnet34d.ra2_in1k,10.827,89.173,38.653,61.347,21.82,288,0.950,bicubic,-82.813,-59.887,+8
dpn107.mx_in1k,10.827,89.173,38.307,61.693,86.92,224,0.875,bicubic,-83.513,-60.193,-71
inception_v3.tf_in1k,10.827,89.173,36.853,63.147,23.83,299,0.875,bicubic,-82.493,-61.527,+47
xcit_nano_12_p8_224.fb_dist_in1k,10.760,89.240,38.120,61.880,3.05,224,1.000,bicubic,-81.330,-60.040,+132
tf_efficientnet_b2.ap_in1k,10.547,89.453,40.133,59.867,9.11,260,0.890,bicubic,-83.963,-58.487,-96
densenetblur121d.ra_in1k,10.547,89.453,39.707,60.293,8.00,288,0.950,bicubic,-82.073,-58.553,+100
dpn131.mx_in1k,10.533,89.467,36.787,63.213,79.25,224,0.875,bicubic,-83.517,-61.923,-42
rexnet_130.nav_in1k,10.413,89.587,41.547,58.453,7.56,224,0.875,bicubic,-83.487,-56.853,-24
hrnet_w44.ms_in1k,10.307,89.693,39.480,60.520,67.06,224,0.875,bilinear,-83.243,-59.120,+12
xcit_nano_12_p8_224.fb_in1k,10.293,89.707,36.960,63.040,3.05,224,1.000,bicubic,-80.717,-60.820,+166
resnext50_32x4d.a3_in1k,10.253,89.747,38.200,61.800,25.03,224,0.950,bicubic,-83.407,-60.320,-2
lambda_resnet26rpt_256.c1_in1k,10.227,89.773,38.120,61.880,10.99,256,0.940,bicubic,-83.483,-60.400,-7
resnext101_32x8d.tv_in1k,10.173,89.827,37.747,62.253,88.79,224,0.875,bilinear,-83.647,-60.833,-21
regnetx_160.pycls_in1k,10.133,89.867,38.053,61.947,54.28,224,0.875,bicubic,-84.007,-60.467,-58
legacy_seresnext50_32x4d.in1k,10.093,89.907,39.213,60.787,27.56,224,0.875,bilinear,-83.657,-59.367,-16
resnetrs50.tf_in1k,10.053,89.947,37.560,62.440,35.69,224,0.910,bicubic,-84.267,-60.970,-83
dpn98.mx_in1k,10.013,89.987,36.187,63.813,61.57,224,0.875,bicubic,-84.147,-62.453,-64
inception_v3.tv_in1k,10.013,89.987,35.213,64.787,23.83,299,0.875,bicubic,-82.737,-63.057,+80
efficientnet_b1.ft_in1k,10.000,90.000,37.600,62.400,7.79,256,1.000,bicubic,-83.250,-60.690,+35
legacy_xception.tf_in1k,9.987,90.013,37.987,62.013,22.86,299,0.897,bicubic,-83.473,-60.543,+12
resnet33ts.ra2_in1k,9.973,90.027,39.813,60.187,19.68,288,1.000,bicubic,-84.127,-58.837,-59
regnety_064.pycls_in1k,9.933,90.067,39.093,60.907,30.58,224,0.875,bicubic,-84.207,-59.657,-65
resnet152.gluon_in1k,9.747,90.253,36.067,63.933,60.19,224,0.875,bicubic,-84.323,-62.393,-59
tf_efficientnet_lite3.in1k,9.680,90.320,39.013,60.987,8.20,300,0.904,bilinear,-84.520,-59.617,-81
tf_efficientnet_b2.aa_in1k,9.667,90.333,38.893,61.107,9.11,260,0.890,bicubic,-84.713,-59.717,-101
tf_efficientnet_cc_b1_8e.in1k,9.573,90.427,36.827,63.173,39.72,240,0.882,bicubic,-84.347,-61.433,-44
res2net101_26w_4s.in1k,9.507,90.493,35.093,64.907,45.21,224,0.875,bilinear,-84.213,-63.227,-23
resnet50.ram_in1k,9.480,90.520,35.507,64.493,25.56,288,0.950,bicubic,-85.040,-63.143,-120
legacy_seresnet152.in1k,9.333,90.667,37.373,62.627,66.82,224,0.875,bilinear,-84.037,-60.967,+11
cspresnet50.ra_in1k,9.280,90.720,39.613,60.387,21.62,256,0.887,bilinear,-84.470,-59.027,-32
resnet34.a1_in1k,9.267,90.733,34.947,65.053,21.80,288,1.000,bicubic,-83.833,-63.373,+36
hrnet_w40.ms_in1k,9.253,90.747,36.947,63.053,57.56,224,0.875,bilinear,-84.247,-61.623,-5
resnet32ts.ra2_in1k,9.213,90.787,38.613,61.387,17.96,288,1.000,bicubic,-84.617,-60.027,-44
regnetx_120.pycls_in1k,9.213,90.787,37.200,62.800,46.11,224,0.875,bicubic,-85.017,-61.470,-93
crossvit_tiny_240.in1k,9.107,90.893,34.600,65.400,7.01,240,0.875,bicubic,-81.123,-62.990,+161
resnest26d.gluon_in1k,9.053,90.947,37.827,62.173,17.07,224,0.875,bilinear,-84.267,-60.533,+10
resnet50d.a3_in1k,9.040,90.960,37.320,62.680,25.58,224,0.950,bicubic,-84.430,-61.130,-6
vit_tiny_patch16_224.augreg_in21k_ft_in1k,9.040,90.960,34.627,65.373,5.72,224,0.900,bicubic,-82.720,-63.143,+117
gcresnext26ts.ch_in1k,8.987,91.013,36.920,63.080,10.48,288,1.000,bicubic,-84.173,-61.450,+24
vit_base_patch16_224.sam_in1k,8.987,91.013,36.120,63.880,86.57,224,0.900,bicubic,-85.163,-62.380,-86
regnety_040.pycls_in1k,8.933,91.067,37.053,62.947,20.65,224,0.875,bicubic,-84.947,-61.467,-55
resnext50_32x4d.gluon_in1k,8.933,91.067,36.307,63.693,25.03,224,0.875,bicubic,-84.877,-62.113,-49
bat_resnext26ts.ch_in1k,8.893,91.107,36.440,63.560,10.73,256,0.900,bicubic,-84.427,-62.180,+5
rexnet_100.nav_in1k,8.893,91.107,36.400,63.600,4.80,224,0.875,bicubic,-84.127,-61.900,+33
mixnet_l.ft_in1k,8.880,91.120,36.227,63.773,7.33,224,0.875,bicubic,-84.550,-61.993,-11
convit_tiny.fb_in1k,8.867,91.133,34.307,65.693,5.71,224,0.875,bicubic,-81.793,-63.423,+142
mobilenetv3_large_100.miil_in21k_ft_in1k,8.853,91.147,33.067,66.933,5.48,224,0.875,bilinear,-83.407,-64.563,+81
hrnet_w18.ms_aug_in1k,8.747,91.253,38.787,61.213,21.30,224,0.950,bilinear,-84.803,-59.913,-26
levit_conv_128s.fb_dist_in1k,8.653,91.347,33.093,66.907,7.78,224,0.900,bicubic,-83.307,-64.967,+98
resnet50.bt_in1k,8.640,91.360,38.720,61.280,25.56,288,0.950,bicubic,-85.670,-59.920,-117
dla169.in1k,8.640,91.360,35.973,64.027,53.39,224,0.875,bilinear,-84.720,-62.637,-9
levit_128s.fb_dist_in1k,8.640,91.360,33.067,66.933,7.78,224,0.900,bicubic,-83.320,-64.993,+95
mixer_b16_224.goog_in21k_ft_in1k,8.627,91.373,29.413,70.587,59.88,224,0.875,bicubic,-83.253,-67.847,+97
hrnet_w30.ms_in1k,8.587,91.413,37.040,62.960,37.71,224,0.875,bilinear,-84.613,-61.450,+3
eca_resnext26ts.ch_in1k,8.560,91.440,36.827,63.173,10.30,288,1.000,bicubic,-84.500,-61.573,+18
legacy_seresnet101.in1k,8.560,91.440,35.960,64.040,49.33,224,0.875,bilinear,-84.750,-62.560,-5
convnext_atto_ols.a2_in1k,8.533,91.467,35.000,65.000,3.70,288,0.950,bicubic,-84.557,-63.470,+12
tf_efficientnet_b2.in1k,8.520,91.480,36.507,63.493,9.11,260,0.890,bicubic,-85.590,-61.943,-97
tf_efficientnet_b1.ap_in1k,8.453,91.547,35.213,64.787,7.79,240,0.882,bicubic,-85.227,-63.147,-52
repvgg_b2.rvgg_in1k,8.440,91.560,36.480,63.520,89.02,224,0.875,bilinear,-85.060,-62.020,-34
resmlp_12_224.fb_distilled_in1k,8.307,91.693,36.853,63.147,15.35,224,0.875,bicubic,-84.513,-61.287,+30
ese_vovnet19b_dw.ra_in1k,8.280,91.720,37.000,63.000,6.54,288,0.950,bicubic,-84.870,-61.250,+2
crossvit_9_240.in1k,8.267,91.733,34.107,65.893,8.55,240,0.875,bicubic,-82.373,-63.623,+126
dla102x.in1k,8.200,91.800,37.027,62.973,26.31,224,0.875,bilinear,-85.300,-61.703,-36
seresnext26ts.ch_in1k,8.147,91.853,36.093,63.907,10.39,288,1.000,bicubic,-84.813,-62.087,+17
hrnet_w32.ms_in1k,8.067,91.933,37.547,62.453,41.23,224,0.875,bilinear,-85.463,-60.913,-43
resnet101c.gluon_in1k,8.027,91.973,33.320,66.680,44.57,224,0.875,bicubic,-85.643,-65.100,-59
vit_base_patch32_224.augreg_in1k,7.987,92.013,30.453,69.547,88.22,224,0.900,bicubic,-83.203,-66.937,+103
cs3darknet_m.c2ns_in1k,7.960,92.040,36.507,63.493,9.31,288,0.950,bicubic,-85.380,-62.093,-26
poolformerv2_s12.sail_in1k,7.960,92.040,34.560,65.440,11.89,224,1.000,bicubic,-85.000,-63.800,+13
resnet50d.gluon_in1k,7.947,92.053,35.000,65.000,25.58,224,0.875,bicubic,-85.803,-63.390,-71
resnet26t.ra2_in1k,7.893,92.107,36.720,63.280,16.01,320,1.000,bicubic,-85.307,-61.690,-16
res2net50_26w_8s.in1k,7.853,92.147,33.707,66.293,48.40,224,0.875,bilinear,-85.537,-64.463,-34
dla60_res2next.in1k,7.800,92.200,34.973,65.027,17.03,224,0.875,bilinear,-85.390,-63.427,-16
mobilevitv2_075.cvnets_in1k,7.773,92.227,33.720,66.280,2.87,256,0.888,bicubic,-83.987,-64.140,+80
convnextv2_atto.fcmae_ft_in1k,7.773,92.227,32.907,67.093,3.71,288,0.950,bicubic,-85.197,-65.253,+6
mobilevit_xs.cvnets_in1k,7.747,92.253,32.520,67.480,2.32,256,0.900,bicubic,-83.053,-65.410,+105
tf_efficientnetv2_b1.in1k,7.707,92.293,34.653,65.347,8.14,240,0.882,bicubic,-86.243,-63.967,-102
deit_tiny_distilled_patch16_224.fb_in1k,7.707,92.293,33.507,66.493,5.91,224,0.900,bicubic,-83.003,-64.053,+108
regnety_032.pycls_in1k,7.693,92.307,34.293,65.707,19.44,224,0.875,bicubic,-85.717,-64.347,-44
efficientformerv2_s0.snap_dist_in1k,7.680,92.320,32.680,67.320,3.60,224,0.950,bicubic,-84.290,-65.210,+63
convnext_atto.d2_in1k,7.613,92.387,35.053,64.947,3.70,288,0.950,bicubic,-85.177,-63.097,+12
dla60_res2net.in1k,7.600,92.400,34.600,65.400,20.85,224,0.875,bilinear,-85.580,-63.840,-23
efficientnet_b1_pruned.in1k,7.480,92.520,34.480,65.520,6.33,240,0.882,bicubic,-85.300,-63.560,+11
regnetx_064.pycls_in1k,7.373,92.627,34.360,65.640,26.21,224,0.875,bicubic,-86.527,-64.280,-102
wide_resnet101_2.tv_in1k,7.360,92.640,34.147,65.853,126.89,224,0.875,bilinear,-86.380,-63.923,-84
densenet121.ra_in1k,7.320,92.680,35.453,64.547,7.98,288,0.950,bicubic,-85.200,-62.767,+26
deit_tiny_patch16_224.fb_in1k,7.307,92.693,30.680,69.320,5.72,224,0.900,bicubic,-82.343,-66.780,+122
regnetx_008.tv2_in1k,7.280,92.720,34.133,65.867,7.26,224,0.965,bicubic,-85.270,-64.047,+21
resnet50s.gluon_in1k,7.280,92.720,33.453,66.547,25.68,224,0.875,bicubic,-86.360,-65.007,-78
resnet34.a2_in1k,7.267,92.733,31.813,68.187,21.80,288,1.000,bicubic,-85.453,-66.357,+9
edgenext_x_small.in1k,7.267,92.733,30.920,69.080,2.34,288,1.000,bicubic,-84.443,-66.680,+69
resnet101.gluon_in1k,7.253,92.747,32.760,67.240,44.55,224,0.875,bicubic,-86.497,-65.620,-92
hardcorenas_e.miil_green_in1k,7.240,92.760,33.280,66.720,8.07,224,0.875,bilinear,-85.340,-64.820,+15
efficientnet_b0.ra_in1k,7.213,92.787,33.987,66.013,5.29,224,0.875,bicubic,-85.477,-64.083,+7
tf_mixnet_l.in1k,7.173,92.827,31.680,68.320,7.33,224,0.875,bicubic,-86.147,-66.350,-46
tf_efficientnet_b1.aa_in1k,7.147,92.853,33.027,66.973,7.79,240,0.882,bicubic,-86.343,-65.333,-68
tf_efficientnet_cc_b0_8e.in1k,7.133,92.867,31.773,68.227,24.01,224,0.875,bicubic,-85.707,-66.407,-9
convmixer_1024_20_ks9_p14.in1k,7.093,92.907,33.053,66.947,24.38,224,0.960,bicubic,-85.307,-65.217,+22
resmlp_12_224.fb_in1k,7.013,92.987,33.933,66.067,15.35,224,0.875,bicubic,-85.207,-64.217,+30
cs3darknet_focus_m.c2ns_in1k,6.933,93.067,34.587,65.413,9.30,288,0.950,bicubic,-86.037,-63.473,-22
hardcorenas_f.miil_green_in1k,6.853,93.147,34.053,65.947,8.20,224,0.875,bilinear,-86.117,-64.337,-22
pit_ti_distilled_224.in1k,6.840,93.160,30.947,69.053,5.10,224,0.900,bicubic,-83.930,-66.663,+80
efficientnet_es.ra_in1k,6.693,93.307,33.987,66.013,5.44,224,0.875,bicubic,-86.467,-64.423,-43
SelecSls60b.in1k,6.693,93.307,33.293,66.707,32.77,224,0.875,bicubic,-86.617,-64.997,-54
res2net50_26w_6s.in1k,6.693,93.307,31.653,68.347,37.05,224,0.875,bilinear,-86.707,-66.627,-67
poolformer_s12.sail_in1k,6.667,93.333,34.520,65.480,11.92,224,0.900,bicubic,-85.943,-63.660,0
tinynet_a.in1k,6.653,93.347,32.227,67.773,6.19,192,0.875,bicubic,-85.787,-65.853,+9
mixnet_m.ft_in1k,6.653,93.347,32.053,67.947,5.01,224,0.875,bicubic,-85.757,-65.817,+12
dpn68b.mx_in1k,6.640,93.360,32.907,67.093,12.61,224,0.875,bicubic,-86.150,-65.173,-17
legacy_seresnext26_32x4d.in1k,6.627,93.373,33.227,66.773,16.79,224,0.875,bicubic,-86.013,-64.893,-7
tf_efficientnet_b1.in1k,6.627,93.373,32.640,67.360,7.79,240,0.882,bicubic,-86.463,-65.660,-42
resnet50.a3_in1k,6.600,93.400,32.053,67.947,25.56,224,0.950,bicubic,-86.120,-65.957,-13
regnety_004.tv2_in1k,6.533,93.467,30.467,69.533,4.34,224,0.965,bicubic,-85.047,-67.423,+47
dla60x.in1k,6.493,93.507,34.080,65.920,17.35,224,0.875,bilinear,-86.587,-64.420,-44
repvgg_b1.rvgg_in1k,6.467,93.533,33.800,66.200,57.42,224,0.875,bilinear,-86.863,-64.720,-71
skresnet34.ra_in1k,6.467,93.533,31.587,68.413,22.28,224,0.875,bicubic,-85.913,-66.563,+7
hardcorenas_d.miil_green_in1k,6.427,93.573,32.240,67.760,7.50,224,0.875,bilinear,-85.983,-65.830,+1
resnet26d.bt_in1k,6.307,93.693,32.733,67.267,16.01,288,0.950,bicubic,-86.213,-65.477,-5
regnetx_080.pycls_in1k,6.293,93.707,32.373,67.627,39.57,224,0.875,bicubic,-87.587,-66.217,-132
resnet18.fb_swsl_ig1b_ft_in1k,6.253,93.747,31.600,68.400,11.69,224,0.875,bilinear,-84.447,-66.100,+66
legacy_seresnet50.in1k,6.200,93.800,32.680,67.320,28.09,224,0.875,bilinear,-86.760,-65.730,-38
pit_ti_224.in1k,6.133,93.867,30.200,69.800,4.85,224,0.900,bicubic,-83.807,-67.250,+80
resnet152.tv_in1k,6.067,93.933,32.080,67.920,60.19,224,0.875,bilinear,-87.253,-65.960,-77
wide_resnet50_2.tv_in1k,6.013,93.987,32.160,67.840,68.88,224,0.875,bilinear,-87.147,-66.160,-63
tf_efficientnet_cc_b0_4e.in1k,5.987,94.013,29.573,70.427,13.31,224,0.875,bicubic,-86.603,-68.507,-18
regnetx_040.pycls_in1k,5.947,94.053,31.493,68.507,22.12,224,0.875,bicubic,-87.613,-67.037,-109
seresnet50.a3_in1k,5.947,94.053,30.813,69.187,28.09,224,0.950,bicubic,-86.123,-67.227,+9
tf_efficientnetv2_b0.in1k,5.880,94.120,30.787,69.213,7.14,224,0.875,bicubic,-87.230,-67.603,-64
mixer_l16_224.goog_in21k_ft_in1k,5.880,94.120,18.547,81.453,208.20,224,0.875,bicubic,-81.270,-77.553,+104
dla102.in1k,5.867,94.133,32.760,67.240,33.27,224,0.875,bilinear,-87.193,-65.790,-59
regnety_016.pycls_in1k,5.680,94.320,30.480,69.520,11.20,224,0.875,bicubic,-87.360,-67.890,-58
SelecSls60.in1k,5.653,94.347,32.533,67.467,30.67,224,0.875,bicubic,-87.367,-65.657,-59
res2next50.in1k,5.653,94.347,30.893,69.107,24.67,224,0.875,bilinear,-87.187,-67.287,-46
hardcorenas_c.miil_green_in1k,5.653,94.347,30.453,69.547,5.52,224,0.875,bilinear,-86.367,-67.387,+8
hrnet_w18.ms_in1k,5.493,94.507,30.920,69.080,21.30,224,0.875,bilinear,-86.827,-67.350,-11
resnest14d.gluon_in1k,5.453,94.547,28.533,71.467,10.61,224,0.875,bilinear,-86.267,-69.337,+22
tf_efficientnet_em.in1k,5.360,94.640,31.093,68.907,6.90,240,0.882,bicubic,-87.560,-67.117,-53
tf_efficientnet_lite2.in1k,5.320,94.680,30.880,69.120,6.09,260,0.890,bicubic,-87.340,-67.350,-37
gernet_s.idstcv_in1k,5.320,94.680,30.147,69.853,8.17,224,0.875,bilinear,-86.810,-68.043,-5
resnext26ts.ra2_in1k,5.307,94.693,29.680,70.320,10.30,288,1.000,bicubic,-86.823,-68.350,-6
tf_efficientnet_b0.ap_in1k,5.307,94.693,28.840,71.160,5.29,224,0.875,bicubic,-86.893,-69.180,-10
repvgg_b1g4.rvgg_in1k,5.240,94.760,30.760,69.240,39.97,224,0.875,bilinear,-87.760,-67.670,-65
resnet34.bt_in1k,5.213,94.787,29.413,70.587,21.80,288,0.950,bicubic,-87.187,-68.647,-22
xcit_nano_12_p16_224.fb_dist_in1k,5.200,94.800,26.493,73.507,3.05,224,1.000,bicubic,-84.510,-70.607,+61
res2net50_26w_4s.in1k,5.173,94.827,29.360,70.640,25.70,224,0.875,bilinear,-87.317,-68.670,-30
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,5.080,94.920,27.027,72.973,6.34,224,0.900,bicubic,-84.100,-70.193,+66
mobilenetv3_large_100.ra_in1k,5.067,94.933,28.187,71.813,5.48,224,0.875,bicubic,-86.283,-69.523,+18
tf_efficientnet_b0.aa_in1k,5.053,94.947,28.773,71.227,5.29,224,0.875,bicubic,-87.197,-69.227,-20
tf_mixnet_m.in1k,5.053,94.947,28.187,71.813,5.01,224,0.875,bicubic,-87.247,-69.703,-24
res2net50_14w_8s.in1k,5.040,94.960,28.720,71.280,25.06,224,0.875,bilinear,-87.730,-69.450,-55
regnetx_004_tv.tv2_in1k,5.000,95.000,27.560,72.440,5.50,224,0.965,bicubic,-85.640,-70.030,+38
hardcorenas_b.miil_green_in1k,4.947,95.053,28.053,71.947,5.18,224,0.875,bilinear,-86.813,-69.987,+4
mobilenetv3_rw.rmsp_in1k,4.933,95.067,29.853,70.147,5.48,224,0.875,bicubic,-86.277,-67.807,+14
mixnet_s.ft_in1k,4.933,95.067,28.560,71.440,4.13,224,0.875,bicubic,-86.897,-69.460,0
hardcorenas_a.miil_green_in1k,4.893,95.107,28.107,71.893,5.26,224,0.875,bilinear,-86.457,-69.743,+9
resnet50c.gluon_in1k,4.893,95.107,28.080,71.920,25.58,224,0.875,bicubic,-88.137,-70.320,-82
regnetx_032.pycls_in1k,4.880,95.120,30.227,69.773,15.30,224,0.875,bicubic,-88.230,-68.163,-93
xcit_nano_12_p16_224.fb_in1k,4.853,95.147,25.467,74.533,3.05,224,1.000,bicubic,-83.767,-71.323,+65
resnext50_32x4d.tv_in1k,4.827,95.173,30.267,69.733,25.03,224,0.875,bilinear,-87.923,-67.703,-63
densenet161.tv_in1k,4.733,95.267,29.547,70.453,28.68,224,0.875,bicubic,-87.747,-68.753,-44
resnet101.tv_in1k,4.707,95.293,29.333,70.667,44.55,224,0.875,bilinear,-88.103,-68.917,-70
SelecSls42b.in1k,4.693,95.307,28.560,71.440,32.46,224,0.875,bicubic,-87.597,-69.550,-36
tf_efficientnet_lite1.in1k,4.600,95.400,28.333,71.667,5.42,240,0.882,bicubic,-88.020,-69.727,-58
mobilenetv2_120d.ra_in1k,4.547,95.453,29.320,70.680,5.83,224,0.875,bicubic,-87.853,-68.830,-42
tf_efficientnet_b0.in1k,4.427,95.573,26.680,73.320,5.29,224,0.875,bicubic,-87.643,-71.240,-28
pvt_v2_b0.in1k,4.347,95.653,25.960,74.040,3.67,224,0.900,bicubic,-84.433,-70.900,+54
vit_base_patch32_224.sam_in1k,4.333,95.667,24.387,75.613,88.22,224,0.900,bicubic,-85.407,-72.613,+37
resnet50.am_in1k,4.267,95.733,28.613,71.387,25.56,224,0.875,bicubic,-89.703,-70.017,-196
edgenext_xx_small.in1k,4.267,95.733,24.093,75.907,1.33,288,1.000,bicubic,-84.623,-73.017,+51
tinynet_b.in1k,4.200,95.800,26.787,73.213,3.73,188,0.875,bicubic,-86.720,-70.873,+7
efficientnet_es_pruned.in1k,4.200,95.800,26.467,73.533,5.44,224,0.875,bicubic,-86.970,-71.273,+1
densenet201.tv_in1k,4.133,95.867,27.547,72.453,20.01,224,0.875,bicubic,-88.607,-70.683,-74
resnet50.gluon_in1k,4.120,95.880,26.933,73.067,25.56,224,0.875,bicubic,-88.420,-71.237,-63
fbnetc_100.rmsp_in1k,4.120,95.880,25.893,74.107,5.57,224,0.875,bilinear,-86.610,-71.317,+11
semnasnet_100.rmsp_in1k,3.947,96.053,26.920,73.080,3.89,224,0.875,bicubic,-87.363,-70.640,-10
mobilevitv2_050.cvnets_in1k,3.947,96.053,23.933,76.067,1.37,256,0.888,bicubic,-84.243,-73.057,+51
resnet26.bt_in1k,3.933,96.067,28.200,71.800,16.00,288,0.950,bicubic,-88.057,-69.820,-37
repvgg_a2.rvgg_in1k,3.933,96.067,27.227,72.773,28.21,224,0.875,bilinear,-88.007,-70.873,-30
dpn68.mx_in1k,3.893,96.107,25.693,74.307,12.61,224,0.875,bicubic,-88.097,-72.527,-37
tf_mixnet_s.in1k,3.880,96.120,25.280,74.720,4.13,224,0.875,bicubic,-87.640,-72.340,-19
semnasnet_075.rmsp_in1k,3.853,96.147,27.080,72.920,2.91,224,0.875,bicubic,-86.237,-70.360,+16
tf_efficientnet_es.in1k,3.827,96.173,26.147,73.853,5.44,224,0.875,bicubic,-88.143,-71.733,-39
mobilevit_xxs.cvnets_in1k,3.827,96.173,21.787,78.213,1.27,256,0.900,bicubic,-83.323,-71.733,+49
resnet18d.ra2_in1k,3.813,96.187,25.987,74.013,11.71,288,0.950,bicubic,-86.467,-71.573,+10
regnety_008.pycls_in1k,3.800,96.200,27.147,72.853,6.26,224,0.875,bicubic,-87.930,-71.033,-28
dla60.in1k,3.787,96.213,27.973,72.027,22.04,224,0.875,bilinear,-88.413,-70.137,-56
resnet18.fb_ssl_yfcc100m_ft_in1k,3.747,96.253,25.387,74.613,11.69,224,0.875,bilinear,-86.463,-72.153,+9
mobilenetv2_140.ra_in1k,3.720,96.280,26.720,73.280,6.11,224,0.875,bicubic,-88.120,-71.140,-37
densenet169.tv_in1k,3.707,96.293,25.587,74.413,14.15,224,0.875,bicubic,-88.233,-72.553,-41
resnet18.a1_in1k,3.707,96.293,22.960,77.040,11.69,288,1.000,bicubic,-85.973,-74.140,+16
regnetx_016.pycls_in1k,3.613,96.387,26.320,73.680,9.19,224,0.875,bicubic,-88.567,-71.880,-59
spnasnet_100.rmsp_in1k,3.587,96.413,24.253,75.747,4.42,224,0.875,bilinear,-86.743,-72.937,+1
res2net50_48w_2s.in1k,3.560,96.440,26.613,73.387,25.29,224,0.875,bilinear,-88.990,-71.467,-83
tf_mobilenetv3_large_100.in1k,3.560,96.440,25.120,74.880,5.48,224,0.875,bilinear,-87.680,-72.540,-27
regnety_006.pycls_in1k,3.453,96.547,24.920,75.080,6.06,224,0.875,bicubic,-87.937,-72.780,-32
resnet34.a3_in1k,3.373,96.627,23.387,76.613,21.80,224,0.950,bicubic,-86.567,-73.793,+7
legacy_seresnet34.in1k,3.347,96.653,23.813,76.187,21.96,224,0.875,bilinear,-87.553,-73.767,-18
resnet18.a2_in1k,3.267,96.733,22.373,77.627,11.69,288,1.000,bicubic,-86.303,-74.587,+13
efficientnet_lite0.ra_in1k,3.240,96.760,25.933,74.067,4.65,224,0.875,bicubic,-87.880,-71.697,-28
dla34.in1k,3.240,96.760,23.587,76.413,15.74,224,0.875,bilinear,-87.520,-74.063,-16
ghostnet_100.in1k,3.227,96.773,24.813,75.187,5.18,224,0.875,bilinear,-86.803,-72.557,-1
regnety_004.pycls_in1k,3.187,96.813,22.680,77.320,4.34,224,0.875,bicubic,-87.303,-74.850,-11
mobilenetv2_110d.ra_in1k,3.173,96.827,24.587,75.413,4.52,224,0.875,bicubic,-87.797,-73.043,-26
mnasnet_100.rmsp_in1k,3.120,96.880,24.240,75.760,4.38,224,0.875,bicubic,-87.380,-73.230,-14
tinynet_c.in1k,3.120,96.880,21.533,78.467,2.46,184,0.875,bicubic,-84.670,-74.837,+22
tf_efficientnet_lite0.in1k,3.040,96.960,22.907,77.093,4.65,224,0.875,bicubic,-88.010,-74.673,-32
skresnet18.ra_in1k,3.027,96.973,22.827,77.173,11.96,224,0.875,bicubic,-86.633,-74.403,0
vgg19_bn.tv_in1k,2.947,97.053,23.440,76.560,143.68,224,0.875,bilinear,-87.133,-74.140,-9
tinynet_d.in1k,2.853,97.147,17.787,82.213,2.34,152,0.875,bicubic,-81.867,-77.383,+33
tf_mobilenetv3_large_075.in1k,2.840,97.160,21.547,78.453,3.99,224,0.875,bilinear,-86.820,-75.643,-2
resnet14t.c3_in1k,2.760,97.240,20.213,79.787,10.08,224,0.950,bicubic,-86.240,-76.517,+4
hrnet_w18_small_v2.ms_in1k,2.720,97.280,23.707,76.293,15.60,224,0.875,bilinear,-88.480,-74.193,-43
regnetx_008.pycls_in1k,2.667,97.333,22.440,77.560,7.26,224,0.875,bicubic,-88.383,-75.280,-40
vgg16_bn.tv_in1k,2.653,97.347,23.800,76.200,138.37,224,0.875,bilinear,-87.437,-73.570,-17
resnet34.gluon_in1k,2.653,97.347,21.693,78.307,21.80,224,0.875,bicubic,-88.317,-75.847,-38
lcnet_100.ra2_in1k,2.627,97.373,20.760,79.240,2.95,224,0.875,bicubic,-86.123,-76.220,+6
vgg16.tv_in1k,2.627,97.373,20.360,79.640,138.36,224,0.875,bilinear,-85.933,-76.440,+7
repvgg_b0.rvgg_in1k,2.560,97.440,24.013,75.987,15.82,224,0.875,bilinear,-88.840,-73.987,-56
densenet121.tv_in1k,2.547,97.453,22.653,77.347,7.98,224,0.875,bicubic,-88.343,-75.057,-39
regnetx_006.pycls_in1k,2.533,97.467,20.627,79.373,6.20,224,0.875,bicubic,-87.817,-76.803,-28
legacy_seresnet18.in1k,2.480,97.520,20.093,79.907,11.78,224,0.875,bicubic,-86.410,-76.887,-3
lcnet_075.ra2_in1k,2.333,97.667,17.187,82.813,2.36,224,0.875,bicubic,-83.637,-78.493,+17
mobilenetv3_small_075.lamb_in1k,2.307,97.693,15.893,84.107,2.04,224,0.875,bicubic,-80.723,-78.207,+25
resnet18.a3_in1k,2.227,97.773,17.760,82.240,11.69,224,0.950,bicubic,-84.223,-78.120,+11
mobilenetv2_100.ra_in1k,2.133,97.867,19.933,80.067,3.50,224,0.875,bicubic,-87.447,-77.207,-15
regnety_002.pycls_in1k,2.120,97.880,18.907,81.093,3.16,224,0.875,bicubic,-85.250,-77.703,+4
vgg19.tv_in1k,2.107,97.893,20.760,79.240,143.67,224,0.875,bilinear,-86.943,-76.110,-13
vgg13_bn.tv_in1k,2.093,97.907,20.333,79.667,133.05,224,0.875,bilinear,-86.657,-76.387,-7
tf_mobilenetv3_small_100.in1k,2.013,97.987,15.827,84.173,2.54,224,0.875,bilinear,-83.177,-79.943,+12
mobilenetv3_small_100.lamb_in1k,2.000,98.000,17.080,82.920,2.54,224,0.875,bicubic,-83.220,-78.570,+10
tf_mobilenetv3_small_075.in1k,1.987,98.013,14.840,85.160,2.04,224,0.875,bilinear,-81.513,-80.000,+16
regnetx_004.pycls_in1k,1.933,98.067,19.173,80.827,5.16,224,0.875,bicubic,-86.957,-77.527,-15
resnet34.tv_in1k,1.853,98.147,20.053,79.947,21.80,224,0.875,bilinear,-88.097,-77.287,-32
tinynet_e.in1k,1.853,98.147,14.013,85.987,2.04,106,0.875,bicubic,-77.067,-78.527,+17
vgg13.tv_in1k,1.840,98.160,18.013,81.987,133.05,224,0.875,bilinear,-85.200,-78.317,-2
lcnet_050.ra2_in1k,1.827,98.173,13.880,86.120,1.88,224,0.875,bicubic,-79.953,-79.860,+13
mobilenetv3_small_050.lamb_in1k,1.813,98.187,12.533,87.467,1.59,224,0.875,bicubic,-75.207,-78.767,+15
mnasnet_small.lamb_in1k,1.773,98.227,15.080,84.920,2.03,224,0.875,bicubic,-82.647,-80.110,+5
dla46x_c.in1k,1.747,98.253,16.427,83.573,1.07,224,0.875,bilinear,-82.453,-78.843,+5
vgg11_bn.tv_in1k,1.720,98.280,18.093,81.907,132.87,224,0.875,bilinear,-85.780,-78.727,-12
dla60x_c.in1k,1.613,98.387,18.000,82.000,1.32,224,0.875,bilinear,-84.667,-78.160,-5
tf_mobilenetv3_large_minimal_100.in1k,1.613,98.387,17.120,82.880,3.92,224,0.875,bilinear,-87.337,-79.740,-27
mobilenetv2_050.lamb_in1k,1.613,98.387,14.187,85.813,1.97,224,0.875,bicubic,-82.287,-80.543,+3
resnet10t.c3_in1k,1.600,98.400,16.040,83.960,5.44,224,0.950,bicubic,-84.620,-79.700,-7
vgg11.tv_in1k,1.560,98.440,16.187,83.813,132.86,224,0.875,bilinear,-85.020,-80.093,-11
resnet18.gluon_in1k,1.547,98.453,16.640,83.360,11.69,224,0.875,bicubic,-86.823,-80.030,-21
hrnet_w18_small.ms_in1k,1.533,98.467,18.093,81.907,13.19,224,0.875,bilinear,-87.517,-79.027,-34
dla46_c.in1k,1.493,98.507,15.213,84.787,1.30,224,0.875,bilinear,-82.117,-79.737,-2
regnetx_002.pycls_in1k,1.373,98.627,15.067,84.933,2.68,224,0.875,bicubic,-84.767,-80.893,-11
resnet18.tv_in1k,1.147,98.853,16.227,83.773,11.69,224,0.875,bilinear,-86.233,-80.063,-21
tf_mobilenetv3_small_minimal_100.in1k,1.040,98.960,11.493,88.507,2.04,224,0.875,bilinear,-80.360,-82.187,-1
resnet50.tv_in1k,0.000,100.000,14.440,85.560,25.56,224,0.875,bilinear,-91.870,-83.600,-102
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-r-clean.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,98.160,1.840,99.880,0.120,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,98.030,1.970,99.890,0.110,305.08,448,1.000,bicubic
eva_giant_patch14_560.m30m_ft_in22k_in1k,98.000,2.000,99.860,0.140,"1,014.45",560,1.000,bicubic
eva_giant_patch14_336.m30m_ft_in22k_in1k,97.990,2.010,99.900,0.100,"1,013.01",336,1.000,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_384,97.870,2.130,99.910,0.090,660.29,384,1.000,bicubic
eva_large_patch14_336.in22k_ft_in22k_in1k,97.860,2.140,99.880,0.120,304.53,336,1.000,bicubic
eva02_large_patch14_448.mim_in22k_ft_in1k,97.860,2.140,99.800,0.200,305.08,448,1.000,bicubic
eva_giant_patch14_336.clip_ft_in1k,97.860,2.140,99.790,0.210,"1,013.01",336,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in1k,97.830,2.170,99.820,0.180,305.08,448,1.000,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_512,97.810,2.190,99.860,0.140,660.29,512,1.000,bicubic
eva_large_patch14_336.in22k_ft_in1k,97.810,2.190,99.840,0.160,304.53,336,1.000,bicubic
beit_large_patch16_384.in22k_ft_in22k_in1k,97.810,2.190,99.790,0.210,305.00,384,1.000,bicubic
tf_efficientnet_l2.ns_jft_in1k,97.780,2.220,99.890,0.110,480.31,800,0.960,bicubic
regnety_1280.swag_ft_in1k,97.780,2.220,99.860,0.140,644.81,384,1.000,bicubic
beit_large_patch16_512.in22k_ft_in22k_in1k,97.780,2.220,99.820,0.180,305.67,512,1.000,bicubic
maxvit_base_tf_512.in21k_ft_in1k,97.760,2.240,99.860,0.140,119.88,512,1.000,bicubic
maxvit_xlarge_tf_512.in21k_ft_in1k,97.760,2.240,99.820,0.180,475.77,512,1.000,bicubic
tf_efficientnet_l2.ns_jft_in1k_475,97.750,2.250,99.820,0.180,480.31,475,0.936,bicubic
convnext_xxlarge.clip_laion2b_soup_ft_in1k,97.750,2.250,99.810,0.190,846.47,256,1.000,bicubic
beitv2_large_patch16_224.in1k_ft_in22k_in1k,97.750,2.250,99.790,0.210,304.43,224,0.950,bicubic
maxvit_xlarge_tf_384.in21k_ft_in1k,97.740,2.260,99.850,0.150,475.32,384,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in1k,97.720,2.280,99.760,0.240,87.12,448,1.000,bicubic
maxvit_large_tf_512.in21k_ft_in1k,97.670,2.330,99.730,0.270,212.33,512,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k_384,97.660,2.340,99.860,0.140,98.75,384,1.000,bicubic
maxvit_large_tf_384.in21k_ft_in1k,97.660,2.340,99.820,0.180,212.03,384,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k_384,97.630,2.370,99.800,0.200,197.96,384,1.000,bicubic
eva_large_patch14_196.in22k_ft_in22k_in1k,97.620,2.380,99.810,0.190,304.14,196,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,97.610,2.390,99.820,0.180,87.12,448,1.000,bicubic
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,97.610,2.390,99.780,0.220,632.46,336,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in12k_in1k,97.610,2.390,99.730,0.270,304.20,224,1.000,bicubic
vit_large_patch14_clip_336.openai_ft_in12k_in1k,97.600,2.400,99.730,0.270,304.53,336,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k_384,97.590,2.410,99.770,0.230,350.20,384,1.000,bicubic
deit3_large_patch16_384.fb_in22k_ft_in1k,97.580,2.420,99.710,0.290,304.76,384,1.000,bicubic
eva_giant_patch14_224.clip_ft_in1k,97.570,2.430,99.710,0.290,"1,012.56",224,0.900,bicubic
maxvit_base_tf_384.in21k_ft_in1k,97.560,2.440,99.760,0.240,119.65,384,1.000,bicubic
eva_large_patch14_196.in22k_ft_in1k,97.520,2.480,99.790,0.210,304.14,196,1.000,bicubic
convformer_b36.sail_in22k_ft_in1k_384,97.490,2.510,99.760,0.240,99.88,384,1.000,bicubic
beit_large_patch16_224.in22k_ft_in22k_in1k,97.480,2.520,99.690,0.310,304.43,224,0.900,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,97.470,2.530,99.760,0.240,200.13,384,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,97.460,2.540,99.780,0.220,304.53,336,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k,97.450,2.550,99.820,0.180,350.20,288,1.000,bicubic
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,97.450,2.550,99.760,0.240,116.09,384,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in1k,97.440,2.560,99.680,0.320,304.20,224,1.000,bicubic
vit_large_patch16_384.augreg_in21k_ft_in1k,97.410,2.590,99.780,0.220,304.72,384,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,97.390,2.610,99.740,0.260,304.20,224,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,97.390,2.610,99.730,0.270,200.13,384,1.000,bicubic
regnety_1280.swag_lc_in1k,97.390,2.610,99.730,0.270,644.81,224,0.965,bicubic
regnety_320.swag_ft_in1k,97.380,2.620,99.760,0.240,145.05,384,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k_384,97.380,2.620,99.720,0.280,88.72,384,1.000,bicubic
caformer_m36.sail_in22k_ft_in1k_384,97.370,2.630,99.790,0.210,56.20,384,1.000,bicubic
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,97.370,2.630,99.700,0.300,73.88,384,1.000,bicubic
convformer_m36.sail_in22k_ft_in1k_384,97.370,2.630,99.680,0.320,57.05,384,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k,97.360,2.640,99.830,0.170,98.75,224,1.000,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,97.360,2.640,99.800,0.200,632.05,224,1.000,bicubic
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,97.340,2.660,99.690,0.310,116.14,384,1.000,bicubic
tf_efficientnetv2_xl.in21k_ft_in1k,97.330,2.670,99.600,0.400,208.12,512,1.000,bicubic
beit_base_patch16_384.in22k_ft_in22k_in1k,97.320,2.680,99.720,0.280,86.74,384,1.000,bicubic
tf_efficientnetv2_l.in21k_ft_in1k,97.320,2.680,99.640,0.360,118.52,480,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k,97.310,2.690,99.760,0.240,197.96,288,1.000,bicubic
beitv2_large_patch16_224.in1k_ft_in1k,97.310,2.690,99.740,0.260,304.43,224,0.950,bicubic
deit3_large_patch16_224.fb_in22k_ft_in1k,97.310,2.690,99.680,0.320,304.37,224,1.000,bicubic
convnext_large.fb_in22k_ft_in1k_384,97.300,2.700,99.760,0.240,197.77,384,1.000,bicubic
volo_d5_512.sail_in1k,97.300,2.700,99.760,0.240,296.09,512,1.150,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,97.290,2.710,99.780,0.220,93.59,320,1.000,bicubic
caformer_s36.sail_in22k_ft_in1k_384,97.290,2.710,99.720,0.280,39.30,384,1.000,bicubic
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,97.280,2.720,99.780,0.220,196.74,384,1.000,bicubic
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,97.270,2.730,99.790,0.210,87.92,384,1.000,bicubic
convformer_b36.sail_in22k_ft_in1k,97.260,2.740,99.750,0.250,99.88,224,1.000,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,97.260,2.740,99.740,0.260,200.13,320,1.000,bicubic
convnext_base.fb_in22k_ft_in1k_384,97.260,2.740,99.710,0.290,88.59,384,1.000,bicubic
convnextv2_huge.fcmae_ft_in1k,97.250,2.750,99.720,0.280,660.29,288,1.000,bicubic
deit3_huge_patch14_224.fb_in22k_ft_in1k,97.250,2.750,99.720,0.280,632.13,224,1.000,bicubic
volo_d5_448.sail_in1k,97.240,2.760,99.740,0.260,295.91,448,1.150,bicubic
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,97.240,2.760,99.710,0.290,196.74,256,0.900,bicubic
deit3_base_patch16_384.fb_in22k_ft_in1k,97.240,2.760,99.670,0.330,86.88,384,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in1k,97.230,2.770,99.720,0.280,304.53,336,1.000,bicubic
convnext_large.fb_in22k_ft_in1k,97.220,2.780,99.730,0.270,197.77,288,1.000,bicubic
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,97.220,2.780,99.700,0.300,86.86,384,1.000,bicubic
convnext_base.fb_in22k_ft_in1k,97.200,2.800,99.760,0.240,88.59,288,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k,97.200,2.800,99.760,0.240,88.72,288,1.000,bicubic
tf_efficientnet_b7.ns_jft_in1k,97.190,2.810,99.700,0.300,66.35,600,0.949,bicubic
maxvit_small_tf_512.in1k,97.190,2.810,99.620,0.380,69.13,512,1.000,bicubic
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,97.180,2.820,99.650,0.350,73.88,224,0.950,bicubic
regnety_160.swag_ft_in1k,97.170,2.830,99.780,0.220,83.59,384,1.000,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k,97.170,2.830,99.740,0.260,93.59,288,1.000,bicubic
swin_large_patch4_window12_384.ms_in22k_ft_in1k,97.170,2.830,99.680,0.320,196.74,384,1.000,bicubic
maxvit_base_tf_512.in1k,97.170,2.830,99.640,0.360,119.88,512,1.000,bicubic
caformer_b36.sail_in1k_384,97.160,2.840,99.610,0.390,98.75,384,1.000,bicubic
swin_base_patch4_window12_384.ms_in22k_ft_in1k,97.130,2.870,99.780,0.220,87.90,384,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,97.130,2.870,99.720,0.280,200.13,256,1.000,bicubic
vit_base_patch16_clip_384.openai_ft_in12k_in1k,97.120,2.880,99.640,0.360,86.86,384,0.950,bicubic
maxvit_base_tf_384.in1k,97.120,2.880,99.570,0.430,119.65,384,1.000,bicubic
convnext_small.fb_in22k_ft_in1k_384,97.110,2.890,99.640,0.360,50.22,384,1.000,bicubic
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,97.110,2.890,99.600,0.400,116.14,224,0.950,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in1k,97.100,2.900,99.690,0.310,632.05,224,1.000,bicubic
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,97.090,2.910,99.680,0.320,116.09,224,0.950,bicubic
vit_base_patch8_224.augreg_in21k_ft_in1k,97.090,2.910,99.610,0.390,86.58,224,0.900,bicubic
volo_d4_448.sail_in1k,97.070,2.930,99.750,0.250,193.41,448,1.150,bicubic
convformer_m36.sail_in22k_ft_in1k,97.070,2.930,99.630,0.370,57.05,224,1.000,bicubic
convformer_s36.sail_in22k_ft_in1k_384,97.060,2.940,99.710,0.290,40.01,384,1.000,bicubic
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,97.050,2.950,99.660,0.340,87.92,256,0.900,bicubic
maxvit_large_tf_512.in1k,97.050,2.950,99.590,0.410,212.33,512,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,97.040,2.960,99.670,0.330,88.59,384,1.000,bicubic
caformer_m36.sail_in1k_384,97.030,2.970,99.710,0.290,56.20,384,1.000,bicubic
volo_d3_448.sail_in1k,97.030,2.970,99.680,0.320,86.63,448,1.000,bicubic
dm_nfnet_f5.dm_in1k,97.030,2.970,99.670,0.330,377.21,544,0.954,bicubic
caformer_m36.sail_in22k_ft_in1k,97.020,2.980,99.730,0.270,56.20,224,1.000,bicubic
tf_efficientnet_b6.ns_jft_in1k,97.020,2.980,99.710,0.290,43.04,528,0.942,bicubic
vit_base_patch16_384.augreg_in21k_ft_in1k,97.020,2.980,99.710,0.290,86.86,384,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in1k,97.020,2.980,99.670,0.330,304.20,224,1.000,bicubic
tf_efficientnetv2_m.in21k_ft_in1k,97.000,3.000,99.630,0.370,54.14,480,1.000,bicubic
convnext_small.in12k_ft_in1k_384,96.990,3.010,99.660,0.340,50.22,384,1.000,bicubic
coatnet_2_rw_224.sw_in12k_ft_in1k,96.990,3.010,99.650,0.350,73.87,224,0.950,bicubic
dm_nfnet_f6.dm_in1k,96.970,3.030,99.760,0.240,438.36,576,0.956,bicubic
maxvit_tiny_tf_512.in1k,96.970,3.030,99.670,0.330,31.05,512,1.000,bicubic
vit_large_r50_s32_384.augreg_in21k_ft_in1k,96.950,3.050,99.710,0.290,329.09,384,1.000,bicubic
vit_base_patch8_224.augreg2_in21k_ft_in1k,96.950,3.050,99.640,0.360,86.58,224,0.900,bicubic
dm_nfnet_f4.dm_in1k,96.950,3.050,99.630,0.370,316.07,512,0.951,bicubic
swin_large_patch4_window7_224.ms_in22k_ft_in1k,96.940,3.060,99.670,0.330,196.53,224,0.900,bicubic
xcit_large_24_p16_384.fb_dist_in1k,96.940,3.060,99.510,0.490,189.10,384,1.000,bicubic
maxvit_large_tf_384.in1k,96.930,3.070,99.570,0.430,212.03,384,1.000,bicubic
beitv2_base_patch16_224.in1k_ft_in22k_in1k,96.910,3.090,99.730,0.270,86.53,224,0.900,bicubic
vit_base_patch16_clip_384.laion2b_ft_in1k,96.900,3.100,99.670,0.330,86.86,384,1.000,bicubic
caformer_s36.sail_in1k_384,96.880,3.120,99.670,0.330,39.30,384,1.000,bicubic
volo_d5_224.sail_in1k,96.880,3.120,99.670,0.330,295.46,224,0.960,bicubic
resnetv2_152x4_bit.goog_in21k_ft_in1k,96.880,3.120,99.660,0.340,936.53,480,1.000,bilinear
cait_m48_448.fb_dist_in1k,96.880,3.120,99.620,0.380,356.46,448,1.000,bicubic
convformer_b36.sail_in1k_384,96.870,3.130,99.650,0.350,99.88,384,1.000,bicubic
tf_efficientnet_b5.ns_jft_in1k,96.870,3.130,99.640,0.360,30.39,456,0.934,bicubic
convnext_base.clip_laiona_augreg_ft_in1k_384,96.860,3.140,99.690,0.310,88.59,384,1.000,bicubic
deit3_base_patch16_224.fb_in22k_ft_in1k,96.860,3.140,99.620,0.380,86.59,224,1.000,bicubic
deit3_large_patch16_384.fb_in1k,96.850,3.150,99.620,0.380,304.76,384,1.000,bicubic
cait_m36_384.fb_dist_in1k,96.840,3.160,99.660,0.340,271.22,384,1.000,bicubic
convnextv2_large.fcmae_ft_in1k,96.830,3.170,99.760,0.240,197.96,288,1.000,bicubic
regnety_160.sw_in12k_ft_in1k,96.820,3.180,99.690,0.310,83.59,288,1.000,bicubic
caformer_s36.sail_in22k_ft_in1k,96.820,3.180,99.620,0.380,39.30,224,1.000,bicubic
regnety_160.lion_in12k_ft_in1k,96.810,3.190,99.710,0.290,83.59,288,1.000,bicubic
vit_base_patch16_clip_384.openai_ft_in1k,96.810,3.190,99.660,0.340,86.86,384,1.000,bicubic
xcit_small_24_p8_384.fb_dist_in1k,96.810,3.190,99.630,0.370,47.63,384,1.000,bicubic
convnext_small.fb_in22k_ft_in1k,96.810,3.190,99.510,0.490,50.22,288,1.000,bicubic
convformer_s18.sail_in22k_ft_in1k_384,96.790,3.210,99.710,0.290,26.77,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,96.790,3.210,99.680,0.320,88.59,256,1.000,bicubic
regnety_320.swag_lc_in1k,96.780,3.220,99.730,0.270,145.05,224,0.965,bicubic
volo_d4_224.sail_in1k,96.780,3.220,99.670,0.330,192.96,224,0.960,bicubic
convformer_m36.sail_in1k_384,96.780,3.220,99.620,0.380,57.05,384,1.000,bicubic
flexivit_large.1200ep_in1k,96.780,3.220,99.610,0.390,304.36,240,0.950,bicubic
xcit_medium_24_p8_384.fb_dist_in1k,96.770,3.230,99.620,0.380,84.32,384,1.000,bicubic
efficientnet_b5.sw_in12k_ft_in1k,96.770,3.230,99.600,0.400,30.39,448,1.000,bicubic
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,96.770,3.230,99.530,0.470,468.53,224,0.875,bilinear
xcit_large_24_p8_384.fb_dist_in1k,96.760,3.240,99.560,0.440,188.93,384,1.000,bicubic
maxvit_small_tf_384.in1k,96.750,3.250,99.600,0.400,69.02,384,1.000,bicubic
beitv2_base_patch16_224.in1k_ft_in1k,96.750,3.250,99.540,0.460,86.53,224,0.900,bicubic
tf_efficientnetv2_l.in1k,96.740,3.260,99.550,0.450,118.52,480,1.000,bicubic
flexivit_large.600ep_in1k,96.730,3.270,99.560,0.440,304.36,240,0.950,bicubic
tf_efficientnet_b4.ns_jft_in1k,96.710,3.290,99.640,0.360,19.34,380,0.922,bicubic
volo_d2_384.sail_in1k,96.710,3.290,99.600,0.400,58.87,384,1.000,bicubic
vit_large_patch16_224.augreg_in21k_ft_in1k,96.700,3.300,99.650,0.350,304.33,224,0.900,bicubic
flexivit_large.300ep_in1k,96.700,3.300,99.580,0.420,304.36,240,0.950,bicubic
convformer_s36.sail_in1k_384,96.700,3.300,99.570,0.430,40.01,384,1.000,bicubic
tf_efficientnet_b8.ra_in1k,96.700,3.300,99.530,0.470,87.41,672,0.954,bicubic
eva02_small_patch14_336.mim_in22k_ft_in1k,96.690,3.310,99.610,0.390,22.13,336,1.000,bicubic
xcit_medium_24_p16_384.fb_dist_in1k,96.690,3.310,99.600,0.400,84.40,384,1.000,bicubic
swin_base_patch4_window7_224.ms_in22k_ft_in1k,96.680,3.320,99.670,0.330,87.77,224,0.900,bicubic
deit3_small_patch16_384.fb_in22k_ft_in1k,96.670,3.330,99.640,0.360,22.21,384,1.000,bicubic
beit_base_patch16_224.in22k_ft_in22k_in1k,96.660,3.340,99.660,0.340,86.53,224,0.900,bicubic
cait_s36_384.fb_dist_in1k,96.630,3.370,99.610,0.390,68.37,384,1.000,bicubic
xcit_large_24_p8_224.fb_dist_in1k,96.630,3.370,99.460,0.540,188.93,224,1.000,bicubic
dm_nfnet_f3.dm_in1k,96.620,3.380,99.630,0.370,254.92,416,0.940,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k_384,96.620,3.380,99.580,0.420,28.64,384,1.000,bicubic
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,96.620,3.380,99.560,0.440,86.57,224,0.950,bicubic
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,96.610,3.390,99.480,0.520,88.30,384,1.000,bicubic
regnetz_e8.ra3_in1k,96.600,3.400,99.620,0.380,57.70,320,1.000,bicubic
convnext_small.in12k_ft_in1k,96.600,3.400,99.580,0.420,50.22,288,1.000,bicubic
maxvit_tiny_tf_384.in1k,96.590,3.410,99.560,0.440,30.98,384,1.000,bicubic
deit3_huge_patch14_224.fb_in1k,96.580,3.420,99.520,0.480,632.13,224,0.900,bicubic
cait_s24_384.fb_dist_in1k,96.570,3.430,99.550,0.450,47.06,384,1.000,bicubic
tf_efficientnet_b7.ra_in1k,96.570,3.430,99.520,0.480,66.35,600,0.949,bicubic
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,96.570,3.430,99.520,0.480,88.34,448,1.000,bicubic
coat_lite_medium_384.in1k,96.570,3.430,99.470,0.530,44.57,384,1.000,bicubic
regnety_120.sw_in12k_ft_in1k,96.560,3.440,99.680,0.320,51.82,288,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in1k,96.560,3.440,99.650,0.350,88.59,256,1.000,bicubic
convnext_tiny.in12k_ft_in1k_384,96.560,3.440,99.630,0.370,28.59,384,1.000,bicubic
xcit_small_24_p8_224.fb_dist_in1k,96.550,3.450,99.560,0.440,47.63,224,1.000,bicubic
tf_efficientnet_b8.ap_in1k,96.550,3.450,99.540,0.460,87.41,672,0.954,bicubic
hrnet_w48_ssld.paddle_in1k,96.540,3.460,99.640,0.360,77.47,288,1.000,bilinear
caformer_s18.sail_in22k_ft_in1k_384,96.530,3.470,99.580,0.420,26.34,384,1.000,bicubic
regnety_2560.seer_ft_in1k,96.530,3.470,99.520,0.480,"1,282.60",384,1.000,bicubic
xcit_medium_24_p8_224.fb_dist_in1k,96.530,3.470,99.510,0.490,84.32,224,1.000,bicubic
resnetv2_152x2_bit.goog_in21k_ft_in1k,96.520,3.480,99.590,0.410,236.34,448,1.000,bilinear
dm_nfnet_f2.dm_in1k,96.520,3.480,99.570,0.430,193.78,352,0.920,bicubic
deit_base_distilled_patch16_384.fb_in1k,96.510,3.490,99.590,0.410,87.63,384,1.000,bicubic
vit_base_patch16_224.augreg2_in21k_ft_in1k,96.510,3.490,99.560,0.440,86.57,224,0.900,bicubic
vit_base_patch16_clip_224.openai_ft_in12k_in1k,96.510,3.490,99.550,0.450,86.57,224,0.950,bicubic
convformer_s36.sail_in22k_ft_in1k,96.500,3.500,99.630,0.370,40.01,224,1.000,bicubic
caformer_b36.sail_in1k,96.500,3.500,99.460,0.540,98.75,224,1.000,bicubic
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,96.490,3.510,99.620,0.380,39.03,384,0.950,bicubic
tf_efficientnetv2_m.in1k,96.480,3.520,99.610,0.390,54.14,480,1.000,bicubic
volo_d1_384.sail_in1k,96.480,3.520,99.550,0.450,26.78,384,1.000,bicubic
convnextv2_base.fcmae_ft_in1k,96.480,3.520,99.520,0.480,88.72,288,1.000,bicubic
tf_efficientnetv2_s.in21k_ft_in1k,96.470,3.530,99.570,0.430,21.46,384,1.000,bicubic
xcit_small_12_p8_384.fb_dist_in1k,96.470,3.530,99.490,0.510,26.21,384,1.000,bicubic
regnety_160.swag_lc_in1k,96.450,3.550,99.750,0.250,83.59,224,0.965,bicubic
vit_base_r50_s16_384.orig_in21k_ft_in1k,96.450,3.550,99.660,0.340,98.95,384,1.000,bicubic
eca_nfnet_l2.ra3_in1k,96.450,3.550,99.610,0.390,56.72,384,1.000,bicubic
ecaresnet269d.ra2_in1k,96.450,3.550,99.610,0.390,102.09,352,1.000,bicubic
seresnextaa101d_32x8d.ah_in1k,96.440,3.560,99.510,0.490,93.59,288,1.000,bicubic
volo_d3_224.sail_in1k,96.430,3.570,99.630,0.370,86.33,224,0.960,bicubic
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,96.430,3.570,99.540,0.460,194.03,224,0.875,bilinear
volo_d2_224.sail_in1k,96.420,3.580,99.500,0.500,58.68,224,0.960,bicubic
vit_base_patch32_clip_384.openai_ft_in12k_in1k,96.420,3.580,99.460,0.540,88.30,384,0.950,bicubic
caformer_s18.sail_in1k_384,96.410,3.590,99.560,0.440,26.34,384,1.000,bicubic
caformer_m36.sail_in1k,96.410,3.590,99.530,0.470,56.20,224,1.000,bicubic
resnetrs420.tf_in1k,96.400,3.600,99.540,0.460,191.89,416,1.000,bicubic
convnext_large.fb_in1k,96.400,3.600,99.530,0.470,197.77,288,1.000,bicubic
mvitv2_large.fb_in1k,96.400,3.600,99.450,0.550,217.99,224,0.900,bicubic
swin_base_patch4_window12_384.ms_in1k,96.380,3.620,99.420,0.580,87.90,384,1.000,bicubic
tf_efficientnet_b6.ap_in1k,96.370,3.630,99.550,0.450,43.04,528,0.942,bicubic
seresnext101d_32x8d.ah_in1k,96.360,3.640,99.470,0.530,93.59,288,1.000,bicubic
resnetaa101d.sw_in12k_ft_in1k,96.360,3.640,99.440,0.560,44.57,288,1.000,bicubic
tf_efficientnet_b7.ap_in1k,96.350,3.650,99.590,0.410,66.35,600,0.949,bicubic
resnetrs200.tf_in1k,96.350,3.650,99.550,0.450,93.21,320,1.000,bicubic
resmlp_big_24_224.fb_in22k_ft_in1k,96.350,3.650,99.520,0.480,129.14,224,0.875,bicubic
xcit_small_24_p16_384.fb_dist_in1k,96.340,3.660,99.580,0.420,47.67,384,1.000,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k,96.340,3.660,99.550,0.450,28.64,288,1.000,bicubic
xcit_small_12_p16_384.fb_dist_in1k,96.340,3.660,99.490,0.510,26.25,384,1.000,bicubic
maxvit_base_tf_224.in1k,96.340,3.660,99.370,0.630,119.47,224,0.950,bicubic
maxvit_large_tf_224.in1k,96.330,3.670,99.410,0.590,211.79,224,0.950,bicubic
vit_base_patch16_clip_224.laion2b_ft_in1k,96.320,3.680,99.540,0.460,86.57,224,1.000,bicubic
regnetz_040_h.ra3_in1k,96.320,3.680,99.520,0.480,28.94,320,1.000,bicubic
xcit_large_24_p16_224.fb_dist_in1k,96.320,3.680,99.500,0.500,189.10,224,1.000,bicubic
vit_base_patch16_clip_224.openai_ft_in1k,96.310,3.690,99.560,0.440,86.57,224,0.900,bicubic
seresnet152d.ra2_in1k,96.310,3.690,99.510,0.490,66.84,320,1.000,bicubic
convnext_base.fb_in1k,96.310,3.690,99.500,0.500,88.59,288,1.000,bicubic
regnety_1280.seer_ft_in1k,96.310,3.690,99.410,0.590,644.81,384,1.000,bicubic
vit_base_patch16_224.augreg_in21k_ft_in1k,96.300,3.700,99.560,0.440,86.57,224,0.900,bicubic
dm_nfnet_f1.dm_in1k,96.300,3.700,99.530,0.470,132.63,320,0.910,bicubic
tf_efficientnet_b6.aa_in1k,96.300,3.700,99.530,0.470,43.04,528,0.942,bicubic
resnetv2_50x3_bit.goog_in21k_ft_in1k,96.270,3.730,99.630,0.370,217.32,448,1.000,bilinear
efficientnetv2_rw_m.agc_in1k,96.270,3.730,99.560,0.440,53.24,416,1.000,bicubic
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,96.270,3.730,99.500,0.500,194.03,224,0.875,bilinear
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,96.250,3.750,99.590,0.410,88.79,224,0.875,bilinear
resnetv2_101x3_bit.goog_in21k_ft_in1k,96.250,3.750,99.580,0.420,387.93,448,1.000,bilinear
convformer_s18.sail_in1k_384,96.250,3.750,99.540,0.460,26.77,384,1.000,bicubic
resnetrs350.tf_in1k,96.250,3.750,99.470,0.530,163.96,384,1.000,bicubic
xcit_medium_24_p16_224.fb_dist_in1k,96.250,3.750,99.410,0.590,84.40,224,1.000,bicubic
convnext_tiny.in12k_ft_in1k,96.240,3.760,99.640,0.360,28.59,288,1.000,bicubic
davit_base.msft_in1k,96.240,3.760,99.410,0.590,87.95,224,0.950,bicubic
convformer_b36.sail_in1k,96.240,3.760,99.290,0.710,99.88,224,1.000,bicubic
xcit_tiny_24_p8_384.fb_dist_in1k,96.230,3.770,99.440,0.560,12.11,384,1.000,bicubic
deit3_base_patch16_384.fb_in1k,96.230,3.770,99.400,0.600,86.88,384,1.000,bicubic
maxxvit_rmlp_small_rw_256.sw_in1k,96.210,3.790,99.480,0.520,66.01,256,0.950,bicubic
coatnet_rmlp_2_rw_224.sw_in1k,96.210,3.790,99.280,0.720,73.88,224,0.950,bicubic
vit_base_patch16_384.orig_in21k_ft_in1k,96.200,3.800,99.530,0.470,86.86,384,1.000,bicubic
edgenext_base.in21k_ft_in1k,96.200,3.800,99.470,0.530,18.51,320,1.000,bicubic
maxvit_small_tf_224.in1k,96.200,3.800,99.460,0.540,68.93,224,0.950,bicubic
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,96.190,3.810,99.500,0.500,236.34,384,1.000,bicubic
deit3_large_patch16_224.fb_in1k,96.190,3.810,99.300,0.700,304.37,224,0.900,bicubic
vit_large_r50_s32_224.augreg_in21k_ft_in1k,96.180,3.820,99.530,0.470,328.99,224,0.900,bicubic
regnetz_040.ra3_in1k,96.180,3.820,99.510,0.490,27.12,320,1.000,bicubic
convnext_tiny.fb_in22k_ft_in1k_384,96.170,3.830,99.500,0.500,28.59,384,1.000,bicubic
swinv2_base_window16_256.ms_in1k,96.170,3.830,99.390,0.610,87.92,256,0.900,bicubic
crossvit_18_dagger_408.in1k,96.150,3.850,99.470,0.530,44.61,408,1.000,bicubic
regnetz_d8_evos.ch_in1k,96.140,3.860,99.490,0.510,23.46,320,1.000,bicubic
deit3_medium_patch16_224.fb_in22k_ft_in1k,96.140,3.860,99.480,0.520,38.85,224,1.000,bicubic
seresnext101_32x8d.ah_in1k,96.140,3.860,99.360,0.640,93.57,288,1.000,bicubic
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,96.130,3.870,99.340,0.660,41.72,224,0.950,bicubic
resnest269e.in1k,96.110,3.890,99.520,0.480,110.93,416,0.928,bicubic
resnet200d.ra2_in1k,96.110,3.890,99.460,0.540,64.69,320,1.000,bicubic
flexivit_base.1200ep_in1k,96.110,3.890,99.410,0.590,86.59,240,0.950,bicubic
convformer_s36.sail_in1k,96.110,3.890,99.310,0.690,40.01,224,1.000,bicubic
convformer_s18.sail_in22k_ft_in1k,96.100,3.900,99.490,0.510,26.77,224,1.000,bicubic
tf_efficientnet_b3.ns_jft_in1k,96.100,3.900,99.480,0.520,12.23,300,0.904,bicubic
rexnetr_300.sw_in12k_ft_in1k,96.090,3.910,99.540,0.460,34.81,288,1.000,bicubic
tf_efficientnet_b5.ap_in1k,96.090,3.910,99.540,0.460,30.39,456,0.934,bicubic
xcit_large_24_p8_224.fb_in1k,96.090,3.910,99.140,0.860,188.93,224,1.000,bicubic
caformer_s36.sail_in1k,96.080,3.920,99.510,0.490,39.30,224,1.000,bicubic
gcvit_base.in1k,96.080,3.920,99.390,0.610,90.32,224,0.875,bicubic
convformer_m36.sail_in1k,96.080,3.920,99.250,0.750,57.05,224,1.000,bicubic
resnest200e.in1k,96.070,3.930,99.480,0.520,70.20,320,0.909,bicubic
tf_efficientnet_b7.aa_in1k,96.070,3.930,99.450,0.550,66.35,600,0.949,bicubic
swinv2_small_window16_256.ms_in1k,96.070,3.930,99.340,0.660,49.73,256,0.900,bicubic
regnety_640.seer_ft_in1k,96.060,3.940,99.500,0.500,281.38,384,1.000,bicubic
resnetrs270.tf_in1k,96.060,3.940,99.480,0.520,129.86,352,1.000,bicubic
swin_small_patch4_window7_224.ms_in22k_ft_in1k,96.060,3.940,99.480,0.520,49.61,224,0.900,bicubic
swinv2_base_window8_256.ms_in1k,96.060,3.940,99.410,0.590,87.92,256,0.900,bicubic
vit_small_r26_s32_384.augreg_in21k_ft_in1k,96.050,3.950,99.550,0.450,36.47,384,1.000,bicubic
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,96.040,3.960,99.540,0.460,44.18,224,0.875,bilinear
maxvit_rmlp_tiny_rw_256.sw_in1k,96.040,3.960,99.410,0.590,29.15,256,0.950,bicubic
swin_s3_base_224.ms_in1k,96.040,3.960,99.350,0.650,71.13,224,0.900,bicubic
vit_base_patch16_224_miil.in21k_ft_in1k,96.040,3.960,99.350,0.650,86.54,224,0.875,bilinear
davit_small.msft_in1k,96.030,3.970,99.400,0.600,49.75,224,0.950,bicubic
volo_d1_224.sail_in1k,96.030,3.970,99.390,0.610,26.63,224,0.960,bicubic
caformer_s18.sail_in22k_ft_in1k,96.010,3.990,99.550,0.450,26.34,224,1.000,bicubic
regnetz_d8.ra3_in1k,96.010,3.990,99.520,0.480,23.37,320,1.000,bicubic
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,96.000,4.000,99.500,0.500,38.86,256,0.950,bicubic
cs3se_edgenet_x.c2ns_in1k,96.000,4.000,99.430,0.570,50.72,320,1.000,bicubic
cait_xs24_384.fb_dist_in1k,96.000,4.000,99.420,0.580,26.67,384,1.000,bicubic
coat_lite_medium.in1k,96.000,4.000,99.350,0.650,44.57,224,0.900,bicubic
vit_small_patch16_384.augreg_in21k_ft_in1k,95.980,4.020,99.590,0.410,22.20,384,1.000,bicubic
mvitv2_base.fb_in1k,95.980,4.020,99.330,0.670,51.47,224,0.900,bicubic
tf_efficientnet_b5.ra_in1k,95.970,4.030,99.450,0.550,30.39,456,0.934,bicubic
convnext_small.fb_in1k,95.970,4.030,99.430,0.570,50.22,288,1.000,bicubic
xcit_small_12_p8_224.fb_dist_in1k,95.960,4.040,99.430,0.570,26.21,224,1.000,bicubic
flexivit_base.600ep_in1k,95.960,4.040,99.420,0.580,86.59,240,0.950,bicubic
resnetrs152.tf_in1k,95.960,4.040,99.380,0.620,86.62,320,1.000,bicubic
flexivit_base.300ep_in1k,95.960,4.040,99.370,0.630,86.59,240,0.950,bicubic
maxvit_rmlp_small_rw_224.sw_in1k,95.960,4.040,99.350,0.650,64.90,224,0.900,bicubic
pvt_v2_b5.in1k,95.940,4.060,99.390,0.610,81.96,224,0.900,bicubic
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,95.940,4.060,99.380,0.620,88.79,224,0.875,bilinear
eca_nfnet_l1.ra2_in1k,95.930,4.070,99.490,0.510,41.41,320,1.000,bicubic
pvt_v2_b4.in1k,95.920,4.080,99.360,0.640,62.56,224,0.900,bicubic
gcvit_small.in1k,95.910,4.090,99.280,0.720,51.09,224,0.875,bicubic
vit_base_patch32_384.augreg_in21k_ft_in1k,95.900,4.100,99.440,0.560,88.30,384,1.000,bicubic
focalnet_base_srf.ms_in1k,95.900,4.100,99.340,0.660,88.15,224,0.900,bicubic
swin_base_patch4_window7_224.ms_in1k,95.900,4.100,99.310,0.690,87.77,224,0.900,bicubic
xcit_small_24_p8_224.fb_in1k,95.900,4.100,99.180,0.820,47.63,224,1.000,bicubic
mvitv2_small.fb_in1k,95.890,4.110,99.360,0.640,34.87,224,0.900,bicubic
tf_efficientnet_b5.aa_in1k,95.880,4.120,99.350,0.650,30.39,456,0.934,bicubic
regnety_160.deit_in1k,95.870,4.130,99.560,0.440,83.59,288,1.000,bicubic
sequencer2d_l.in1k,95.870,4.130,99.470,0.530,54.30,224,0.875,bicubic
regnety_080.ra3_in1k,95.870,4.130,99.440,0.560,39.18,288,1.000,bicubic
resmlp_big_24_224.fb_distilled_in1k,95.870,4.130,99.440,0.560,129.14,224,0.875,bicubic
regnetz_d32.ra3_in1k,95.870,4.130,99.430,0.570,27.58,320,0.950,bicubic
resnet152d.ra2_in1k,95.870,4.130,99.430,0.570,60.21,320,1.000,bicubic
tf_efficientnet_b5.in1k,95.870,4.130,99.390,0.610,30.39,456,0.934,bicubic
xcit_medium_24_p8_224.fb_in1k,95.860,4.140,99.080,0.920,84.32,224,1.000,bicubic
deit3_small_patch16_224.fb_in22k_ft_in1k,95.830,4.170,99.390,0.610,22.06,224,1.000,bicubic
convnextv2_tiny.fcmae_ft_in1k,95.830,4.170,99.340,0.660,28.64,288,1.000,bicubic
crossvit_15_dagger_408.in1k,95.830,4.170,99.320,0.680,28.50,408,1.000,bicubic
swin_s3_small_224.ms_in1k,95.830,4.170,99.200,0.800,49.74,224,0.900,bicubic
focalnet_base_lrf.ms_in1k,95.830,4.170,99.180,0.820,88.75,224,0.900,bicubic
resnext101_64x4d.tv_in1k,95.820,4.180,99.320,0.680,83.46,224,0.875,bilinear
tresnet_v2_l.miil_in21k_ft_in1k,95.820,4.180,99.290,0.710,46.17,224,0.875,bilinear
pit_b_distilled_224.in1k,95.820,4.180,99.210,0.790,74.79,224,0.900,bicubic
maxvit_tiny_tf_224.in1k,95.810,4.190,99.260,0.740,30.92,224,0.950,bicubic
regnety_320.seer_ft_in1k,95.800,4.200,99.390,0.610,145.05,384,1.000,bicubic
edgenext_base.usi_in1k,95.790,4.210,99.570,0.430,18.51,320,1.000,bicubic
xcit_small_24_p16_224.fb_dist_in1k,95.790,4.210,99.350,0.650,47.67,224,1.000,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k_384,95.790,4.210,99.300,0.700,15.62,384,1.000,bicubic
regnety_064.ra3_in1k,95.790,4.210,99.290,0.710,30.58,288,1.000,bicubic
regnetv_064.ra3_in1k,95.780,4.220,99.420,0.580,30.58,288,1.000,bicubic
deit3_base_patch16_224.fb_in1k,95.770,4.230,99.270,0.730,86.59,224,0.900,bicubic
efficientformerv2_l.snap_dist_in1k,95.760,4.240,99.370,0.630,26.32,224,0.950,bicubic
resnet152.a1h_in1k,95.750,4.250,99.430,0.570,60.19,288,1.000,bicubic
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,95.750,4.250,99.430,0.570,236.34,224,0.875,bicubic
deit_base_distilled_patch16_224.fb_in1k,95.750,4.250,99.280,0.720,87.34,224,0.900,bicubic
resnet101d.ra2_in1k,95.740,4.260,99.440,0.560,44.57,320,1.000,bicubic
focalnet_small_lrf.ms_in1k,95.740,4.260,99.210,0.790,50.34,224,0.900,bicubic
maxvit_tiny_rw_224.sw_in1k,95.740,4.260,99.160,0.840,29.06,224,0.950,bicubic
regnetv_040.ra3_in1k,95.730,4.270,99.380,0.620,20.64,288,1.000,bicubic
swinv2_small_window8_256.ms_in1k,95.730,4.270,99.360,0.640,49.73,256,0.900,bicubic
xcit_small_12_p16_224.fb_dist_in1k,95.730,4.270,99.300,0.700,26.25,224,1.000,bicubic
twins_pcpvt_large.in1k,95.720,4.280,99.490,0.510,60.99,224,0.900,bicubic
tf_efficientnetv2_s.in1k,95.710,4.290,99.400,0.600,21.46,384,1.000,bicubic
efficientnetv2_rw_s.ra2_in1k,95.710,4.290,99.380,0.620,23.94,384,1.000,bicubic
hrnet_w18_ssld.paddle_in1k,95.710,4.290,99.340,0.660,21.30,288,1.000,bilinear
swin_small_patch4_window7_224.ms_in1k,95.710,4.290,99.290,0.710,49.61,224,0.900,bicubic
swinv2_cr_small_ns_224.sw_in1k,95.710,4.290,99.290,0.710,49.70,224,0.900,bicubic
twins_svt_large.in1k,95.700,4.300,99.370,0.630,99.27,224,0.900,bicubic
dm_nfnet_f0.dm_in1k,95.690,4.310,99.350,0.650,71.49,256,0.900,bicubic
xception65.ra3_in1k,95.690,4.310,99.320,0.680,39.92,299,0.940,bicubic
caformer_s18.sail_in1k,95.680,4.320,99.290,0.710,26.34,224,1.000,bicubic
cait_s24_224.fb_dist_in1k,95.660,4.340,99.390,0.610,46.92,224,1.000,bicubic
gcvit_tiny.in1k,95.660,4.340,99.330,0.670,28.22,224,0.875,bicubic
xception65p.ra3_in1k,95.660,4.340,99.270,0.730,39.82,299,0.940,bicubic
regnetz_c16_evos.ch_in1k,95.640,4.360,99.420,0.580,13.49,320,0.950,bicubic
deit_base_patch16_384.fb_in1k,95.640,4.360,99.240,0.760,86.86,384,1.000,bicubic
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,95.630,4.370,99.440,0.560,25.03,224,0.875,bilinear
ecaresnet101d.miil_in1k,95.630,4.370,99.410,0.590,44.57,288,0.950,bicubic
focalnet_small_srf.ms_in1k,95.630,4.370,99.290,0.710,49.89,224,0.900,bicubic
coatnet_1_rw_224.sw_in1k,95.610,4.390,99.220,0.780,41.72,224,0.950,bicubic
efficientformer_l7.snap_dist_in1k,95.600,4.400,99.440,0.560,82.23,224,0.950,bicubic
deit3_small_patch16_384.fb_in1k,95.600,4.400,99.390,0.610,22.21,384,1.000,bicubic
tf_efficientnetv2_b3.in21k_ft_in1k,95.600,4.400,99.280,0.720,14.36,300,0.900,bicubic
tf_efficientnet_b4.aa_in1k,95.590,4.410,99.330,0.670,19.34,380,0.922,bicubic
sequencer2d_m.in1k,95.580,4.420,99.270,0.730,38.31,224,0.875,bicubic
resnet101.a1h_in1k,95.580,4.420,99.250,0.750,44.55,288,1.000,bicubic
resnetv2_101.a1h_in1k,95.570,4.430,99.370,0.630,44.54,288,1.000,bicubic
resnest101e.in1k,95.570,4.430,99.270,0.730,48.28,256,0.875,bilinear
regnety_320.tv2_in1k,95.560,4.440,99.390,0.610,145.05,224,0.965,bicubic
twins_svt_base.in1k,95.560,4.440,99.230,0.770,56.07,224,0.900,bicubic
rexnet_300.nav_in1k,95.540,4.460,99.320,0.680,34.71,224,0.875,bicubic
nest_base_jx.goog_in1k,95.540,4.460,99.290,0.710,67.72,224,0.875,bicubic
nest_small_jx.goog_in1k,95.540,4.460,99.220,0.780,38.35,224,0.875,bicubic
efficientnet_b4.ra2_in1k,95.530,4.470,99.400,0.600,19.34,384,1.000,bicubic
resnext101_64x4d.c1_in1k,95.530,4.470,99.290,0.710,83.46,288,1.000,bicubic
tf_efficientnet_b2.ns_jft_in1k,95.520,4.480,99.340,0.660,9.11,260,0.890,bicubic
tresnet_xl.miil_in1k_448,95.510,4.490,99.340,0.660,78.44,448,0.875,bilinear
coatnet_rmlp_1_rw_224.sw_in1k,95.500,4.500,99.250,0.750,41.69,224,0.950,bicubic
regnety_040.ra3_in1k,95.490,4.510,99.420,0.580,20.65,288,1.000,bicubic
tf_efficientnet_b4.ap_in1k,95.490,4.510,99.390,0.610,19.34,380,0.922,bicubic
xcit_tiny_24_p16_384.fb_dist_in1k,95.490,4.510,99.360,0.640,12.12,384,1.000,bicubic
tf_efficientnet_b4.in1k,95.480,4.520,99.270,0.730,19.34,380,0.922,bicubic
twins_pcpvt_base.in1k,95.470,4.530,99.390,0.610,43.83,224,0.900,bicubic
pvt_v2_b3.in1k,95.470,4.530,99.310,0.690,45.24,224,0.900,bicubic
maxvit_nano_rw_256.sw_in1k,95.470,4.530,99.120,0.880,15.45,256,0.950,bicubic
eca_nfnet_l0.ra2_in1k,95.460,4.540,99.390,0.610,24.14,288,1.000,bicubic
regnety_032.ra_in1k,95.460,4.540,99.320,0.680,19.44,288,1.000,bicubic
cs3edgenet_x.c2_in1k,95.460,4.540,99.280,0.720,47.82,288,1.000,bicubic
sequencer2d_s.in1k,95.460,4.540,99.260,0.740,27.65,224,0.875,bicubic
xcit_tiny_24_p8_224.fb_dist_in1k,95.450,4.550,99.360,0.640,12.11,224,1.000,bicubic
maxvit_rmlp_nano_rw_256.sw_in1k,95.440,4.560,99.060,0.940,15.50,256,0.950,bicubic
maxxvitv2_nano_rw_256.sw_in1k,95.430,4.570,99.190,0.810,23.70,256,0.950,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k,95.420,4.580,99.310,0.690,15.62,288,1.000,bicubic
xcit_small_12_p8_224.fb_in1k,95.420,4.580,99.190,0.810,26.21,224,1.000,bicubic
resnetv2_50x1_bit.goog_distilled_in1k,95.410,4.590,99.430,0.570,25.55,224,0.875,bicubic
cs3sedarknet_x.c2ns_in1k,95.410,4.590,99.320,0.680,35.40,288,1.000,bicubic
swinv2_cr_small_224.sw_in1k,95.410,4.590,99.060,0.940,49.70,224,0.900,bicubic
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,95.400,4.600,99.400,0.600,194.03,224,0.875,bilinear
tresnet_l.miil_in1k_448,95.400,4.600,99.300,0.700,55.99,448,0.875,bilinear
mvitv2_tiny.fb_in1k,95.400,4.600,99.160,0.840,24.17,224,0.900,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k_384,95.390,4.610,99.280,0.720,18.45,384,1.000,bicubic
nfnet_l0.ra2_in1k,95.380,4.620,99.420,0.580,35.07,288,1.000,bicubic
regnetz_c16.ra3_in1k,95.380,4.620,99.350,0.650,13.46,320,1.000,bicubic
deit3_medium_patch16_224.fb_in1k,95.380,4.620,99.210,0.790,38.85,224,0.900,bicubic
tresnet_m.miil_in21k_ft_in1k,95.380,4.620,99.150,0.850,31.39,224,0.875,bilinear
pnasnet5large.tf_in1k,95.360,4.640,99.130,0.870,86.06,331,0.911,bicubic
convnext_nano.in12k_ft_in1k,95.350,4.650,99.450,0.550,15.59,288,1.000,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k_384,95.350,4.650,99.120,0.880,10.59,384,1.000,bicubic
xcit_tiny_12_p8_384.fb_dist_in1k,95.340,4.660,99.340,0.660,6.71,384,1.000,bicubic
maxxvit_rmlp_nano_rw_256.sw_in1k,95.340,4.660,99.310,0.690,16.78,256,0.950,bicubic
swinv2_tiny_window16_256.ms_in1k,95.330,4.670,99.300,0.700,28.35,256,0.900,bicubic
convformer_s18.sail_in1k,95.330,4.670,99.150,0.850,26.77,224,1.000,bicubic
resnetv2_101x1_bit.goog_in21k_ft_in1k,95.320,4.680,99.370,0.630,44.54,448,1.000,bilinear
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,95.320,4.680,99.320,0.680,88.79,224,0.875,bilinear
rexnetr_200.sw_in12k_ft_in1k,95.310,4.690,99.480,0.520,16.52,288,1.000,bicubic
resnext101_32x8d.tv2_in1k,95.300,4.700,99.360,0.640,88.79,224,0.965,bilinear
regnety_080_tv.tv2_in1k,95.300,4.700,99.230,0.770,39.38,224,0.965,bicubic
vit_relpos_medium_patch16_cls_224.sw_in1k,95.300,4.700,99.100,0.900,38.76,224,0.900,bicubic
resnetaa50d.sw_in12k_ft_in1k,95.290,4.710,99.380,0.620,25.58,288,1.000,bicubic
gc_efficientnetv2_rw_t.agc_in1k,95.290,4.710,99.220,0.780,13.68,288,1.000,bicubic
cs3darknet_x.c2ns_in1k,95.280,4.720,99.290,0.710,35.05,288,1.000,bicubic
regnetx_320.tv2_in1k,95.280,4.720,99.290,0.710,107.81,224,0.965,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k_384,95.260,4.740,99.380,0.620,14.25,384,1.000,bicubic
flexivit_small.600ep_in1k,95.260,4.740,99.160,0.840,22.06,240,0.950,bicubic
resnetrs101.tf_in1k,95.250,4.750,99.210,0.790,63.62,288,0.940,bicubic
vit_relpos_base_patch16_clsgap_224.sw_in1k,95.250,4.750,99.200,0.800,86.43,224,0.900,bicubic
convnext_tiny_hnf.a2h_in1k,95.250,4.750,98.980,1.020,28.59,288,1.000,bicubic
cait_xxs36_384.fb_dist_in1k,95.240,4.760,99.320,0.680,17.37,384,1.000,bicubic
vit_large_patch32_384.orig_in21k_ft_in1k,95.240,4.760,99.320,0.680,306.63,384,1.000,bicubic
wide_resnet101_2.tv2_in1k,95.240,4.760,99.200,0.800,126.89,224,0.965,bilinear
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,95.230,4.770,99.240,0.760,88.22,224,0.900,bicubic
pvt_v2_b2_li.in1k,95.220,4.780,99.260,0.740,22.55,224,0.900,bicubic
resnetv2_50d_gn.ah_in1k,95.220,4.780,99.030,0.970,25.57,288,1.000,bicubic
convnext_tiny.fb_in1k,95.210,4.790,99.310,0.690,28.59,288,1.000,bicubic
efficientformer_l3.snap_dist_in1k,95.210,4.790,99.310,0.690,31.41,224,0.950,bicubic
regnetx_160.tv2_in1k,95.210,4.790,99.280,0.720,54.28,224,0.965,bicubic
levit_384.fb_dist_in1k,95.210,4.790,99.160,0.840,39.13,224,0.900,bicubic
levit_conv_384.fb_dist_in1k,95.210,4.790,99.160,0.840,39.13,224,0.900,bicubic
resnet50.fb_swsl_ig1b_ft_in1k,95.200,4.800,99.390,0.610,25.56,224,0.875,bilinear
resnet51q.ra2_in1k,95.200,4.800,99.280,0.720,35.70,288,1.000,bilinear
vit_base_patch16_224.orig_in21k_ft_in1k,95.200,4.800,99.230,0.770,86.57,224,0.900,bicubic
coat_small.in1k,95.190,4.810,99.280,0.720,21.69,224,0.900,bicubic
focalnet_tiny_lrf.ms_in1k,95.190,4.810,99.220,0.780,28.65,224,0.900,bicubic
vit_relpos_medium_patch16_224.sw_in1k,95.190,4.810,99.220,0.780,38.75,224,0.900,bicubic
flexivit_small.1200ep_in1k,95.180,4.820,99.180,0.820,22.06,240,0.950,bicubic
poolformerv2_m48.sail_in1k,95.180,4.820,99.160,0.840,73.35,224,1.000,bicubic
crossvit_18_dagger_240.in1k,95.180,4.820,99.120,0.880,44.27,240,0.875,bicubic
regnety_160.tv2_in1k,95.160,4.840,99.250,0.750,83.59,224,0.965,bicubic
efficientnet_b3.ra2_in1k,95.150,4.850,99.210,0.790,12.23,320,1.000,bicubic
flexivit_small.300ep_in1k,95.150,4.850,99.150,0.850,22.06,240,0.950,bicubic
nasnetalarge.tf_in1k,95.150,4.850,99.130,0.870,88.75,331,0.911,bicubic
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,95.140,4.860,99.300,0.700,44.18,224,0.875,bilinear
convnextv2_nano.fcmae_ft_in1k,95.140,4.860,99.220,0.780,15.62,288,1.000,bicubic
vit_relpos_base_patch16_224.sw_in1k,95.130,4.870,99.290,0.710,86.43,224,0.900,bicubic
wide_resnet50_2.racm_in1k,95.130,4.870,99.260,0.740,68.88,288,0.950,bicubic
vit_small_r26_s32_224.augreg_in21k_ft_in1k,95.130,4.870,99.220,0.780,36.43,224,0.900,bicubic
resnet61q.ra2_in1k,95.130,4.870,99.080,0.920,36.85,288,1.000,bicubic
xcit_medium_24_p16_224.fb_in1k,95.130,4.870,98.940,1.060,84.40,224,1.000,bicubic
fbnetv3_g.ra2_in1k,95.120,4.880,99.200,0.800,16.62,288,0.950,bilinear
tf_efficientnetv2_b3.in1k,95.120,4.880,99.200,0.800,14.36,300,0.904,bicubic
efficientformerv2_s2.snap_dist_in1k,95.120,4.880,99.120,0.880,12.71,224,0.950,bicubic
cs3sedarknet_l.c2ns_in1k,95.110,4.890,99.210,0.790,21.91,288,0.950,bicubic
convit_base.fb_in1k,95.100,4.900,99.150,0.850,86.54,224,0.875,bicubic
poolformer_m48.sail_in1k,95.100,4.900,99.100,0.900,73.47,224,0.950,bicubic
coatnet_rmlp_nano_rw_224.sw_in1k,95.090,4.910,99.170,0.830,15.15,224,0.900,bicubic
resnet152.a1_in1k,95.090,4.910,98.990,1.010,60.19,288,1.000,bicubic
ecaresnet50t.ra2_in1k,95.080,4.920,99.290,0.710,25.57,320,0.950,bicubic
tresnet_xl.miil_in1k,95.080,4.920,99.260,0.740,78.44,224,0.875,bilinear
davit_tiny.msft_in1k,95.080,4.920,99.140,0.860,28.36,224,0.950,bicubic
crossvit_18_240.in1k,95.070,4.930,99.120,0.880,43.27,240,0.875,bicubic
coat_lite_small.in1k,95.070,4.930,99.030,0.970,19.84,224,0.900,bicubic
crossvit_base_240.in1k,95.070,4.930,98.980,1.020,105.03,240,0.875,bicubic
efficientnetv2_rw_t.ra2_in1k,95.060,4.940,99.220,0.780,13.65,288,1.000,bicubic
vit_relpos_medium_patch16_rpn_224.sw_in1k,95.060,4.940,99.200,0.800,38.73,224,0.900,bicubic
xception41p.ra3_in1k,95.060,4.940,99.160,0.840,26.91,299,0.940,bicubic
xcit_small_24_p16_224.fb_in1k,95.060,4.940,99.070,0.930,47.67,224,1.000,bicubic
focalnet_tiny_srf.ms_in1k,95.050,4.950,99.280,0.720,28.43,224,0.900,bicubic
poolformerv2_m36.sail_in1k,95.050,4.950,99.150,0.850,56.08,224,1.000,bicubic
coatnet_nano_rw_224.sw_in1k,95.050,4.950,99.140,0.860,15.14,224,0.900,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k,95.050,4.950,99.080,0.920,18.45,256,0.888,bicubic
resnet152.tv2_in1k,95.040,4.960,99.180,0.820,60.19,224,0.965,bilinear
poolformer_m36.sail_in1k,95.030,4.970,99.100,0.900,56.17,224,0.950,bicubic
swinv2_tiny_window8_256.ms_in1k,95.020,4.980,99.170,0.830,28.35,256,0.900,bicubic
gcvit_xtiny.in1k,95.020,4.980,99.160,0.840,19.98,224,0.875,bicubic
deit_base_patch16_224.fb_in1k,95.020,4.980,98.970,1.030,86.57,224,0.900,bicubic
pvt_v2_b2.in1k,95.010,4.990,99.140,0.860,25.36,224,0.900,bicubic
halo2botnet50ts_256.a1h_in1k,95.010,4.990,99.040,0.960,22.64,256,0.950,bicubic
ecaresnet101d_pruned.miil_in1k,95.000,5.000,99.230,0.770,24.88,288,0.950,bicubic
seresnext50_32x4d.racm_in1k,95.000,5.000,99.200,0.800,27.56,288,0.950,bicubic
resnext50_32x4d.a1h_in1k,94.990,5.010,99.190,0.810,25.03,288,1.000,bicubic
crossvit_15_dagger_240.in1k,94.990,5.010,99.160,0.840,28.21,240,0.875,bicubic
coatnet_bn_0_rw_224.sw_in1k,94.980,5.020,99.240,0.760,27.44,224,0.950,bicubic
visformer_small.in1k,94.970,5.030,99.210,0.790,40.22,224,0.900,bicubic
convmixer_1536_20.in1k,94.970,5.030,99.170,0.830,51.63,224,0.960,bicubic
tf_efficientnet_b3.ap_in1k,94.970,5.030,99.110,0.890,12.23,300,0.904,bicubic
resnet152.a2_in1k,94.970,5.030,99.070,0.930,60.19,288,1.000,bicubic
xcit_large_24_p16_224.fb_in1k,94.960,5.040,98.830,1.170,189.10,224,1.000,bicubic
cait_xxs24_384.fb_dist_in1k,94.950,5.050,99.130,0.870,12.03,384,1.000,bicubic
vit_srelpos_medium_patch16_224.sw_in1k,94.940,5.060,99.200,0.800,38.74,224,0.900,bicubic
resnet101.a1_in1k,94.940,5.060,99.040,0.960,44.55,288,1.000,bicubic
gernet_l.idstcv_in1k,94.930,5.070,99.200,0.800,31.08,256,0.875,bilinear
resnetv2_50d_evos.ah_in1k,94.920,5.080,99.180,0.820,25.59,288,1.000,bicubic
swin_s3_tiny_224.ms_in1k,94.920,5.080,99.170,0.830,28.33,224,0.900,bicubic
convit_small.fb_in1k,94.920,5.080,99.100,0.900,27.78,224,0.875,bicubic
nest_tiny_jx.goog_in1k,94.920,5.080,99.100,0.900,17.06,224,0.875,bicubic
tf_efficientnet_b3.aa_in1k,94.910,5.090,99.110,0.890,12.23,300,0.904,bicubic
xcit_tiny_24_p8_224.fb_in1k,94.900,5.100,99.190,0.810,12.11,224,1.000,bicubic
tresnet_l.miil_in1k,94.900,5.100,99.030,0.970,55.99,224,0.875,bilinear
coatnet_0_rw_224.sw_in1k,94.900,5.100,99.020,0.980,27.44,224,0.950,bicubic
vit_small_patch16_224.augreg_in21k_ft_in1k,94.890,5.110,99.270,0.730,22.05,224,0.900,bicubic
ecaresnet50t.a1_in1k,94.890,5.110,99.070,0.930,25.57,288,1.000,bicubic
resnet101.a2_in1k,94.890,5.110,99.060,0.940,44.55,288,1.000,bicubic
mixer_b16_224.miil_in21k_ft_in1k,94.880,5.120,99.080,0.920,59.88,224,0.875,bilinear
regnety_032.tv2_in1k,94.870,5.130,99.230,0.770,19.44,224,0.965,bicubic
convnext_nano.d1h_in1k,94.870,5.130,99.140,0.860,15.59,288,1.000,bicubic
tf_efficientnet_lite4.in1k,94.870,5.130,99.100,0.900,13.01,380,0.920,bilinear
tf_efficientnet_b1.ns_jft_in1k,94.860,5.140,99.250,0.750,7.79,240,0.882,bicubic
coatnext_nano_rw_224.sw_in1k,94.850,5.150,99.200,0.800,14.70,224,0.900,bicubic
resnetaa50.a1h_in1k,94.850,5.150,99.120,0.880,25.56,288,1.000,bicubic
resnet101.tv2_in1k,94.840,5.160,99.030,0.970,44.55,224,0.965,bilinear
edgenext_small.usi_in1k,94.830,5.170,99.410,0.590,5.59,320,1.000,bicubic
vit_base_patch16_rpn_224.sw_in1k,94.830,5.170,99.090,0.910,86.54,224,0.900,bicubic
xcit_small_12_p16_224.fb_in1k,94.820,5.180,99.060,0.940,26.25,224,1.000,bicubic
wide_resnet50_2.tv2_in1k,94.810,5.190,99.260,0.740,68.88,224,0.965,bilinear
resnet50d.ra2_in1k,94.810,5.190,99.230,0.770,25.58,288,0.950,bicubic
lamhalobotnet50ts_256.a1h_in1k,94.810,5.190,98.980,1.020,22.57,256,0.950,bicubic
pit_b_224.in1k,94.810,5.190,98.820,1.180,73.76,224,0.900,bicubic
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,94.800,5.200,99.290,0.710,28.29,224,0.900,bicubic
cs3darknet_focus_l.c2ns_in1k,94.790,5.210,99.160,0.840,21.15,288,0.950,bicubic
gcresnet50t.ra2_in1k,94.780,5.220,99.120,0.880,25.90,288,1.000,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k,94.780,5.220,99.090,0.910,14.25,256,0.888,bicubic
swinv2_cr_tiny_ns_224.sw_in1k,94.770,5.230,99.110,0.890,28.33,224,0.900,bicubic
twins_svt_small.in1k,94.760,5.240,99.090,0.910,24.06,224,0.900,bicubic
coat_mini.in1k,94.760,5.240,98.950,1.050,10.34,224,0.900,bicubic
vit_base_patch32_clip_224.laion2b_ft_in1k,94.750,5.250,99.070,0.930,88.22,224,0.900,bicubic
resnetv2_50x1_bit.goog_in21k_ft_in1k,94.740,5.260,99.180,0.820,25.55,448,1.000,bilinear
seresnet50.ra2_in1k,94.740,5.260,99.110,0.890,28.09,288,0.950,bicubic
legacy_senet154.in1k,94.730,5.270,99.100,0.900,115.09,224,0.875,bilinear
regnetx_080.tv2_in1k,94.730,5.270,99.030,0.970,39.57,224,0.965,bicubic
resnet152s.gluon_in1k,94.720,5.280,99.060,0.940,60.32,224,0.875,bicubic
halonet50ts.a1h_in1k,94.720,5.280,98.830,1.170,22.73,256,0.940,bicubic
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,94.710,5.290,99.240,0.760,25.03,224,0.875,bilinear
poolformerv2_s36.sail_in1k,94.700,5.300,99.230,0.770,30.79,224,1.000,bicubic
resnest50d_4s2x40d.in1k,94.700,5.300,99.130,0.870,30.42,224,0.875,bicubic
crossvit_15_240.in1k,94.700,5.300,99.080,0.920,27.53,240,0.875,bicubic
senet154.gluon_in1k,94.700,5.300,98.970,1.030,115.09,224,0.875,bicubic
xcit_tiny_12_p8_224.fb_dist_in1k,94.690,5.310,99.180,0.820,6.71,224,1.000,bicubic
pit_s_distilled_224.in1k,94.690,5.310,99.150,0.850,24.04,224,0.900,bicubic
resnetv2_50.a1h_in1k,94.690,5.310,99.090,0.910,25.55,288,1.000,bicubic
deit3_small_patch16_224.fb_in1k,94.690,5.310,98.760,1.240,22.06,224,0.900,bicubic
cs3darknet_l.c2ns_in1k,94.680,5.320,99.230,0.770,21.16,288,0.950,bicubic
vit_relpos_small_patch16_224.sw_in1k,94.680,5.320,99.110,0.890,21.98,224,0.900,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k,94.680,5.320,98.920,1.080,10.59,256,0.888,bicubic
ecaresnet50d.miil_in1k,94.670,5.330,99.260,0.740,25.58,288,0.950,bicubic
efficientnet_el.ra_in1k,94.670,5.330,99.130,0.870,10.59,300,0.904,bicubic
regnetz_b16.ra3_in1k,94.670,5.330,99.130,0.870,9.72,288,1.000,bicubic
rexnet_200.nav_in1k,94.670,5.330,99.090,0.910,16.37,224,0.875,bicubic
tresnet_m.miil_in1k_448,94.660,5.340,99.150,0.850,31.39,448,0.875,bilinear
seresnext101_64x4d.gluon_in1k,94.650,5.350,98.970,1.030,88.23,224,0.875,bicubic
resnet50_gn.a1h_in1k,94.620,5.380,99.150,0.850,25.56,288,0.950,bicubic
swin_tiny_patch4_window7_224.ms_in1k,94.620,5.380,99.120,0.880,28.29,224,0.900,bicubic
poolformer_s36.sail_in1k,94.620,5.380,99.050,0.950,30.86,224,0.900,bicubic
vit_small_patch16_384.augreg_in1k,94.610,5.390,99.140,0.860,22.20,384,1.000,bicubic
twins_pcpvt_small.in1k,94.600,5.400,99.150,0.850,24.11,224,0.900,bicubic
deit_small_distilled_patch16_224.fb_in1k,94.600,5.400,99.100,0.900,22.44,224,0.900,bicubic
resnet50.tv2_in1k,94.600,5.400,99.090,0.910,25.56,224,0.965,bilinear
resnest50d.in1k,94.600,5.400,99.030,0.970,27.48,224,0.875,bilinear
vit_small_patch32_384.augreg_in21k_ft_in1k,94.590,5.410,99.140,0.860,22.92,384,1.000,bicubic
efficientnet_b3_pruned.in1k,94.590,5.410,99.070,0.930,9.86,300,0.904,bicubic
pit_s_224.in1k,94.590,5.410,98.930,1.070,23.46,224,0.900,bicubic
crossvit_small_240.in1k,94.580,5.420,99.110,0.890,26.86,240,0.875,bicubic
convnext_nano_ols.d1h_in1k,94.580,5.420,99.050,0.950,15.65,288,1.000,bicubic
ecaresnet50t.a2_in1k,94.570,5.430,99.040,0.960,25.57,288,1.000,bicubic
repvgg_b3.rvgg_in1k,94.570,5.430,98.910,1.090,123.09,224,0.875,bilinear
lambda_resnet50ts.a1h_in1k,94.570,5.430,98.650,1.350,21.54,256,0.950,bicubic
tnt_s_patch16_224,94.560,5.440,99.170,0.830,23.76,224,0.900,bicubic
resmlp_36_224.fb_distilled_in1k,94.560,5.440,99.160,0.840,44.69,224,0.875,bicubic
convnextv2_pico.fcmae_ft_in1k,94.560,5.440,99.140,0.860,9.07,288,0.950,bicubic
vit_srelpos_small_patch16_224.sw_in1k,94.550,5.450,99.150,0.850,21.97,224,0.900,bicubic
gernet_m.idstcv_in1k,94.540,5.460,98.920,1.080,21.14,224,0.875,bilinear
ecaresnetlight.miil_in1k,94.530,5.470,99.180,0.820,30.16,288,0.950,bicubic
res2net101d.in1k,94.530,5.470,98.980,1.020,45.23,224,0.875,bilinear
regnety_320.pycls_in1k,94.520,5.480,99.170,0.830,145.05,224,0.875,bicubic
xcit_tiny_12_p16_384.fb_dist_in1k,94.520,5.480,99.170,0.830,6.72,384,1.000,bicubic
mobilevitv2_200.cvnets_in1k,94.520,5.480,98.970,1.030,18.45,256,0.888,bicubic
haloregnetz_b.ra3_in1k,94.520,5.480,98.960,1.040,11.68,224,0.940,bicubic
regnetx_032.tv2_in1k,94.520,5.480,98.910,1.090,15.30,224,0.965,bicubic
sehalonet33ts.ra2_in1k,94.520,5.480,98.770,1.230,13.69,256,0.940,bicubic
resnet50.c1_in1k,94.510,5.490,99.070,0.930,25.56,288,1.000,bicubic
resnet50.b1k_in1k,94.510,5.490,99.000,1.000,25.56,288,1.000,bicubic
repvgg_b3g4.rvgg_in1k,94.500,5.500,99.020,0.980,83.83,224,0.875,bilinear
gcresnext50ts.ch_in1k,94.490,5.510,99.010,0.990,15.67,288,1.000,bicubic
ese_vovnet39b.ra_in1k,94.480,5.520,99.060,0.940,24.57,288,0.950,bicubic
poolformerv2_s24.sail_in1k,94.470,5.530,99.010,0.990,21.34,224,1.000,bicubic
resnet50.d_in1k,94.470,5.530,99.000,1.000,25.56,288,1.000,bicubic
resnext50_32x4d.tv2_in1k,94.460,5.540,99.030,0.970,25.03,224,0.965,bilinear
resnet50d.a2_in1k,94.460,5.540,98.900,1.100,25.58,288,1.000,bicubic
eva02_tiny_patch14_336.mim_in22k_ft_in1k,94.450,5.550,99.100,0.900,5.76,336,1.000,bicubic
seresnet50.a2_in1k,94.450,5.550,98.890,1.110,28.09,288,1.000,bicubic
vit_base_patch32_clip_224.openai_ft_in1k,94.440,5.560,99.180,0.820,88.22,224,0.900,bicubic
convmixer_768_32.in1k,94.440,5.560,99.110,0.890,21.11,224,0.960,bicubic
vit_base_patch16_384.augreg_in1k,94.440,5.560,99.020,0.980,86.86,384,1.000,bicubic
resnet152.a3_in1k,94.440,5.560,98.880,1.120,60.19,224,0.950,bicubic
seresnext101_32x4d.gluon_in1k,94.430,5.570,99.090,0.910,48.96,224,0.875,bicubic
resnet152d.gluon_in1k,94.430,5.570,99.000,1.000,60.21,224,0.875,bicubic
regnety_016.tv2_in1k,94.410,5.590,99.040,0.960,11.20,224,0.965,bicubic
levit_256.fb_dist_in1k,94.400,5.600,99.060,0.940,18.89,224,0.900,bicubic
levit_conv_256.fb_dist_in1k,94.400,5.600,99.060,0.940,18.89,224,0.900,bicubic
vit_base_patch32_224.augreg_in21k_ft_in1k,94.400,5.600,99.060,0.940,88.22,224,0.900,bicubic
resnext50d_32x4d.bt_in1k,94.400,5.600,99.050,0.950,25.05,288,0.950,bicubic
resnet50d.a1_in1k,94.400,5.600,98.790,1.210,25.58,288,1.000,bicubic
poolformer_s24.sail_in1k,94.390,5.610,99.060,0.940,21.39,224,0.900,bicubic
nf_resnet50.ra2_in1k,94.380,5.620,99.070,0.930,25.56,288,0.940,bicubic
resnest50d_1s4x24d.in1k,94.380,5.620,99.070,0.930,25.68,224,0.875,bicubic
inception_v4.tf_in1k,94.380,5.620,98.820,1.180,42.68,299,0.875,bicubic
resnext50_32x4d.a1_in1k,94.380,5.620,98.780,1.220,25.03,288,1.000,bicubic
darknet53.c2ns_in1k,94.360,5.640,99.050,0.950,41.61,288,1.000,bicubic
efficientnet_b2.ra_in1k,94.360,5.640,99.050,0.950,9.11,288,1.000,bicubic
edgenext_small_rw.sw_in1k,94.360,5.640,99.040,0.960,7.83,320,1.000,bicubic
inception_resnet_v2.tf_in1k,94.360,5.640,98.800,1.200,55.84,299,0.897,bicubic
tf_efficientnet_el.in1k,94.350,5.650,99.100,0.900,10.59,300,0.904,bicubic
xcit_tiny_12_p8_224.fb_in1k,94.350,5.650,99.070,0.930,6.71,224,1.000,bicubic
gcresnet33ts.ra2_in1k,94.350,5.650,98.960,1.040,19.88,288,1.000,bicubic
resnext101_64x4d.gluon_in1k,94.350,5.650,98.880,1.120,83.46,224,0.875,bicubic
resmlp_24_224.fb_distilled_in1k,94.330,5.670,99.090,0.910,30.02,224,0.875,bicubic
resnext50_32x4d.ra_in1k,94.330,5.670,99.030,0.970,25.03,288,0.950,bicubic
resnet50.fb_ssl_yfcc100m_ft_in1k,94.320,5.680,99.150,0.850,25.56,224,0.875,bilinear
sebotnet33ts_256.a1h_in1k,94.310,5.690,98.600,1.400,13.70,256,0.940,bicubic
ecaresnet50d_pruned.miil_in1k,94.300,5.700,99.200,0.800,19.94,288,0.950,bicubic
resnet50.b2k_in1k,94.300,5.700,98.930,1.070,25.56,288,1.000,bicubic
tf_efficientnet_b3.in1k,94.290,5.710,99.100,0.900,12.23,300,0.904,bicubic
res2net50d.in1k,94.280,5.720,98.860,1.140,25.72,224,0.875,bilinear
rexnet_150.nav_in1k,94.270,5.730,99.090,0.910,9.73,224,0.875,bicubic
resnet50.c2_in1k,94.270,5.730,99.040,0.960,25.56,288,1.000,bicubic
tf_efficientnet_b2.ap_in1k,94.270,5.730,98.950,1.050,9.11,260,0.890,bicubic
resmlp_big_24_224.fb_in1k,94.270,5.730,98.820,1.180,129.14,224,0.875,bicubic
regnetx_120.pycls_in1k,94.260,5.740,99.170,0.830,46.11,224,0.875,bicubic
seresnet33ts.ra2_in1k,94.260,5.740,99.000,1.000,19.78,288,1.000,bicubic
eca_resnet33ts.ra2_in1k,94.250,5.750,99.030,0.970,19.68,288,1.000,bicubic
cspresnext50.ra_in1k,94.240,5.760,99.050,0.950,20.57,256,0.887,bilinear
xcit_tiny_24_p16_224.fb_dist_in1k,94.240,5.760,98.960,1.040,12.12,224,1.000,bicubic
regnetx_320.pycls_in1k,94.230,5.770,99.050,0.950,107.81,224,0.875,bicubic
mobilevitv2_175.cvnets_in1k,94.230,5.770,98.930,1.070,14.25,256,0.888,bicubic
mixnet_xl.ra_in1k,94.230,5.770,98.820,1.180,11.90,224,0.875,bicubic
tf_efficientnet_b2.aa_in1k,94.220,5.780,99.040,0.960,9.11,260,0.890,bicubic
resnet50.a1_in1k,94.220,5.780,98.930,1.070,25.56,288,1.000,bicubic
resnext50_32x4d.a2_in1k,94.220,5.780,98.750,1.250,25.03,288,1.000,bicubic
maxvit_rmlp_pico_rw_256.sw_in1k,94.210,5.790,99.000,1.000,7.52,256,0.950,bicubic
darknetaa53.c2ns_in1k,94.210,5.790,98.950,1.050,36.02,288,1.000,bilinear
resnet50.a1h_in1k,94.210,5.790,98.920,1.080,25.56,224,1.000,bicubic
resnet101s.gluon_in1k,94.180,5.820,99.010,0.990,44.67,224,0.875,bicubic
resnet101d.gluon_in1k,94.180,5.820,98.940,1.060,44.57,224,0.875,bicubic
seresnext50_32x4d.gluon_in1k,94.180,5.820,98.920,1.080,27.56,224,0.875,bicubic
resnetblur50.bt_in1k,94.170,5.830,99.010,0.990,25.56,288,0.950,bicubic
seresnet50.a1_in1k,94.160,5.840,98.850,1.150,28.09,288,1.000,bicubic
dpn92.mx_in1k,94.150,5.850,98.950,1.050,37.67,224,0.875,bicubic
regnety_064.pycls_in1k,94.130,5.870,99.030,0.970,30.58,224,0.875,bicubic
regnety_160.pycls_in1k,94.130,5.870,99.020,0.980,83.59,224,0.875,bicubic
resnext101_32x4d.gluon_in1k,94.130,5.870,98.940,1.060,44.18,224,0.875,bicubic
resnet50.a2_in1k,94.130,5.870,98.850,1.150,25.56,288,1.000,bicubic
legacy_seresnext101_32x4d.in1k,94.120,5.880,98.970,1.030,48.96,224,0.875,bilinear
inception_resnet_v2.tf_ens_adv_in1k,94.120,5.880,98.790,1.210,55.84,299,0.897,bicubic
resnet50.ra_in1k,94.100,5.900,98.840,1.160,25.56,288,0.950,bicubic
cspdarknet53.ra_in1k,94.090,5.910,98.980,1.020,27.64,256,0.887,bilinear
tf_efficientnet_lite3.in1k,94.090,5.910,98.960,1.040,8.20,300,0.904,bilinear
efficientnet_el_pruned.in1k,94.080,5.920,99.020,0.980,10.59,300,0.904,bicubic
tresnet_m.miil_in1k,94.080,5.920,98.830,1.170,31.39,224,0.875,bilinear
tf_efficientnetv2_b2.in1k,94.060,5.940,98.930,1.070,10.10,260,0.890,bicubic
gcvit_xxtiny.in1k,94.050,5.950,99.080,0.920,12.00,224,0.875,bicubic
mobilevitv2_150.cvnets_in1k,94.050,5.950,98.900,1.100,10.59,256,0.888,bicubic
convnext_pico.d1_in1k,94.030,5.970,99.010,0.990,9.05,288,0.950,bicubic
resnetrs50.tf_in1k,94.030,5.970,98.850,1.150,35.69,224,0.910,bicubic
resnet152.gluon_in1k,94.030,5.970,98.740,1.260,60.19,224,0.875,bicubic
regnetx_016.tv2_in1k,94.020,5.980,98.930,1.070,9.19,224,0.965,bicubic
hrnet_w48.ms_in1k,94.010,5.990,99.030,0.970,77.47,224,0.875,bilinear
regnety_120.pycls_in1k,94.010,5.990,99.030,0.970,51.82,224,0.875,bicubic
convnext_pico_ols.d1_in1k,94.010,5.990,98.930,1.070,9.06,288,1.000,bicubic
dpn107.mx_in1k,94.010,5.990,98.820,1.180,86.92,224,0.875,bicubic
resnet50.ram_in1k,94.000,6.000,98.880,1.120,25.56,288,0.950,bicubic
dla102x2.in1k,93.990,6.010,99.040,0.960,41.28,224,0.875,bilinear
deit_small_patch16_224.fb_in1k,93.990,6.010,98.960,1.040,22.05,224,0.900,bicubic
resnet50.bt_in1k,93.960,6.040,98.930,1.070,25.56,288,0.950,bicubic
skresnext50_32x4d.ra_in1k,93.960,6.040,98.830,1.170,27.48,224,0.875,bicubic
efficientformer_l1.snap_dist_in1k,93.940,6.060,99.030,0.970,12.29,224,0.950,bicubic
ecaresnet26t.ra2_in1k,93.940,6.060,98.930,1.070,16.01,320,0.950,bicubic
dpn98.mx_in1k,93.930,6.070,98.910,1.090,61.57,224,0.875,bicubic
resnet33ts.ra2_in1k,93.930,6.070,98.880,1.120,19.68,288,1.000,bicubic
xception71.tf_in1k,93.920,6.080,98.950,1.050,42.34,299,0.903,bicubic
regnetx_160.pycls_in1k,93.910,6.090,99.090,0.910,54.28,224,0.875,bicubic
cait_xxs36_224.fb_dist_in1k,93.910,6.090,98.890,1.110,17.30,224,1.000,bicubic
vit_base_patch16_224.sam_in1k,93.890,6.110,98.890,1.110,86.57,224,0.900,bicubic
nf_regnet_b1.ra2_in1k,93.890,6.110,98.750,1.250,10.22,288,0.900,bicubic
regnety_080.pycls_in1k,93.880,6.120,99.000,1.000,39.18,224,0.875,bicubic
resnet152c.gluon_in1k,93.880,6.120,98.800,1.200,60.21,224,0.875,bicubic
fbnetv3_d.ra2_in1k,93.870,6.130,98.900,1.100,10.31,256,0.950,bilinear
cspresnet50.ra_in1k,93.870,6.130,98.870,1.130,21.62,256,0.887,bilinear
ecaresnet50t.a3_in1k,93.860,6.140,98.850,1.150,25.57,224,0.950,bicubic
resnet101.a3_in1k,93.860,6.140,98.760,1.240,44.55,224,0.950,bicubic
xcit_tiny_24_p16_224.fb_in1k,93.850,6.150,98.750,1.250,12.12,224,1.000,bicubic
efficientformerv2_s1.snap_dist_in1k,93.840,6.160,98.890,1.110,6.19,224,0.950,bicubic
hrnet_w64.ms_in1k,93.830,6.170,98.940,1.060,128.06,224,0.875,bilinear
repvgg_b2g4.rvgg_in1k,93.830,6.170,98.930,1.070,61.76,224,0.875,bilinear
efficientnet_b2_pruned.in1k,93.800,6.200,98.910,1.090,8.31,260,0.890,bicubic
regnetx_080.pycls_in1k,93.790,6.210,98.910,1.090,39.57,224,0.875,bicubic
dla169.in1k,93.790,6.210,98.860,1.140,53.39,224,0.875,bilinear
dpn68b.ra_in1k,93.790,6.210,98.540,1.460,12.61,288,1.000,bicubic
resnext101_32x8d.tv_in1k,93.780,6.220,98.960,1.040,88.79,224,0.875,bilinear
dpn131.mx_in1k,93.780,6.220,98.850,1.150,79.25,224,0.875,bicubic
resnet101.gluon_in1k,93.760,6.240,98.700,1.300,44.55,224,0.875,bicubic
tf_efficientnet_b0.ns_jft_in1k,93.750,6.250,98.970,1.030,5.29,224,0.875,bicubic
convnextv2_femto.fcmae_ft_in1k,93.750,6.250,98.930,1.070,5.23,288,0.950,bicubic
xception65.tf_in1k,93.750,6.250,98.860,1.140,39.92,299,0.903,bicubic
efficientnet_em.ra2_in1k,93.730,6.270,98.930,1.070,6.90,240,0.882,bicubic
hrnet_w40.ms_in1k,93.730,6.270,98.800,1.200,57.56,224,0.875,bilinear
wide_resnet101_2.tv_in1k,93.720,6.280,98.810,1.190,126.89,224,0.875,bilinear
tf_efficientnet_b1.aa_in1k,93.720,6.280,98.800,1.200,7.79,240,0.882,bicubic
tf_efficientnet_b2.in1k,93.710,6.290,98.930,1.070,9.11,260,0.890,bicubic
tf_efficientnetv2_b1.in1k,93.710,6.290,98.820,1.180,8.14,240,0.882,bicubic
levit_192.fb_dist_in1k,93.710,6.290,98.790,1.210,10.95,224,0.900,bicubic
levit_conv_192.fb_dist_in1k,93.710,6.290,98.790,1.210,10.95,224,0.900,bicubic
regnetx_040.pycls_in1k,93.690,6.310,98.930,1.070,22.12,224,0.875,bicubic
resnet101c.gluon_in1k,93.690,6.310,98.760,1.240,44.57,224,0.875,bicubic
rexnet_130.nav_in1k,93.680,6.320,98.700,1.300,7.56,224,0.875,bicubic
resnext50_32x4d.gluon_in1k,93.670,6.330,98.690,1.310,25.03,224,0.875,bicubic
resmlp_36_224.fb_in1k,93.650,6.350,98.950,1.050,44.69,224,0.875,bicubic
regnetx_064.pycls_in1k,93.640,6.360,99.040,0.960,26.21,224,0.875,bicubic
regnety_040.pycls_in1k,93.630,6.370,98.950,1.050,20.65,224,0.875,bicubic
fbnetv3_b.ra2_in1k,93.630,6.370,98.910,1.090,8.60,256,0.950,bilinear
resnet50.am_in1k,93.630,6.370,98.870,1.130,25.56,224,0.875,bicubic
hrnet_w44.ms_in1k,93.620,6.380,98.960,1.040,67.06,224,0.875,bilinear
tf_efficientnet_b1.ap_in1k,93.620,6.380,98.800,1.200,7.79,240,0.882,bicubic
legacy_xception.tf_in1k,93.620,6.380,98.770,1.230,22.86,299,0.897,bicubic
resnet34d.ra2_in1k,93.600,6.400,98.760,1.240,21.82,288,0.950,bicubic
res2net50_26w_6s.in1k,93.590,6.410,98.740,1.260,37.05,224,0.875,bilinear
resnet32ts.ra2_in1k,93.590,6.410,98.740,1.260,17.96,288,1.000,bicubic
halonet26t.a1h_in1k,93.590,6.410,98.630,1.370,12.48,256,0.950,bicubic
repvgg_b2.rvgg_in1k,93.580,6.420,99.070,0.930,89.02,224,0.875,bilinear
dla60_res2next.in1k,93.580,6.420,98.780,1.220,17.03,224,0.875,bilinear
tf_efficientnet_cc_b1_8e.in1k,93.580,6.420,98.690,1.310,39.72,240,0.882,bicubic
resnet50s.gluon_in1k,93.570,6.430,98.840,1.160,25.68,224,0.875,bicubic
inception_v3.gluon_in1k,93.560,6.440,98.840,1.160,23.83,299,0.875,bicubic
resnext50_32x4d.a3_in1k,93.550,6.450,98.820,1.180,25.03,224,0.950,bicubic
eca_halonext26ts.c1_in1k,93.550,6.450,98.680,1.320,10.76,256,0.940,bicubic
dla102x.in1k,93.540,6.460,98.850,1.150,26.31,224,0.875,bilinear
resnet50d.gluon_in1k,93.540,6.460,98.710,1.290,25.58,224,0.875,bicubic
res2net101_26w_4s.in1k,93.530,6.470,98.600,1.400,45.21,224,0.875,bilinear
convnext_tiny.fb_in22k_ft_in1k,93.530,6.470,98.570,1.430,28.59,288,1.000,bicubic
coat_tiny.in1k,93.510,6.490,98.680,1.320,5.50,224,0.900,bicubic
SelecSls60b.in1k,93.500,6.500,98.840,1.160,32.77,224,0.875,bicubic
gmlp_s16_224.ra3_in1k,93.500,6.500,98.780,1.220,19.42,224,0.875,bicubic
pvt_v2_b1.in1k,93.490,6.510,98.860,1.140,14.01,224,0.900,bicubic
hrnet_w18.ms_aug_in1k,93.480,6.520,98.980,1.020,21.30,224,0.950,bilinear
mobilevitv2_125.cvnets_in1k,93.480,6.520,98.840,1.160,7.48,256,0.888,bicubic
xception41.tf_in1k,93.480,6.520,98.750,1.250,26.97,299,0.903,bicubic
coat_lite_mini.in1k,93.470,6.530,98.770,1.230,11.01,224,0.900,bicubic
regnety_032.pycls_in1k,93.460,6.540,98.950,1.050,19.44,224,0.875,bicubic
wide_resnet50_2.tv_in1k,93.460,6.540,98.950,1.050,68.88,224,0.875,bilinear
legacy_seresnext50_32x4d.in1k,93.450,6.550,98.800,1.200,27.56,224,0.875,bilinear
cait_xxs24_224.fb_dist_in1k,93.450,6.550,98.780,1.220,11.96,224,1.000,bicubic
vit_small_patch16_224.augreg_in1k,93.450,6.550,98.780,1.220,22.05,224,0.900,bicubic
convnext_femto.d1_in1k,93.440,6.560,98.820,1.180,5.22,288,0.950,bicubic
repvgg_b1.rvgg_in1k,93.440,6.560,98.790,1.210,57.42,224,0.875,bilinear
botnet26t_256.c1_in1k,93.440,6.560,98.650,1.350,12.49,256,0.950,bicubic
lambda_resnet26rpt_256.c1_in1k,93.430,6.570,98.880,1.120,10.99,256,0.940,bicubic
lambda_resnet26t.c1_in1k,93.430,6.570,98.730,1.270,10.96,256,0.940,bicubic
vit_tiny_patch16_384.augreg_in21k_ft_in1k,93.420,6.580,98.830,1.170,5.79,384,1.000,bicubic
resmlp_24_224.fb_in1k,93.420,6.580,98.810,1.190,30.02,224,0.875,bicubic
legacy_seresnet152.in1k,93.410,6.590,98.850,1.150,66.82,224,0.875,bilinear
hrnet_w30.ms_in1k,93.410,6.590,98.830,1.170,37.71,224,0.875,bilinear
resnet50d.a3_in1k,93.410,6.590,98.750,1.250,25.58,224,0.950,bicubic
res2net50_26w_8s.in1k,93.410,6.590,98.690,1.310,48.40,224,0.875,bilinear
convnext_femto_ols.d1_in1k,93.390,6.610,98.910,1.090,5.23,288,0.950,bicubic
dla60_res2net.in1k,93.370,6.630,98.830,1.170,20.85,224,0.875,bilinear
xcit_tiny_12_p16_224.fb_dist_in1k,93.350,6.650,98.760,1.240,6.72,224,1.000,bicubic
eca_botnext26ts_256.c1_in1k,93.350,6.650,98.690,1.310,10.59,256,0.950,bicubic
seresnext26t_32x4d.bt_in1k,93.350,6.650,98.690,1.310,16.81,288,0.950,bicubic
vit_base_patch16_224.augreg_in1k,93.350,6.650,98.660,1.340,86.57,224,0.900,bicubic
xcit_nano_12_p8_384.fb_dist_in1k,93.300,6.700,98.860,1.140,3.05,384,1.000,bicubic
pit_xs_distilled_224.in1k,93.280,6.720,98.790,1.210,11.00,224,0.900,bicubic
cs3darknet_m.c2ns_in1k,93.280,6.720,98.720,1.280,9.31,288,0.950,bicubic
legacy_seresnet101.in1k,93.270,6.730,98.740,1.260,49.33,224,0.875,bilinear
dla102.in1k,93.250,6.750,98.790,1.210,33.27,224,0.875,bilinear
resnet152.tv_in1k,93.240,6.760,98.750,1.250,60.19,224,0.875,bilinear
regnetx_032.pycls_in1k,93.240,6.760,98.720,1.280,15.30,224,0.875,bicubic
mixnet_l.ft_in1k,93.240,6.760,98.700,1.300,7.33,224,0.875,bicubic
resnest26d.gluon_in1k,93.230,6.770,98.850,1.150,17.07,224,0.875,bilinear
dla60x.in1k,93.190,6.810,98.720,1.280,17.35,224,0.875,bilinear
tf_efficientnet_em.in1k,93.190,6.810,98.660,1.340,6.90,240,0.882,bicubic
inception_v3.tf_in1k,93.190,6.810,98.490,1.510,23.83,299,0.875,bicubic
res2net50_26w_4s.in1k,93.180,6.820,98.660,1.340,25.70,224,0.875,bilinear
regnety_008_tv.tv2_in1k,93.160,6.840,98.680,1.320,6.43,224,0.965,bicubic
mobilevit_s.cvnets_in1k,93.150,6.850,98.780,1.220,5.58,256,0.900,bicubic
res2next50.in1k,93.150,6.850,98.640,1.360,24.67,224,0.875,bilinear
vit_base_patch32_384.augreg_in1k,93.150,6.850,98.610,1.390,88.30,384,1.000,bicubic
mobilevitv2_100.cvnets_in1k,93.140,6.860,98.760,1.240,4.90,256,0.888,bicubic
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,93.140,6.860,98.310,1.690,119.42,256,0.900,bicubic
bat_resnext26ts.ch_in1k,93.120,6.880,98.730,1.270,10.73,256,0.900,bicubic
cs3darknet_focus_m.c2ns_in1k,93.100,6.900,98.750,1.250,9.30,288,0.950,bicubic
seresnext26d_32x4d.bt_in1k,93.060,6.940,98.710,1.290,16.81,288,0.950,bicubic
tf_efficientnetv2_b0.in1k,93.060,6.940,98.700,1.300,7.14,224,0.875,bicubic
repvgg_b1g4.rvgg_in1k,93.030,6.970,98.820,1.180,39.97,224,0.875,bilinear
levit_128.fb_dist_in1k,93.030,6.970,98.710,1.290,9.21,224,0.900,bicubic
levit_conv_128.fb_dist_in1k,93.030,6.970,98.700,1.300,9.21,224,0.900,bicubic
res2net50_14w_8s.in1k,93.030,6.970,98.700,1.300,25.06,224,0.875,bilinear
densenetblur121d.ra_in1k,93.030,6.970,98.600,1.400,8.00,288,0.950,bicubic
tf_mixnet_l.in1k,93.030,6.970,98.530,1.470,7.33,224,0.875,bicubic
efficientnet_b1.ft_in1k,93.020,6.980,98.710,1.290,7.79,256,1.000,bicubic
regnety_016.pycls_in1k,93.010,6.990,98.670,1.330,11.20,224,0.875,bicubic
SelecSls60.in1k,93.000,7.000,98.820,1.180,30.67,224,0.875,bicubic
resnet34.a1_in1k,93.000,7.000,98.630,1.370,21.80,288,1.000,bicubic
inception_v3.tf_adv_in1k,93.000,7.000,98.490,1.510,23.83,299,0.875,bicubic
visformer_tiny.in1k,92.980,7.020,98.730,1.270,10.32,224,0.900,bicubic
convnext_atto_ols.a2_in1k,92.980,7.020,98.670,1.330,3.70,288,0.950,bicubic
hardcorenas_f.miil_green_in1k,92.980,7.020,98.620,1.380,8.20,224,0.875,bilinear
efficientnet_b1_pruned.in1k,92.980,7.020,98.530,1.470,6.33,240,0.882,bicubic
hrnet_w32.ms_in1k,92.960,7.040,98.850,1.150,41.23,224,0.875,bilinear
seresnext26ts.ch_in1k,92.940,7.060,98.670,1.330,10.39,288,1.000,bicubic
tf_efficientnet_b1.in1k,92.940,7.060,98.660,1.340,7.79,240,0.882,bicubic
hardcorenas_e.miil_green_in1k,92.940,7.060,98.580,1.420,8.07,224,0.875,bilinear
resnet50.a3_in1k,92.940,7.060,98.530,1.470,25.56,224,0.950,bicubic
efficientnet_es.ra_in1k,92.920,7.080,98.690,1.310,5.44,224,0.875,bicubic
resnet26t.ra2_in1k,92.920,7.080,98.680,1.320,16.01,320,1.000,bicubic
convnextv2_atto.fcmae_ft_in1k,92.920,7.080,98.560,1.440,3.71,288,0.950,bicubic
resnet50c.gluon_in1k,92.910,7.090,98.700,1.300,25.58,224,0.875,bicubic
resnext50_32x4d.tv_in1k,92.900,7.100,98.730,1.270,25.03,224,0.875,bilinear
inception_v3.tv_in1k,92.900,7.100,98.320,1.680,23.83,299,0.875,bicubic
densenet161.tv_in1k,92.890,7.110,98.790,1.210,28.68,224,0.875,bicubic
pit_xs_224.in1k,92.890,7.110,98.770,1.230,10.62,224,0.900,bicubic
poolformerv2_s12.sail_in1k,92.890,7.110,98.530,1.470,11.89,224,1.000,bicubic
resnet101.tv_in1k,92.880,7.120,98.660,1.340,44.55,224,0.875,bilinear
resmlp_12_224.fb_distilled_in1k,92.870,7.130,98.620,1.380,15.35,224,0.875,bicubic
tf_efficientnet_cc_b0_8e.in1k,92.870,7.130,98.460,1.540,24.01,224,0.875,bicubic
coat_lite_tiny.in1k,92.860,7.140,98.630,1.370,5.72,224,0.900,bicubic
rexnet_100.nav_in1k,92.830,7.170,98.600,1.400,4.80,224,0.875,bicubic
tf_efficientnet_cc_b0_4e.in1k,92.820,7.180,98.440,1.560,13.31,224,0.875,bicubic
tinynet_a.in1k,92.800,7.200,98.560,1.440,6.19,192,0.875,bicubic
dpn68b.mx_in1k,92.790,7.210,98.520,1.480,12.61,224,0.875,bicubic
res2net50_48w_2s.in1k,92.780,7.220,98.470,1.530,25.29,224,0.875,bilinear
hrnet_w18.ms_in1k,92.770,7.230,98.660,1.340,21.30,224,0.875,bilinear
convnext_atto.d2_in1k,92.770,7.230,98.620,1.380,3.70,288,0.950,bicubic
ese_vovnet19b_dw.ra_in1k,92.760,7.240,98.650,1.350,6.54,288,0.950,bicubic
crossvit_9_dagger_240.in1k,92.760,7.240,98.490,1.510,8.78,240,0.875,bicubic
eca_resnext26ts.ch_in1k,92.750,7.250,98.710,1.290,10.30,288,1.000,bicubic
gcresnext26ts.ch_in1k,92.740,7.260,98.610,1.390,10.48,288,1.000,bicubic
densenet201.tv_in1k,92.700,7.300,98.640,1.360,20.01,224,0.875,bicubic
densenet121.ra_in1k,92.700,7.300,98.600,1.400,7.98,288,0.950,bicubic
repvgg_a2.rvgg_in1k,92.680,7.320,98.530,1.470,28.21,224,0.875,bilinear
gmixer_24_224.ra3_in1k,92.680,7.320,98.280,1.720,24.72,224,0.875,bicubic
legacy_seresnet50.in1k,92.670,7.330,98.650,1.350,28.09,224,0.875,bilinear
dla60.in1k,92.640,7.360,98.630,1.370,22.04,224,0.875,bilinear
tf_efficientnet_b0.ap_in1k,92.620,7.380,98.370,1.630,5.29,224,0.875,bicubic
mobilenetv2_120d.ra_in1k,92.610,7.390,98.510,1.490,5.83,224,0.875,bicubic
hardcorenas_d.miil_green_in1k,92.610,7.390,98.430,1.570,7.50,224,0.875,bilinear
legacy_seresnext26_32x4d.in1k,92.600,7.400,98.410,1.590,16.79,224,0.875,bicubic
tf_efficientnet_lite2.in1k,92.590,7.410,98.540,1.460,6.09,260,0.890,bicubic
resnet34.a2_in1k,92.570,7.430,98.570,1.430,21.80,288,1.000,bicubic
skresnet34.ra_in1k,92.570,7.430,98.520,1.480,22.28,224,0.875,bicubic
resnet50.gluon_in1k,92.560,7.440,98.550,1.450,25.56,224,0.875,bicubic
resnet26d.bt_in1k,92.550,7.450,98.650,1.350,16.01,288,0.950,bicubic
regnetx_016.pycls_in1k,92.530,7.470,98.550,1.450,9.19,224,0.875,bicubic
poolformer_s12.sail_in1k,92.500,7.500,98.400,1.600,11.92,224,0.900,bicubic
regnetx_008.tv2_in1k,92.490,7.510,98.430,1.570,7.26,224,0.965,bicubic
efficientnet_b0.ra_in1k,92.480,7.520,98.680,1.320,5.29,224,0.875,bicubic
xcit_tiny_12_p16_224.fb_in1k,92.480,7.520,98.630,1.370,6.72,224,1.000,bicubic
SelecSls42b.in1k,92.480,7.520,98.430,1.570,32.46,224,0.875,bicubic
gernet_s.idstcv_in1k,92.440,7.560,98.500,1.500,8.17,224,0.875,bilinear
xcit_nano_12_p8_224.fb_dist_in1k,92.430,7.570,98.530,1.470,3.05,224,1.000,bicubic
tf_efficientnet_b0.aa_in1k,92.400,7.600,98.470,1.530,5.29,224,0.875,bicubic
resnext26ts.ra2_in1k,92.380,7.620,98.390,1.610,10.30,288,1.000,bicubic
seresnet50.a3_in1k,92.360,7.640,98.330,1.670,28.09,224,0.950,bicubic
hardcorenas_c.miil_green_in1k,92.350,7.650,98.340,1.660,5.52,224,0.875,bilinear
convmixer_1024_20_ks9_p14.in1k,92.330,7.670,98.430,1.570,24.38,224,0.960,bicubic
dpn68.mx_in1k,92.300,7.700,98.610,1.390,12.61,224,0.875,bicubic
densenet169.tv_in1k,92.300,7.700,98.590,1.410,14.15,224,0.875,bicubic
tf_efficientnet_lite1.in1k,92.290,7.710,98.500,1.500,5.42,240,0.882,bicubic
resnet34.bt_in1k,92.280,7.720,98.600,1.400,21.80,288,0.950,bicubic
tf_efficientnet_b0.in1k,92.280,7.720,98.550,1.450,5.29,224,0.875,bicubic
mixnet_m.ft_in1k,92.270,7.730,98.360,1.640,5.01,224,0.875,bicubic
mobilenetv3_large_100.miil_in21k_ft_in1k,92.260,7.740,98.240,1.760,5.48,224,0.875,bilinear
tf_mixnet_m.in1k,92.210,7.790,98.420,1.580,5.01,224,0.875,bicubic
vit_small_patch32_224.augreg_in21k_ft_in1k,92.140,7.860,98.520,1.480,22.88,224,0.900,bicubic
xcit_nano_12_p16_384.fb_dist_in1k,92.140,7.860,98.510,1.490,3.05,384,1.000,bicubic
resmlp_12_224.fb_in1k,92.120,7.880,98.570,1.430,15.35,224,0.875,bicubic
resnet26.bt_in1k,92.120,7.880,98.550,1.450,16.00,288,0.950,bicubic
resnet50.tv_in1k,92.120,7.880,98.410,1.590,25.56,224,0.875,bilinear
tf_efficientnet_es.in1k,92.110,7.890,98.430,1.570,5.44,224,0.875,bicubic
mobilenetv2_140.ra_in1k,92.050,7.950,98.250,1.750,6.11,224,0.875,bicubic
mobilevitv2_075.cvnets_in1k,91.970,8.030,98.300,1.700,2.87,256,0.888,bicubic
hardcorenas_b.miil_green_in1k,91.920,8.080,98.410,1.590,5.18,224,0.875,bilinear
vit_tiny_patch16_224.augreg_in21k_ft_in1k,91.920,8.080,98.340,1.660,5.72,224,0.900,bicubic
regnety_008.pycls_in1k,91.890,8.110,98.410,1.590,6.26,224,0.875,bicubic
efficientformerv2_s0.snap_dist_in1k,91.870,8.130,98.370,1.630,3.60,224,0.950,bicubic
mixnet_s.ft_in1k,91.770,8.230,98.310,1.690,4.13,224,0.875,bicubic
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,91.730,8.270,98.430,1.570,6.36,384,1.000,bicubic
efficientnet_es_pruned.in1k,91.690,8.310,98.410,1.590,5.44,224,0.875,bicubic
repvgg_b0.rvgg_in1k,91.680,8.320,98.450,1.550,15.82,224,0.875,bilinear
tf_mixnet_s.in1k,91.680,8.320,98.240,1.760,4.13,224,0.875,bicubic
semnasnet_100.rmsp_in1k,91.670,8.330,98.290,1.710,3.89,224,0.875,bicubic
regnety_004.tv2_in1k,91.620,8.380,98.290,1.710,4.34,224,0.965,bicubic
hardcorenas_a.miil_green_in1k,91.620,8.380,98.170,1.830,5.26,224,0.875,bilinear
edgenext_x_small.in1k,91.580,8.420,98.190,1.810,2.34,288,1.000,bicubic
regnety_006.pycls_in1k,91.560,8.440,98.430,1.570,6.06,224,0.875,bicubic
mobilenetv3_rw.rmsp_in1k,91.550,8.450,98.280,1.720,5.48,224,0.875,bicubic
levit_128s.fb_dist_in1k,91.510,8.490,98.400,1.600,7.78,224,0.900,bicubic
levit_conv_128s.fb_dist_in1k,91.500,8.500,98.400,1.600,7.78,224,0.900,bicubic
legacy_seresnet34.in1k,91.490,8.510,98.200,1.800,21.96,224,0.875,bilinear
mobilenetv3_large_100.ra_in1k,91.470,8.530,98.320,1.680,5.48,224,0.875,bicubic
tf_mobilenetv3_large_100.in1k,91.420,8.580,98.260,1.740,5.48,224,0.875,bilinear
densenet121.tv_in1k,91.400,8.600,98.250,1.750,7.98,224,0.875,bicubic
mobilenetv2_110d.ra_in1k,91.330,8.670,98.190,1.810,4.52,224,0.875,bicubic
tf_efficientnet_lite0.in1k,91.280,8.720,98.090,1.910,4.65,224,0.875,bicubic
fbnetc_100.rmsp_in1k,91.280,8.720,97.840,2.160,5.57,224,0.875,bilinear
efficientnet_lite0.ra_in1k,91.260,8.740,98.240,1.760,4.65,224,0.875,bicubic
dla34.in1k,91.220,8.780,98.170,1.830,15.74,224,0.875,bilinear
mobilevit_xs.cvnets_in1k,91.210,8.790,98.220,1.780,2.32,256,0.900,bicubic
mnasnet_100.rmsp_in1k,91.210,8.790,98.060,1.940,4.38,224,0.875,bicubic
regnetx_008.pycls_in1k,91.180,8.820,98.370,1.630,7.26,224,0.875,bicubic
hrnet_w18_small_v2.ms_in1k,91.160,8.840,98.340,1.660,15.60,224,0.875,bilinear
regnetx_004_tv.tv2_in1k,91.160,8.840,98.110,1.890,5.50,224,0.965,bicubic
resnest14d.gluon_in1k,91.150,8.850,98.330,1.670,10.61,224,0.875,bilinear
mixer_b16_224.goog_in21k_ft_in1k,91.140,8.860,97.400,2.600,59.88,224,0.875,bicubic
tinynet_b.in1k,91.120,8.880,98.060,1.940,3.73,188,0.875,bicubic
xcit_nano_12_p8_224.fb_in1k,91.100,8.900,98.230,1.770,3.05,224,1.000,bicubic
resnet34.gluon_in1k,91.090,8.910,98.170,1.830,21.80,224,0.875,bicubic
deit_tiny_distilled_patch16_224.fb_in1k,91.080,8.920,98.270,1.730,5.91,224,0.900,bicubic
resnet18.fb_swsl_ig1b_ft_in1k,91.080,8.920,98.200,1.800,11.69,224,0.875,bilinear
crossvit_9_240.in1k,91.060,8.940,98.300,1.700,8.55,240,0.875,bicubic
vgg19_bn.tv_in1k,90.990,9.010,98.100,1.900,143.68,224,0.875,bilinear
resnet18d.ra2_in1k,90.800,9.200,98.160,1.840,11.71,288,0.950,bicubic
regnetx_006.pycls_in1k,90.790,9.210,98.090,1.910,6.20,224,0.875,bicubic
regnety_004.pycls_in1k,90.770,9.230,98.070,1.930,4.34,224,0.875,bicubic
pit_ti_distilled_224.in1k,90.730,9.270,98.250,1.750,5.10,224,0.900,bicubic
resnet18.fb_ssl_yfcc100m_ft_in1k,90.700,9.300,98.030,1.970,11.69,224,0.875,bilinear
spnasnet_100.rmsp_in1k,90.590,9.410,97.950,2.050,4.42,224,0.875,bilinear
vit_base_patch32_224.augreg_in1k,90.590,9.410,97.720,2.280,88.22,224,0.900,bicubic
convit_tiny.fb_in1k,90.550,9.450,98.190,1.810,5.71,224,0.875,bicubic
vgg16_bn.tv_in1k,90.540,9.460,97.990,2.010,138.37,224,0.875,bilinear
crossvit_tiny_240.in1k,90.530,9.470,97.950,2.050,7.01,240,0.875,bicubic
ghostnet_100.in1k,90.450,9.550,97.820,2.180,5.18,224,0.875,bilinear
pit_ti_224.in1k,90.420,9.580,98.010,1.990,4.85,224,0.900,bicubic
tf_mobilenetv3_large_075.in1k,90.330,9.670,97.870,2.130,3.99,224,0.875,bilinear
resnet34.tv_in1k,90.300,9.700,97.970,2.030,21.80,224,0.875,bilinear
resnet34.a3_in1k,90.240,9.760,97.880,2.120,21.80,224,0.950,bicubic
semnasnet_075.rmsp_in1k,90.220,9.780,97.950,2.050,2.91,224,0.875,bicubic
resnet18.a1_in1k,90.200,9.800,97.760,2.240,11.69,288,1.000,bicubic
xcit_nano_12_p16_224.fb_dist_in1k,90.190,9.810,97.750,2.250,3.05,224,1.000,bicubic
skresnet18.ra_in1k,90.180,9.820,97.780,2.220,11.96,224,0.875,bicubic
hrnet_w18_small.ms_in1k,89.870,10.130,97.890,2.110,13.19,224,0.875,bilinear
vit_base_patch32_224.sam_in1k,89.870,10.130,97.600,2.400,88.22,224,0.900,bicubic
mobilenetv2_100.ra_in1k,89.850,10.150,97.830,2.170,3.50,224,0.875,bicubic
edgenext_xx_small.in1k,89.800,10.200,97.500,2.500,1.33,288,1.000,bicubic
vgg19.tv_in1k,89.680,10.320,97.550,2.450,143.67,224,0.875,bilinear
deit_tiny_patch16_224.fb_in1k,89.610,10.390,97.960,2.040,5.72,224,0.900,bicubic
resnet18.a2_in1k,89.480,10.520,97.630,2.370,11.69,288,1.000,bicubic
regnetx_004.pycls_in1k,89.460,10.540,97.760,2.240,5.16,224,0.875,bicubic
vgg16.tv_in1k,89.370,10.630,97.520,2.480,138.36,224,0.875,bilinear
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,89.340,10.660,97.700,2.300,6.34,224,0.900,bicubic
legacy_seresnet18.in1k,89.260,10.740,97.690,2.310,11.78,224,0.875,bicubic
resnet14t.c3_in1k,89.250,10.750,97.440,2.560,10.08,224,0.950,bicubic
vgg13_bn.tv_in1k,89.200,10.800,97.530,2.470,133.05,224,0.875,bilinear
tf_mobilenetv3_large_minimal_100.in1k,89.160,10.840,97.310,2.690,3.92,224,0.875,bilinear
mobilevitv2_050.cvnets_in1k,89.030,10.970,97.590,2.410,1.37,256,0.888,bicubic
pvt_v2_b0.in1k,88.970,11.030,97.690,2.310,3.67,224,0.900,bicubic
xcit_nano_12_p16_224.fb_in1k,88.970,11.030,97.410,2.590,3.05,224,1.000,bicubic
lcnet_100.ra2_in1k,88.910,11.090,97.380,2.620,2.95,224,0.875,bicubic
resnet18.gluon_in1k,88.660,11.340,97.090,2.910,11.69,224,0.875,bicubic
tinynet_c.in1k,88.400,11.600,97.260,2.740,2.46,184,0.875,bicubic
vgg11_bn.tv_in1k,88.400,11.600,97.250,2.750,132.87,224,0.875,bilinear
regnety_002.pycls_in1k,88.190,11.810,97.440,2.560,3.16,224,0.875,bicubic
resnet18.tv_in1k,88.150,11.850,97.120,2.880,11.69,224,0.875,bilinear
mobilevit_xxs.cvnets_in1k,87.930,12.070,97.190,2.810,1.27,256,0.900,bicubic
vgg13.tv_in1k,87.570,12.430,97.110,2.890,133.05,224,0.875,bilinear
regnetx_002.pycls_in1k,87.360,12.640,97.000,3.000,2.68,224,0.875,bicubic
vgg11.tv_in1k,87.350,12.650,97.100,2.900,132.86,224,0.875,bilinear
dla60x_c.in1k,87.130,12.870,97.140,2.860,1.32,224,0.875,bilinear
resnet18.a3_in1k,87.070,12.930,96.660,3.340,11.69,224,0.950,bicubic
mixer_l16_224.goog_in21k_ft_in1k,86.990,13.010,94.070,5.930,208.20,224,0.875,bicubic
lcnet_075.ra2_in1k,86.960,13.040,96.550,3.450,2.36,224,0.875,bicubic
resnet10t.c3_in1k,86.680,13.320,96.730,3.270,5.44,224,0.950,bicubic
mobilenetv3_small_100.lamb_in1k,86.180,13.820,96.450,3.550,2.54,224,0.875,bicubic
tf_mobilenetv3_small_100.in1k,85.940,14.060,96.400,3.600,2.54,224,0.875,bilinear
mnasnet_small.lamb_in1k,85.480,14.520,96.000,4.000,2.03,224,0.875,bicubic
tinynet_d.in1k,85.440,14.560,96.020,3.980,2.34,152,0.875,bicubic
dla46x_c.in1k,85.430,14.570,96.430,3.570,1.07,224,0.875,bilinear
mobilenetv2_050.lamb_in1k,84.990,15.010,95.620,4.380,1.97,224,0.875,bicubic
dla46_c.in1k,84.700,15.300,96.210,3.790,1.30,224,0.875,bilinear
tf_mobilenetv3_small_075.in1k,84.500,15.500,95.880,4.120,2.04,224,0.875,bilinear
mobilenetv3_small_075.lamb_in1k,84.120,15.880,95.520,4.480,2.04,224,0.875,bicubic
lcnet_050.ra2_in1k,83.030,16.970,95.000,5.000,1.88,224,0.875,bicubic
tf_mobilenetv3_small_minimal_100.in1k,82.670,17.330,95.020,4.980,2.04,224,0.875,bilinear
tinynet_e.in1k,79.820,20.180,93.970,6.030,2.04,106,0.875,bicubic
mobilenetv3_small_050.lamb_in1k,78.090,21.910,93.010,6.990,1.59,224,0.875,bicubic
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-r.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
convnext_xxlarge.clip_laion2b_soup_ft_in1k,90.623,9.377,97.913,2.087,846.47,256,1.000,bicubic,-7.127,-1.897,+18
eva_giant_patch14_336.clip_ft_in1k,90.550,9.450,97.230,2.770,"1,013.01",336,1.000,bicubic,-7.310,-2.650,+6
eva02_large_patch14_448.mim_m38m_ft_in1k,90.460,9.540,97.267,2.733,305.08,448,1.000,bicubic,-7.370,-2.553,+6
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,90.297,9.703,97.170,2.830,305.08,448,1.000,bicubic,-7.733,-2.720,-2
eva_giant_patch14_224.clip_ft_in1k,89.843,10.157,97.023,2.977,"1,012.56",224,0.900,bicubic,-7.727,-2.687,+29
eva_giant_patch14_336.m30m_ft_in22k_in1k,88.590,11.410,95.920,4.080,"1,013.01",336,1.000,bicubic,-9.400,-3.980,-2
eva_giant_patch14_560.m30m_ft_in22k_in1k,88.397,11.603,95.610,4.390,"1,014.45",560,1.000,bicubic,-9.603,-4.250,-4
regnety_1280.swag_ft_in1k,88.257,11.743,96.477,3.523,644.81,384,1.000,bicubic,-9.523,-3.383,+6
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,87.983,12.017,95.610,4.390,305.08,448,1.000,bicubic,-10.177,-4.270,-8
eva02_large_patch14_448.mim_in22k_ft_in1k,87.587,12.413,95.733,4.267,305.08,448,1.000,bicubic,-10.273,-4.057,-3
regnety_1280.swag_lc_in1k,86.947,13.053,95.737,4.263,644.81,224,0.965,bicubic,-10.443,-4.003,+36
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,84.437,15.563,94.297,5.703,200.13,384,1.000,bicubic,-12.953,-5.433,+34
vit_large_patch14_clip_336.openai_ft_in12k_in1k,83.927,16.073,93.873,6.127,304.53,336,1.000,bicubic,-13.673,-5.857,+18
vit_large_patch14_clip_336.laion2b_ft_in1k,83.617,16.383,93.513,6.487,304.53,336,1.000,bicubic,-13.613,-6.207,+62
eva_large_patch14_336.in22k_ft_in1k,83.520,16.480,93.103,6.897,304.53,336,1.000,bicubic,-14.290,-6.757,-4
vit_huge_patch14_clip_224.laion2b_ft_in1k,83.263,16.737,93.133,6.867,632.05,224,1.000,bicubic,-13.837,-6.557,+79
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,83.047,16.953,92.837,7.163,632.46,336,1.000,bicubic,-14.563,-6.943,+12
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,82.797,17.203,92.613,7.387,632.05,224,1.000,bicubic,-14.563,-7.187,+36
vit_large_patch14_clip_224.openai_ft_in1k,82.320,17.680,92.910,7.090,304.20,224,1.000,bicubic,-15.120,-6.770,+24
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,82.217,17.783,92.433,7.567,200.13,384,1.000,bicubic,-15.253,-7.327,+19
vit_large_patch14_clip_224.laion2b_ft_in1k,81.703,18.297,92.260,7.740,304.20,224,1.000,bicubic,-15.317,-7.410,+89
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,81.347,18.653,91.963,8.037,200.13,320,1.000,bicubic,-15.913,-7.747,+47
regnety_320.swag_lc_in1k,81.317,18.683,93.153,6.847,145.05,224,0.965,bicubic,-15.463,-6.467,+120
eva_large_patch14_196.in22k_ft_in1k,81.300,18.700,91.530,8.470,304.14,196,1.000,bicubic,-16.220,-8.260,+12
regnety_320.swag_ft_in1k,81.200,18.800,92.703,7.297,145.05,384,1.000,bicubic,-16.180,-7.017,+23
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,80.870,19.130,91.913,8.087,200.13,256,1.000,bicubic,-16.260,-7.867,+64
eva_large_patch14_336.in22k_ft_in22k_in1k,80.083,19.917,89.357,10.643,304.53,336,1.000,bicubic,-17.777,-10.443,-21
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,79.467,20.533,89.197,10.803,468.53,224,0.875,bilinear,-17.303,-10.423,+121
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,78.830,21.170,88.473,11.527,194.03,224,0.875,bilinear,-17.600,-11.157,+179
vit_large_patch14_clip_224.openai_ft_in12k_in1k,78.677,21.323,88.917,11.083,304.20,224,1.000,bicubic,-18.933,-10.813,0
eva_large_patch14_196.in22k_ft_in22k_in1k,78.500,21.500,88.330,11.670,304.14,196,1.000,bicubic,-19.120,-11.480,-4
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,78.427,21.573,88.500,11.500,304.53,336,1.000,bicubic,-19.033,-11.280,+8
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,78.260,21.740,88.673,11.327,304.20,224,1.000,bicubic,-19.130,-11.057,+12
regnety_160.swag_lc_in1k,78.200,21.800,91.667,8.333,83.59,224,0.965,bicubic,-18.250,-7.943,+168
regnety_160.swag_ft_in1k,77.680,22.320,90.733,9.267,83.59,384,1.000,bicubic,-19.490,-8.907,+49
eva02_base_patch14_448.mim_in22k_ft_in1k,77.607,22.393,89.303,10.697,87.12,448,1.000,bicubic,-20.113,-10.457,-14
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,77.497,22.503,88.490,11.510,87.12,448,1.000,bicubic,-20.113,-11.330,-9
tf_efficientnet_l2.ns_jft_in1k_475,76.487,23.513,88.647,11.353,480.31,475,0.936,bicubic,-21.263,-11.143,-20
beitv2_large_patch16_224.in1k_ft_in22k_in1k,76.353,23.647,87.090,12.910,304.43,224,0.950,bicubic,-21.397,-12.730,-19
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,76.300,23.700,87.743,12.257,194.03,224,0.875,bilinear,-19.970,-11.757,+200
convnextv2_huge.fcmae_ft_in22k_in1k_384,75.950,24.050,86.637,13.363,660.29,384,1.000,bicubic,-21.920,-13.273,-36
convnextv2_huge.fcmae_ft_in22k_in1k_512,75.827,24.173,86.947,13.053,660.29,512,1.000,bicubic,-21.983,-12.843,-32
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,75.797,24.203,86.197,13.803,88.79,224,0.875,bilinear,-20.143,-13.183,+266
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,75.600,24.400,86.943,13.057,88.79,224,0.875,bilinear,-20.650,-12.597,+197
convnext_base.clip_laiona_augreg_ft_in1k_384,75.210,24.790,88.580,11.420,88.59,384,1.000,bicubic,-21.650,-11.110,+85
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,75.173,24.827,88.460,11.540,88.59,384,1.000,bicubic,-21.867,-11.210,+57
tf_efficientnet_l2.ns_jft_in1k,74.657,25.343,87.557,12.443,480.31,800,0.960,bicubic,-23.123,-12.263,-34
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,73.733,26.267,87.343,12.657,88.59,256,1.000,bicubic,-23.057,-12.337,+94
convnext_base.clip_laion2b_augreg_ft_in1k,73.400,26.600,87.147,12.853,88.59,256,1.000,bicubic,-23.160,-12.483,+132
beit_large_patch16_384.in22k_ft_in22k_in1k,73.277,26.723,85.030,14.970,305.00,384,1.000,bicubic,-24.533,-14.810,-38
beit_large_patch16_512.in22k_ft_in22k_in1k,73.160,26.840,85.090,14.910,305.67,512,1.000,bicubic,-24.620,-14.800,-36
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,72.657,27.343,85.163,14.837,44.18,224,0.875,bilinear,-23.383,-14.247,+235
maxvit_xlarge_tf_512.in21k_ft_in1k,71.873,28.127,82.927,17.073,475.77,512,1.000,bicubic,-25.887,-16.893,-36
maxvit_xlarge_tf_384.in21k_ft_in1k,71.700,28.300,82.713,17.287,475.32,384,1.000,bicubic,-26.040,-17.137,-33
beit_large_patch16_224.in22k_ft_in22k_in1k,71.040,28.960,83.430,16.570,304.43,224,0.900,bicubic,-26.440,-16.260,-17
deit3_huge_patch14_224.fb_in22k_ft_in1k,70.817,29.183,82.200,17.800,632.13,224,1.000,bicubic,-26.433,-17.520,+16
vit_base_patch16_clip_384.laion2b_ft_in1k,70.773,29.227,83.820,16.180,86.86,384,1.000,bicubic,-26.127,-15.850,+66
caformer_b36.sail_in22k_ft_in1k_384,70.750,29.250,82.647,17.353,98.75,384,1.000,bicubic,-26.910,-17.213,-34
deit3_large_patch16_384.fb_in22k_ft_in1k,70.580,29.420,82.437,17.563,304.76,384,1.000,bicubic,-27.000,-17.273,-26
beitv2_large_patch16_224.in1k_ft_in1k,70.403,29.597,83.373,16.627,304.43,224,0.950,bicubic,-26.907,-16.387,0
maxvit_base_tf_512.in21k_ft_in1k,70.390,29.610,81.600,18.400,119.88,512,1.000,bicubic,-27.370,-18.260,-45
maxvit_large_tf_512.in21k_ft_in1k,70.380,29.620,81.650,18.350,212.33,512,1.000,bicubic,-27.290,-18.080,-39
maxvit_large_tf_384.in21k_ft_in1k,70.010,29.990,81.037,18.963,212.03,384,1.000,bicubic,-27.650,-18.783,-38
deit3_large_patch16_224.fb_in22k_ft_in1k,69.713,30.287,81.197,18.803,304.37,224,1.000,bicubic,-27.597,-18.483,-3
maxvit_base_tf_384.in21k_ft_in1k,69.557,30.443,80.730,19.270,119.65,384,1.000,bicubic,-28.003,-19.030,-30
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,69.123,30.877,80.063,19.937,116.14,384,1.000,bicubic,-28.217,-19.627,-11
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,68.977,31.023,82.800,17.200,25.03,224,0.875,bilinear,-26.653,-16.610,+301
vit_base_patch16_clip_224.laion2b_ft_in1k,68.770,31.230,82.507,17.493,86.57,224,1.000,bicubic,-27.550,-17.013,+160
convnextv2_large.fcmae_ft_in22k_in1k_384,68.653,31.347,81.070,18.930,197.96,384,1.000,bicubic,-28.977,-18.730,-43
caformer_b36.sail_in22k_ft_in1k,68.600,31.400,80.857,19.143,98.75,224,1.000,bicubic,-28.760,-18.973,-17
resnet50.fb_swsl_ig1b_ft_in1k,68.287,31.713,83.307,16.693,25.56,224,0.875,bilinear,-26.913,-16.083,+379
convnext_xlarge.fb_in22k_ft_in1k_384,68.157,31.843,80.453,19.547,350.20,384,1.000,bicubic,-29.433,-19.317,-40
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,68.077,31.923,80.740,19.260,116.09,384,1.000,bicubic,-29.373,-19.020,-31
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,67.667,32.333,80.100,19.900,196.74,384,1.000,bicubic,-29.613,-19.680,-8
tf_efficientnet_b7.ns_jft_in1k,67.510,32.490,81.380,18.620,66.35,600,0.949,bicubic,-29.680,-18.240,+6
vit_base_patch16_clip_384.openai_ft_in1k,67.360,32.640,81.687,18.313,86.86,384,1.000,bicubic,-29.450,-18.023,+62
vit_large_patch16_384.augreg_in21k_ft_in1k,67.060,32.940,78.703,21.297,304.72,384,1.000,bicubic,-30.350,-21.077,-33
convnextv2_base.fcmae_ft_in22k_in1k_384,67.030,32.970,79.800,20.200,88.72,384,1.000,bicubic,-30.350,-19.960,-29
convformer_b36.sail_in22k_ft_in1k_384,66.823,33.177,79.443,20.557,99.88,384,1.000,bicubic,-30.667,-20.317,-42
convnext_large.fb_in22k_ft_in1k_384,66.673,33.327,79.797,20.203,197.77,384,1.000,bicubic,-30.627,-19.963,-18
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,66.580,33.420,78.413,21.587,73.88,384,1.000,bicubic,-30.790,-21.287,-30
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,66.530,33.470,78.027,21.973,116.14,224,0.950,bicubic,-30.580,-21.573,+12
swin_large_patch4_window12_384.ms_in22k_ft_in1k,66.287,33.713,79.747,20.253,196.74,384,1.000,bicubic,-30.883,-19.993,+3
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,66.150,33.850,78.880,21.120,86.86,384,1.000,bicubic,-31.070,-20.820,-6
vit_base_patch16_clip_224.openai_ft_in1k,66.030,33.970,80.983,19.017,86.57,224,0.900,bicubic,-30.280,-18.517,+146
beitv2_base_patch16_224.in1k_ft_in22k_in1k,65.757,34.243,78.893,21.107,86.53,224,0.900,bicubic,-31.153,-20.837,+36
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,65.733,34.267,79.317,20.683,87.92,384,1.000,bicubic,-31.537,-20.473,-20
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,65.640,34.360,78.480,21.520,196.74,256,0.900,bicubic,-31.600,-21.230,-14
tf_efficientnet_b6.ns_jft_in1k,65.590,34.410,79.563,20.437,43.04,528,0.942,bicubic,-31.430,-20.147,+19
caformer_m36.sail_in22k_ft_in1k_384,65.570,34.430,78.713,21.287,56.20,384,1.000,bicubic,-31.800,-21.077,-40
convformer_b36.sail_in22k_ft_in1k,65.513,34.487,78.140,21.860,99.88,224,1.000,bicubic,-31.747,-21.610,-23
convnext_xlarge.fb_in22k_ft_in1k,65.373,34.627,78.350,21.650,350.20,288,1.000,bicubic,-32.077,-21.470,-51
vit_base_patch16_clip_384.openai_ft_in12k_in1k,65.353,34.647,78.957,21.043,86.86,384,0.950,bicubic,-31.767,-20.613,-2
convnext_base.fb_in22k_ft_in1k_384,64.903,35.097,78.387,21.613,88.59,384,1.000,bicubic,-32.357,-21.353,-24
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,64.763,35.237,77.767,22.233,86.57,224,0.950,bicubic,-31.857,-21.793,+75
convnextv2_large.fcmae_ft_in22k_in1k,64.710,35.290,78.157,21.843,197.96,288,1.000,bicubic,-32.600,-21.583,-37
vit_large_patch16_224.augreg_in21k_ft_in1k,64.360,35.640,76.187,23.813,304.33,224,0.900,bicubic,-32.340,-23.383,+60
convnext_large.fb_in22k_ft_in1k,64.263,35.737,77.773,22.227,197.77,288,1.000,bicubic,-32.957,-21.957,-21
vit_large_r50_s32_384.augreg_in21k_ft_in1k,64.107,35.893,75.847,24.153,329.09,384,1.000,bicubic,-32.843,-23.783,+17
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,64.010,35.990,77.513,22.487,116.09,224,0.950,bicubic,-33.080,-22.167,-4
swin_large_patch4_window7_224.ms_in22k_ft_in1k,63.887,36.113,78.187,21.813,196.53,224,0.900,bicubic,-33.053,-21.483,+18
beit_base_patch16_384.in22k_ft_in22k_in1k,63.623,36.377,78.103,21.897,86.74,384,1.000,bicubic,-33.697,-21.617,-45
caformer_m36.sail_in22k_ft_in1k,63.490,36.510,76.917,23.083,56.20,224,1.000,bicubic,-33.530,-22.813,+4
swin_base_patch4_window12_384.ms_in22k_ft_in1k,63.483,36.517,78.050,21.950,87.90,384,1.000,bicubic,-33.647,-21.670,-15
convnextv2_base.fcmae_ft_in22k_in1k,63.307,36.693,77.163,22.837,88.72,288,1.000,bicubic,-33.893,-22.597,-25
caformer_s36.sail_in22k_ft_in1k_384,63.263,36.737,77.460,22.540,39.30,384,1.000,bicubic,-34.027,-22.320,-41
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,63.183,36.817,77.093,22.907,87.92,256,0.900,bicubic,-33.867,-22.497,-6
tf_efficientnet_b5.ns_jft_in1k,63.053,36.947,77.790,22.210,30.39,456,0.934,bicubic,-33.817,-21.850,+21
vit_base_patch16_clip_224.openai_ft_in12k_in1k,62.940,37.060,76.590,23.410,86.57,224,0.950,bicubic,-33.570,-22.960,+84
deit3_base_patch16_384.fb_in22k_ft_in1k,62.637,37.363,75.550,24.450,86.88,384,1.000,bicubic,-34.603,-24.190,-35
convnext_base.fb_in22k_ft_in1k,62.537,37.463,76.550,23.450,88.59,288,1.000,bicubic,-34.663,-23.210,-32
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,62.417,37.583,75.130,24.870,73.88,224,0.950,bicubic,-34.763,-24.520,-29
vit_base_patch8_224.augreg2_in21k_ft_in1k,62.400,37.600,76.600,23.400,86.58,224,0.900,bicubic,-34.550,-23.040,+4
tf_efficientnetv2_l.in21k_ft_in1k,62.370,37.630,76.757,23.243,118.52,480,1.000,bicubic,-34.950,-22.883,-56
vit_base_patch8_224.augreg_in21k_ft_in1k,62.177,37.823,75.630,24.370,86.58,224,0.900,bicubic,-34.913,-23.980,-18
convformer_m36.sail_in22k_ft_in1k_384,62.107,37.893,75.547,24.453,57.05,384,1.000,bicubic,-35.263,-24.133,-64
tf_efficientnetv2_xl.in21k_ft_in1k,62.083,37.917,75.663,24.337,208.12,512,1.000,bicubic,-35.247,-23.937,-61
hrnet_w48_ssld.paddle_in1k,61.927,38.073,75.163,24.837,77.47,288,1.000,bilinear,-34.613,-24.477,+67
deit3_base_patch16_224.fb_in22k_ft_in1k,61.803,38.197,74.710,25.290,86.59,224,1.000,bicubic,-35.057,-24.910,+12
beitv2_base_patch16_224.in1k_ft_in1k,61.427,38.573,75.930,24.070,86.53,224,0.900,bicubic,-35.323,-23.670,+32
coatnet_2_rw_224.sw_in12k_ft_in1k,61.277,38.723,73.967,26.033,73.87,224,0.950,bicubic,-35.713,-25.693,-8
convformer_m36.sail_in22k_ft_in1k,61.263,38.737,74.617,25.383,57.05,224,1.000,bicubic,-35.807,-25.133,-23
tf_efficientnet_b4.ns_jft_in1k,61.233,38.767,76.167,23.833,19.34,380,0.922,bicubic,-35.477,-23.473,+32
convnextv2_huge.fcmae_ft_in1k,61.197,38.803,74.500,25.500,660.29,288,1.000,bicubic,-36.053,-25.220,-53
maxvit_base_tf_512.in1k,61.103,38.897,74.053,25.947,119.88,512,1.000,bicubic,-36.067,-25.627,-38
caformer_s36.sail_in22k_ft_in1k,60.763,39.237,75.170,24.830,39.30,224,1.000,bicubic,-36.057,-24.520,+10
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,60.353,39.647,73.800,26.200,88.30,384,1.000,bicubic,-36.257,-25.680,+44
beit_base_patch16_224.in22k_ft_in22k_in1k,60.317,39.683,75.593,24.407,86.53,224,0.900,bicubic,-36.343,-24.067,+37
tf_efficientnetv2_m.in21k_ft_in1k,60.263,39.737,75.063,24.937,54.14,480,1.000,bicubic,-36.737,-24.567,-18
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,60.240,39.760,73.547,26.453,88.34,448,1.000,bicubic,-36.330,-25.973,+48
vit_base_patch16_384.augreg_in21k_ft_in1k,60.183,39.817,73.850,26.150,86.86,384,1.000,bicubic,-36.837,-25.860,-22
convformer_s36.sail_in22k_ft_in1k_384,60.067,39.933,74.130,25.870,40.01,384,1.000,bicubic,-36.993,-25.580,-32
convnext_small.fb_in22k_ft_in1k_384,59.923,40.077,74.487,25.513,50.22,384,1.000,bicubic,-37.187,-25.153,-40
maxvit_large_tf_512.in1k,59.887,40.113,72.847,27.153,212.33,512,1.000,bicubic,-37.163,-26.813,-32
swin_base_patch4_window7_224.ms_in22k_ft_in1k,59.527,40.473,74.247,25.753,87.77,224,0.900,bicubic,-37.153,-25.423,+28
vit_base_patch32_clip_224.laion2b_ft_in1k,59.163,40.837,73.883,26.117,88.22,224,0.900,bicubic,-35.587,-25.187,+410
maxvit_base_tf_384.in1k,59.073,40.927,71.687,28.313,119.65,384,1.000,bicubic,-38.047,-27.953,-45
vit_base_patch16_224.augreg2_in21k_ft_in1k,59.053,40.947,73.603,26.397,86.57,224,0.900,bicubic,-37.457,-25.957,+54
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,59.030,40.970,72.837,27.163,93.59,320,1.000,bicubic,-38.260,-26.883,-75
volo_d5_512.sail_in1k,58.927,41.073,73.207,26.793,296.09,512,1.150,bicubic,-38.373,-26.553,-77
convformer_s36.sail_in22k_ft_in1k,58.917,41.083,72.940,27.060,40.01,224,1.000,bicubic,-37.583,-26.520,+53
convnext_small.in12k_ft_in1k_384,58.807,41.193,72.797,27.203,50.22,384,1.000,bicubic,-38.183,-26.853,-30
volo_d5_448.sail_in1k,58.800,41.200,73.063,26.937,295.91,448,1.150,bicubic,-38.440,-26.607,-70
vit_large_r50_s32_224.augreg_in21k_ft_in1k,58.663,41.337,71.737,28.263,328.99,224,0.900,bicubic,-37.517,-27.773,+114
vit_base_patch32_clip_384.openai_ft_in12k_in1k,58.580,41.420,73.153,26.847,88.30,384,0.950,bicubic,-37.840,-26.347,+65
maxvit_large_tf_384.in1k,58.427,41.573,71.177,28.823,212.03,384,1.000,bicubic,-38.503,-28.393,-25
deit3_large_patch16_384.fb_in1k,58.360,41.640,72.957,27.043,304.76,384,1.000,bicubic,-38.490,-26.663,-15
eva02_small_patch14_336.mim_in22k_ft_in1k,58.357,41.643,72.880,27.120,22.13,336,1.000,bicubic,-38.333,-26.730,+13
deit3_huge_patch14_224.fb_in1k,58.137,41.863,72.143,27.857,632.13,224,0.900,bicubic,-38.443,-27.377,+26
convnextv2_large.fcmae_ft_in1k,58.103,41.897,72.627,27.373,197.96,288,1.000,bicubic,-38.727,-27.133,-16
tf_efficientnet_b8.ap_in1k,57.837,42.163,72.957,27.043,87.41,672,0.954,bicubic,-38.713,-26.603,+33
convnext_small.fb_in22k_ft_in1k,57.743,42.257,72.773,27.227,50.22,288,1.000,bicubic,-39.067,-26.857,-12
seresnextaa101d_32x8d.sw_in12k_ft_in1k,57.643,42.357,71.357,28.643,93.59,288,1.000,bicubic,-39.527,-28.423,-68
mvitv2_large.fb_in1k,57.497,42.503,70.750,29.250,217.99,224,0.900,bicubic,-38.903,-28.790,+61
cait_m48_448.fb_dist_in1k,57.477,42.523,71.857,28.143,356.46,448,1.000,bicubic,-39.403,-27.813,-28
cait_m36_384.fb_dist_in1k,57.473,42.527,72.310,27.690,271.22,384,1.000,bicubic,-39.367,-27.350,-23
tf_efficientnet_b3.ns_jft_in1k,57.413,42.587,72.377,27.623,12.23,300,0.904,bicubic,-38.687,-27.103,+115
volo_d4_448.sail_in1k,57.283,42.717,71.540,28.460,193.41,448,1.150,bicubic,-39.787,-28.090,-60
maxvit_small_tf_512.in1k,57.077,42.923,70.960,29.040,69.13,512,1.000,bicubic,-40.113,-28.740,-77
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,57.070,42.930,71.267,28.733,88.22,224,0.900,bicubic,-38.160,-27.973,+282
vit_base_patch16_224.augreg_in21k_ft_in1k,56.840,43.160,70.633,29.367,86.57,224,0.900,bicubic,-39.460,-28.897,+74
deit3_medium_patch16_224.fb_in22k_ft_in1k,56.653,43.347,69.743,30.257,38.85,224,1.000,bicubic,-39.487,-29.747,+102
volo_d5_224.sail_in1k,56.503,43.497,70.660,29.340,295.46,224,0.960,bicubic,-40.377,-28.960,-38
deit3_large_patch16_224.fb_in1k,56.450,43.550,70.470,29.530,304.37,224,0.900,bicubic,-39.740,-29.030,+93
xcit_large_24_p8_384.fb_dist_in1k,56.360,43.640,71.307,28.693,188.93,384,1.000,bicubic,-40.400,-28.253,-15
flexivit_large.1200ep_in1k,56.290,43.710,71.557,28.443,304.36,240,0.950,bicubic,-40.490,-28.113,-20
convnextv2_tiny.fcmae_ft_in22k_in1k_384,56.100,43.900,71.900,28.100,28.64,384,1.000,bicubic,-40.520,-27.730,+2
flexivit_large.600ep_in1k,56.077,43.923,71.167,28.833,304.36,240,0.950,bicubic,-40.653,-28.393,-14
xcit_large_24_p8_224.fb_dist_in1k,56.020,43.980,70.670,29.330,188.93,224,1.000,bicubic,-40.610,-28.790,-2
caformer_s18.sail_in22k_ft_in1k_384,56.007,43.993,71.383,28.617,26.34,384,1.000,bicubic,-40.523,-28.197,+16
vit_base_patch32_clip_224.openai_ft_in1k,55.913,44.087,72.157,27.843,88.22,224,0.900,bicubic,-38.527,-26.953,+440
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,55.783,44.217,70.993,29.007,39.03,384,0.950,bicubic,-40.707,-28.627,+24
convnext_small.in12k_ft_in1k,55.700,44.300,70.727,29.273,50.22,288,1.000,bicubic,-40.900,-28.893,0
flexivit_large.300ep_in1k,55.700,44.300,70.703,29.297,304.36,240,0.950,bicubic,-41.000,-28.877,-16
caformer_b36.sail_in1k_384,55.193,44.807,68.070,31.930,98.75,384,1.000,bicubic,-41.967,-31.540,-87
convformer_s18.sail_in22k_ft_in1k_384,55.123,44.877,70.127,29.873,26.77,384,1.000,bicubic,-41.667,-29.583,-35
caformer_m36.sail_in1k_384,55.083,44.917,68.463,31.537,56.20,384,1.000,bicubic,-41.947,-31.247,-73
swin_small_patch4_window7_224.ms_in22k_ft_in1k,54.977,45.023,71.023,28.977,49.61,224,0.900,bicubic,-41.083,-28.457,+106
xcit_large_24_p16_384.fb_dist_in1k,54.910,45.090,69.853,30.147,189.10,384,1.000,bicubic,-42.030,-29.657,-59
volo_d4_224.sail_in1k,54.757,45.243,68.847,31.153,192.96,224,0.960,bicubic,-42.023,-30.763,-36
maxvit_tiny_tf_512.in1k,54.747,45.253,68.940,31.060,31.05,512,1.000,bicubic,-42.223,-30.730,-66
dm_nfnet_f5.dm_in1k,54.610,45.390,68.670,31.330,377.21,544,0.954,bicubic,-42.420,-31.010,-76
caformer_s36.sail_in1k_384,54.573,45.427,68.740,31.260,39.30,384,1.000,bicubic,-42.307,-30.930,-59
deit3_small_patch16_384.fb_in22k_ft_in1k,54.480,45.520,68.313,31.687,22.21,384,1.000,bicubic,-42.190,-31.327,-20
efficientnet_b5.sw_in12k_ft_in1k,54.417,45.583,69.877,30.123,30.39,448,1.000,bicubic,-42.353,-29.653,-37
vit_base_r50_s16_384.orig_in21k_ft_in1k,54.407,45.593,69.557,30.443,98.95,384,1.000,bicubic,-42.043,-30.053,+17
maxvit_small_tf_384.in1k,54.337,45.663,68.190,31.810,69.02,384,1.000,bicubic,-42.413,-31.350,-36
regnety_160.sw_in12k_ft_in1k,54.330,45.670,69.057,30.943,83.59,288,1.000,bicubic,-42.490,-30.563,-53
resnetv2_152x4_bit.goog_in21k_ft_in1k,54.320,45.680,70.173,29.827,936.53,480,1.000,bilinear,-42.560,-29.487,-63
xcit_large_24_p16_224.fb_dist_in1k,54.250,45.750,68.967,31.033,189.10,224,1.000,bicubic,-42.070,-30.533,+40
regnety_160.lion_in12k_ft_in1k,54.203,45.797,68.990,31.010,83.59,288,1.000,bicubic,-42.607,-30.520,-54
vit_small_r26_s32_384.augreg_in21k_ft_in1k,54.187,45.813,68.767,31.233,36.47,384,1.000,bicubic,-41.863,-30.783,+94
caformer_s18.sail_in22k_ft_in1k,54.110,45.890,69.713,30.287,26.34,224,1.000,bicubic,-41.900,-29.837,+100
volo_d3_448.sail_in1k,53.977,46.023,68.037,31.963,86.63,448,1.000,bicubic,-43.053,-31.633,-90
caformer_b36.sail_in1k,53.977,46.023,66.683,33.317,98.75,224,1.000,bicubic,-42.523,-32.947,+1
tf_efficientnet_b5.ap_in1k,53.880,46.120,69.170,30.830,30.39,456,0.934,bicubic,-42.210,-30.370,+78
dm_nfnet_f6.dm_in1k,53.843,46.157,68.413,31.587,438.36,576,0.956,bicubic,-43.127,-31.347,-83
xcit_medium_24_p8_224.fb_dist_in1k,53.650,46.350,68.403,31.597,84.32,224,1.000,bicubic,-42.880,-31.107,-10
tf_efficientnet_b2.ns_jft_in1k,53.600,46.400,70.270,29.730,9.11,260,0.890,bicubic,-41.920,-29.070,+188
cait_s36_384.fb_dist_in1k,53.560,46.440,68.003,31.997,68.37,384,1.000,bicubic,-43.070,-31.607,-34
tf_efficientnet_b6.ap_in1k,53.557,46.443,68.557,31.443,43.04,528,0.942,bicubic,-42.813,-30.993,+16
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,53.543,46.457,69.087,30.913,38.86,256,0.950,bicubic,-42.457,-30.333,+93
dm_nfnet_f3.dm_in1k,53.523,46.477,67.743,32.257,254.92,416,0.940,bicubic,-43.097,-31.837,-35
deit3_base_patch16_384.fb_in1k,53.483,46.517,67.623,32.377,86.88,384,1.000,bicubic,-42.747,-31.817,+46
deit3_base_patch16_224.fb_in1k,53.473,46.527,67.597,32.403,86.59,224,0.900,bicubic,-42.297,-31.673,+137
convformer_s18.sail_in22k_ft_in1k,53.437,46.563,68.680,31.320,26.77,224,1.000,bicubic,-42.663,-30.810,+65
tf_efficientnet_b8.ra_in1k,53.420,46.580,69.093,30.907,87.41,672,0.954,bicubic,-43.280,-30.557,-47
convnextv2_base.fcmae_ft_in1k,53.417,46.583,67.750,32.250,88.72,288,1.000,bicubic,-43.063,-31.800,-9
xcit_medium_24_p8_384.fb_dist_in1k,53.397,46.603,68.130,31.870,84.32,384,1.000,bicubic,-43.373,-31.470,-62
vit_base_patch32_384.augreg_in21k_ft_in1k,53.303,46.697,68.057,31.943,88.30,384,1.000,bicubic,-42.597,-31.283,+103
tf_efficientnet_b7.ap_in1k,53.260,46.740,68.877,31.123,66.35,600,0.949,bicubic,-43.090,-30.643,+9
convnext_large.fb_in1k,53.247,46.753,67.887,32.113,197.77,288,1.000,bicubic,-43.153,-31.563,+2
xcit_medium_24_p16_384.fb_dist_in1k,53.237,46.763,68.043,31.957,84.40,384,1.000,bicubic,-43.453,-31.557,-52
maxvit_base_tf_224.in1k,53.237,46.763,66.137,33.863,119.47,224,0.950,bicubic,-43.103,-33.443,+13
hrnet_w18_ssld.paddle_in1k,53.233,46.767,68.183,31.817,21.30,288,1.000,bilinear,-42.477,-31.107,+141
tf_efficientnetv2_l.in1k,53.163,46.837,67.833,32.167,118.52,480,1.000,bicubic,-43.577,-31.717,-63
tf_efficientnetv2_s.in21k_ft_in1k,53.127,46.873,68.997,31.003,21.46,384,1.000,bicubic,-43.343,-30.573,-17
tf_efficientnet_b4.ap_in1k,53.087,46.913,68.230,31.770,19.34,380,0.922,bicubic,-42.403,-31.160,+173
convnext_tiny.in12k_ft_in1k_384,53.063,46.937,68.503,31.497,28.59,384,1.000,bicubic,-43.497,-31.177,-37
regnetz_e8.ra3_in1k,53.003,46.997,67.147,32.853,57.70,320,1.000,bicubic,-43.597,-32.433,-49
maxvit_large_tf_224.in1k,53.003,46.997,65.340,34.660,211.79,224,0.950,bicubic,-43.327,-34.070,+7
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,52.873,47.127,66.460,33.540,41.72,224,0.950,bicubic,-43.257,-32.880,+44
volo_d3_224.sail_in1k,52.703,47.297,66.307,33.693,86.33,224,0.960,bicubic,-43.727,-33.233,-16
deit3_small_patch16_224.fb_in22k_ft_in1k,52.693,47.307,66.867,33.133,22.06,224,1.000,bicubic,-43.137,-32.473,+103
regnety_120.sw_in12k_ft_in1k,52.637,47.363,67.630,32.370,51.82,288,1.000,bicubic,-43.923,-32.020,-45
dm_nfnet_f4.dm_in1k,52.457,47.543,67.117,32.883,316.07,512,0.951,bicubic,-44.493,-32.593,-108
maxvit_tiny_tf_384.in1k,52.457,47.543,66.777,33.223,30.98,384,1.000,bicubic,-44.133,-32.783,-53
tf_efficientnet_b7.ra_in1k,52.407,47.593,68.227,31.773,66.35,600,0.949,bicubic,-44.163,-31.243,-51
xcit_small_24_p8_384.fb_dist_in1k,52.377,47.623,66.847,33.153,47.63,384,1.000,bicubic,-44.433,-32.813,-90
efficientnetv2_rw_m.agc_in1k,52.333,47.667,67.223,32.777,53.24,416,1.000,bicubic,-43.937,-32.407,+9
resnet18.fb_swsl_ig1b_ft_in1k,52.320,47.680,70.477,29.523,11.69,224,0.875,bilinear,-38.760,-27.723,+706
convformer_b36.sail_in1k_384,52.300,47.700,66.543,33.457,99.88,384,1.000,bicubic,-44.570,-33.107,-104
convformer_m36.sail_in1k_384,52.287,47.713,66.147,33.853,57.05,384,1.000,bicubic,-44.493,-33.583,-88
deit_base_distilled_patch16_384.fb_in1k,52.263,47.737,67.740,32.260,87.63,384,1.000,bicubic,-44.247,-31.850,-43
xcit_medium_24_p16_224.fb_dist_in1k,52.197,47.803,66.917,33.083,84.40,224,1.000,bicubic,-44.053,-32.493,+10
xcit_small_24_p8_224.fb_dist_in1k,52.183,47.817,66.767,33.233,47.63,224,1.000,bicubic,-44.367,-32.773,-53
convnext_tiny.fb_in22k_ft_in1k_384,52.180,47.820,66.923,33.077,28.59,384,1.000,bicubic,-43.990,-32.577,+23
convformer_s36.sail_in1k_384,51.990,48.010,66.200,33.800,40.01,384,1.000,bicubic,-44.710,-33.330,-79
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,51.937,48.063,68.663,31.337,236.34,384,1.000,bicubic,-44.253,-30.637,+17
convnextv2_tiny.fcmae_ft_in22k_in1k,51.903,48.097,67.800,32.200,28.64,288,1.000,bicubic,-44.437,-31.570,-16
resmlp_big_24_224.fb_in22k_ft_in1k,51.893,48.107,68.463,31.537,129.14,224,0.875,bicubic,-44.457,-31.127,-19
xcit_small_24_p16_384.fb_dist_in1k,51.880,48.120,66.367,33.633,47.67,384,1.000,bicubic,-44.460,-33.183,-19
cait_s24_384.fb_dist_in1k,51.787,48.213,66.307,33.693,47.06,384,1.000,bicubic,-44.783,-33.243,-67
resnetv2_152x2_bit.goog_in21k_ft_in1k,51.763,48.237,69.263,30.737,236.34,448,1.000,bilinear,-44.757,-30.307,-55
caformer_m36.sail_in1k,51.693,48.307,64.490,35.510,56.20,224,1.000,bicubic,-44.717,-35.070,-33
ecaresnet269d.ra2_in1k,51.673,48.327,66.040,33.960,102.09,352,1.000,bicubic,-44.777,-33.620,-41
regnety_2560.seer_ft_in1k,51.650,48.350,68.193,31.807,"1,282.60",384,1.000,bicubic,-44.880,-31.327,-60
rexnetr_300.sw_in12k_ft_in1k,51.630,48.370,68.033,31.967,34.81,288,1.000,bicubic,-44.460,-31.507,+25
caformer_s36.sail_in1k,51.593,48.407,64.910,35.090,39.30,224,1.000,bicubic,-44.487,-34.600,+27
coat_lite_medium_384.in1k,51.570,48.430,65.740,34.260,44.57,384,1.000,bicubic,-45.000,-33.780,-71
mvitv2_base.fb_in1k,51.550,48.450,65.633,34.367,51.47,224,0.900,bicubic,-44.430,-33.957,+49
vit_base_patch16_224_miil.in21k_ft_in1k,51.543,48.457,65.210,34.790,86.54,224,0.875,bilinear,-44.497,-34.140,+38
davit_small.msft_in1k,51.523,48.477,66.440,33.560,49.75,224,0.950,bicubic,-44.507,-32.960,+38
convnext_tiny.in12k_ft_in1k,51.440,48.560,67.063,32.937,28.59,288,1.000,bicubic,-44.800,-32.227,-8
maxvit_rmlp_small_rw_224.sw_in1k,51.430,48.570,65.190,34.810,64.90,224,0.900,bicubic,-44.530,-34.240,+52
tf_efficientnetv2_m.in1k,51.427,48.573,66.620,33.380,54.14,480,1.000,bicubic,-45.053,-32.900,-59
caformer_s18.sail_in1k_384,51.353,48.647,65.657,34.343,26.34,384,1.000,bicubic,-45.057,-33.873,-46
edgenext_base.in21k_ft_in1k,51.283,48.717,65.657,34.343,18.51,320,1.000,bicubic,-44.917,-33.803,-4
davit_base.msft_in1k,51.267,48.733,65.223,34.777,87.95,224,0.950,bicubic,-44.973,-34.417,-12
convformer_b36.sail_in1k,51.203,48.797,64.280,35.720,99.88,224,1.000,bicubic,-45.037,-35.130,-12
maxvit_small_tf_224.in1k,51.193,48.807,65.263,34.737,68.93,224,0.950,bicubic,-45.007,-34.267,-6
xcit_small_12_p8_384.fb_dist_in1k,51.107,48.893,65.840,34.160,26.21,384,1.000,bicubic,-45.363,-33.650,-61
convnext_base.fb_in1k,51.057,48.943,65.880,34.120,88.59,288,1.000,bicubic,-45.253,-33.630,-30
convformer_m36.sail_in1k,51.020,48.980,63.650,36.350,57.05,224,1.000,bicubic,-45.060,-35.740,+14
volo_d2_384.sail_in1k,50.897,49.103,65.620,34.380,58.87,384,1.000,bicubic,-45.813,-33.980,-109
tf_efficientnet_b1.ns_jft_in1k,50.887,49.113,67.907,32.093,7.79,240,0.882,bicubic,-43.973,-31.343,+262
vit_base_patch16_384.orig_in21k_ft_in1k,50.880,49.120,65.273,34.727,86.86,384,1.000,bicubic,-45.320,-34.197,-14
convformer_s36.sail_in1k,50.863,49.137,64.083,35.917,40.01,224,1.000,bicubic,-45.247,-35.377,+2
xcit_small_24_p16_224.fb_dist_in1k,50.743,49.257,65.047,34.953,47.67,224,1.000,bicubic,-45.047,-34.523,+69
flexivit_base.1200ep_in1k,50.693,49.307,65.140,34.860,86.59,240,0.950,bicubic,-45.417,-34.380,-1
convformer_s18.sail_in1k_384,50.687,49.313,65.637,34.363,26.77,384,1.000,bicubic,-45.563,-33.943,-28
coatnet_rmlp_2_rw_224.sw_in1k,50.603,49.397,63.363,36.637,73.88,224,0.950,bicubic,-45.607,-36.117,-20
xcit_small_12_p16_384.fb_dist_in1k,50.527,49.473,65.300,34.700,26.25,384,1.000,bicubic,-45.813,-34.190,-48
efficientnet_b4.ra2_in1k,50.500,49.500,65.727,34.273,19.34,384,1.000,bicubic,-45.030,-33.673,+111
volo_d1_384.sail_in1k,50.477,49.523,64.913,35.087,26.78,384,1.000,bicubic,-46.003,-34.697,-77
xcit_small_12_p8_224.fb_dist_in1k,50.437,49.563,65.417,34.583,26.21,224,1.000,bicubic,-45.523,-33.953,+27
resnetv2_101x3_bit.goog_in21k_ft_in1k,50.393,49.607,67.783,32.217,387.93,448,1.000,bilinear,-45.857,-31.687,-35
flexivit_base.600ep_in1k,50.353,49.647,64.620,35.380,86.59,240,0.950,bicubic,-45.607,-34.800,+26
regnetz_040_h.ra3_in1k,50.317,49.683,65.623,34.377,28.94,320,1.000,bicubic,-46.003,-33.917,-50
mvitv2_small.fb_in1k,50.260,49.740,64.893,35.107,34.87,224,0.900,bicubic,-45.630,-34.467,+37
cait_s24_224.fb_dist_in1k,50.250,49.750,65.020,34.980,46.92,224,1.000,bicubic,-45.410,-34.370,+82
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,50.230,49.770,66.033,33.967,194.03,224,0.875,bilinear,-45.170,-33.127,+127
pit_b_distilled_224.in1k,50.227,49.773,65.000,35.000,74.79,224,0.900,bicubic,-45.593,-34.290,+51
eca_nfnet_l2.ra3_in1k,50.207,49.793,65.437,34.563,56.72,384,1.000,bicubic,-46.243,-34.313,-80
vit_small_patch16_384.augreg_in21k_ft_in1k,50.183,49.817,65.797,34.203,22.20,384,1.000,bicubic,-45.797,-33.533,+13
resnest269e.in1k,50.183,49.817,64.683,35.317,110.93,416,0.928,bicubic,-45.927,-34.627,-18
tresnet_v2_l.miil_in21k_ft_in1k,50.163,49.837,65.120,34.880,46.17,224,0.875,bilinear,-45.657,-34.200,+46
pvt_v2_b5.in1k,50.143,49.857,65.023,34.977,81.96,224,0.900,bicubic,-45.797,-34.367,+19
deit3_medium_patch16_224.fb_in1k,50.143,49.857,64.697,35.303,38.85,224,0.900,bicubic,-45.237,-34.653,+127
deit_base_distilled_patch16_224.fb_in1k,50.077,49.923,66.233,33.767,87.34,224,0.900,bicubic,-45.673,-33.197,+56
tf_efficientnet_b3.ap_in1k,50.047,49.953,65.210,34.790,12.23,300,0.904,bicubic,-44.923,-33.900,+215
pvt_v2_b4.in1k,50.027,49.973,65.127,34.873,62.56,224,0.900,bicubic,-45.893,-34.233,+19
flexivit_base.300ep_in1k,50.007,49.993,64.103,35.897,86.59,240,0.950,bicubic,-45.953,-35.277,+13
coat_lite_medium.in1k,49.977,50.023,64.857,35.143,44.57,224,0.900,bicubic,-46.023,-34.643,+4
resnest200e.in1k,49.870,50.130,64.727,35.273,70.20,320,0.909,bicubic,-46.200,-34.753,-16
efficientformer_l7.snap_dist_in1k,49.837,50.163,66.033,33.967,82.23,224,0.950,bicubic,-45.763,-33.357,+76
volo_d2_224.sail_in1k,49.817,50.183,64.583,35.417,58.68,224,0.960,bicubic,-46.603,-34.877,-88
xception65.ra3_in1k,49.777,50.223,63.527,36.473,39.92,299,0.940,bicubic,-45.913,-35.793,+63
seresnextaa101d_32x8d.ah_in1k,49.747,50.253,64.443,35.557,93.59,288,1.000,bicubic,-46.693,-35.067,-93
swinv2_base_window16_256.ms_in1k,49.667,50.333,63.800,36.200,87.92,256,0.900,bicubic,-46.503,-35.590,-39
pvt_v2_b3.in1k,49.613,50.387,64.790,35.210,45.24,224,0.900,bicubic,-45.857,-34.520,+94
convnextv2_nano.fcmae_ft_in22k_in1k_384,49.603,50.397,65.657,34.343,15.62,384,1.000,bicubic,-46.187,-33.633,+37
cait_xs24_384.fb_dist_in1k,49.537,50.463,64.900,35.100,26.67,384,1.000,bicubic,-46.463,-34.530,-6
maxvit_rmlp_tiny_rw_256.sw_in1k,49.530,50.470,63.823,36.177,29.15,256,0.950,bicubic,-46.510,-35.717,-16
tf_efficientnet_b5.ra_in1k,49.527,50.473,65.657,34.343,30.39,456,0.934,bicubic,-46.443,-33.773,-4
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,49.477,50.523,65.627,34.373,236.34,224,0.875,bicubic,-46.273,-33.803,+39
resnet200d.ra2_in1k,49.463,50.537,64.330,35.670,64.69,320,1.000,bicubic,-46.647,-35.080,-39
efficientformerv2_l.snap_dist_in1k,49.460,50.540,64.920,35.080,26.32,224,0.950,bicubic,-46.300,-34.450,+35
xcit_small_12_p16_224.fb_dist_in1k,49.413,50.587,63.847,36.153,26.25,224,1.000,bicubic,-46.317,-35.453,+43
resnest101e.in1k,49.367,50.633,65.583,34.417,48.28,256,0.875,bilinear,-46.203,-33.787,+69
dm_nfnet_f2.dm_in1k,49.350,50.650,63.950,36.050,193.78,352,0.920,bicubic,-47.170,-35.640,-121
regnetz_040.ra3_in1k,49.297,50.703,64.060,35.940,27.12,320,1.000,bicubic,-46.883,-35.470,-53
vit_base_patch32_224.augreg_in21k_ft_in1k,49.267,50.733,64.340,35.660,88.22,224,0.900,bicubic,-45.133,-34.450,+307
resnet152d.ra2_in1k,49.260,50.740,64.413,35.587,60.21,320,1.000,bicubic,-46.610,-35.057,+10
seresnet152d.ra2_in1k,49.237,50.763,64.167,35.833,66.84,320,1.000,bicubic,-47.073,-35.243,-83
xcit_large_24_p8_224.fb_in1k,49.237,50.763,62.840,37.160,188.93,224,1.000,bicubic,-46.853,-36.300,-41
gcvit_base.in1k,49.153,50.847,63.950,36.050,90.32,224,0.875,bicubic,-46.927,-35.300,-40
maxxvit_rmlp_small_rw_256.sw_in1k,49.150,50.850,63.343,36.657,66.01,256,0.950,bicubic,-47.060,-35.937,-67
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,49.093,50.907,65.490,34.510,88.79,224,0.875,bilinear,-46.227,-33.830,+105
resmlp_big_24_224.fb_distilled_in1k,49.093,50.907,65.473,34.527,129.14,224,0.875,bicubic,-46.777,-33.967,+3
convnext_small.fb_in1k,49.067,50.933,64.830,35.170,50.22,288,1.000,bicubic,-46.903,-34.620,-19
resnetaa101d.sw_in12k_ft_in1k,49.013,50.987,64.237,35.763,44.57,288,1.000,bicubic,-47.347,-35.233,-103
resnext101_64x4d.tv_in1k,48.990,51.010,63.510,36.490,83.46,224,0.875,bilinear,-46.830,-35.700,+9
volo_d1_224.sail_in1k,48.977,51.023,63.187,36.813,26.63,224,0.960,bicubic,-47.053,-36.203,-32
repvgg_b3.rvgg_in1k,48.913,51.087,64.890,35.110,123.09,224,0.875,bilinear,-45.657,-33.760,+260
resnetrs420.tf_in1k,48.860,51.140,63.437,36.563,191.89,416,1.000,bicubic,-47.540,-36.093,-113
maxvit_tiny_tf_224.in1k,48.807,51.193,62.933,37.067,30.92,224,0.950,bicubic,-47.003,-36.327,+8
convformer_s18.sail_in1k,48.787,51.213,62.930,37.070,26.77,224,1.000,bicubic,-46.543,-36.370,+95
caformer_s18.sail_in1k,48.750,51.250,62.867,37.133,26.34,224,1.000,bicubic,-46.930,-36.423,+33
deit3_small_patch16_384.fb_in1k,48.670,51.330,62.797,37.203,22.21,384,1.000,bicubic,-46.930,-36.643,+43
seresnext101d_32x8d.ah_in1k,48.603,51.397,62.960,37.040,93.59,288,1.000,bicubic,-47.757,-36.480,-113
swinv2_small_window16_256.ms_in1k,48.593,51.407,62.747,37.253,49.73,256,0.900,bicubic,-47.477,-36.703,-51
regnetz_d32.ra3_in1k,48.583,51.417,65.167,34.833,27.58,320,0.950,bicubic,-47.287,-34.263,-10
efficientnetv2_rw_s.ra2_in1k,48.573,51.427,63.833,36.167,23.94,384,1.000,bicubic,-47.137,-35.507,+21
efficientnet_b3.ra2_in1k,48.560,51.440,64.243,35.757,12.23,320,1.000,bicubic,-46.590,-34.967,+125
edgenext_base.usi_in1k,48.457,51.543,64.317,35.683,18.51,320,1.000,bicubic,-47.333,-34.983,+1
focalnet_base_lrf.ms_in1k,48.443,51.557,63.120,36.880,88.75,224,0.900,bicubic,-47.387,-36.080,-6
focalnet_base_srf.ms_in1k,48.423,51.577,63.103,36.897,88.15,224,0.900,bicubic,-47.477,-36.207,-24
vit_small_r26_s32_224.augreg_in21k_ft_in1k,48.377,51.623,63.800,36.200,36.43,224,0.900,bicubic,-46.753,-35.420,+128
swinv2_base_window8_256.ms_in1k,48.333,51.667,63.610,36.390,87.92,256,0.900,bicubic,-47.727,-35.800,-55
repvgg_b3g4.rvgg_in1k,48.310,51.690,64.780,35.220,83.83,224,0.875,bilinear,-46.190,-34.240,+261
vit_large_patch32_384.orig_in21k_ft_in1k,48.240,51.760,61.827,38.173,306.63,384,1.000,bicubic,-47.000,-37.493,+98
convit_base.fb_in1k,48.210,51.790,63.000,37.000,86.54,224,0.875,bicubic,-46.890,-36.150,+131
swin_s3_base_224.ms_in1k,48.153,51.847,62.243,37.757,71.13,224,0.900,bicubic,-47.887,-37.107,-55
sequencer2d_l.in1k,48.107,51.893,62.357,37.643,54.30,224,0.875,bicubic,-47.763,-37.203,-25
resnext101_32x8d.tv2_in1k,48.093,51.907,62.723,37.277,88.79,224,0.965,bilinear,-47.207,-36.507,+81
resnetrs350.tf_in1k,48.057,51.943,62.667,37.333,163.96,384,1.000,bicubic,-48.193,-36.923,-103
tf_efficientnetv2_b3.in21k_ft_in1k,48.030,51.970,64.750,35.250,14.36,300,0.900,bicubic,-47.570,-34.530,+26
focalnet_small_lrf.ms_in1k,48.027,51.973,63.130,36.870,50.34,224,0.900,bicubic,-47.713,-36.030,-1
gcvit_small.in1k,48.023,51.977,62.713,37.287,51.09,224,0.875,bicubic,-47.887,-36.567,-38
regnetz_d8.ra3_in1k,48.010,51.990,64.423,35.577,23.37,320,1.000,bicubic,-48.000,-35.097,-57
regnety_1280.seer_ft_in1k,47.990,52.010,64.230,35.770,644.81,384,1.000,bicubic,-48.320,-35.330,-118
twins_svt_large.in1k,47.963,52.037,62.907,37.093,99.27,224,0.900,bicubic,-47.737,-36.463,+6
vit_relpos_base_patch16_224.sw_in1k,47.923,52.077,62.840,37.160,86.43,224,0.900,bicubic,-47.207,-36.240,+111
repvgg_b2g4.rvgg_in1k,47.810,52.190,64.397,35.603,61.76,224,0.875,bilinear,-46.020,-34.533,+358
mixer_b16_224.miil_in21k_ft_in1k,47.793,52.207,63.403,36.597,59.88,224,0.875,bilinear,-47.087,-35.677,+168
vit_relpos_base_patch16_clsgap_224.sw_in1k,47.760,52.240,62.410,37.590,86.43,224,0.900,bicubic,-47.490,-36.800,+80
tf_efficientnet_b5.aa_in1k,47.730,52.270,63.907,36.093,30.39,456,0.934,bicubic,-48.150,-35.443,-40
mvitv2_tiny.fb_in1k,47.667,52.333,62.830,37.170,24.17,224,0.900,bicubic,-47.733,-36.470,+52
eca_nfnet_l1.ra2_in1k,47.653,52.347,62.767,37.233,41.41,320,1.000,bicubic,-48.277,-36.723,-50
vit_relpos_medium_patch16_cls_224.sw_in1k,47.653,52.347,61.783,38.217,38.76,224,0.900,bicubic,-47.647,-37.317,+67
seresnext101_32x8d.ah_in1k,47.653,52.347,61.477,38.523,93.57,288,1.000,bicubic,-48.487,-37.883,-96
ecaresnet101d.miil_in1k,47.630,52.370,63.540,36.460,44.57,288,0.950,bicubic,-48.000,-35.750,+6
regnetz_d8_evos.ch_in1k,47.623,52.377,63.807,36.193,23.46,320,1.000,bicubic,-48.517,-35.673,-101
resnetv2_50x3_bit.goog_in21k_ft_in1k,47.593,52.407,65.603,34.397,217.32,448,1.000,bilinear,-48.677,-33.957,-127
focalnet_small_srf.ms_in1k,47.547,52.453,62.510,37.490,49.89,224,0.900,bicubic,-48.083,-36.930,+4
pit_s_distilled_224.in1k,47.540,52.460,63.170,36.830,24.04,224,0.900,bicubic,-47.150,-35.980,+192
resnest50d_4s2x40d.in1k,47.483,52.517,63.803,36.197,30.42,224,0.875,bicubic,-47.217,-35.427,+187
dm_nfnet_f1.dm_in1k,47.457,52.543,62.083,37.917,132.63,320,0.910,bicubic,-48.843,-37.447,-133
efficientnet_b3_pruned.in1k,47.450,52.550,62.803,37.197,9.86,300,0.904,bicubic,-47.140,-36.127,+210
davit_tiny.msft_in1k,47.410,52.590,63.360,36.640,28.36,224,0.950,bicubic,-47.670,-35.900,+109
crossvit_18_dagger_408.in1k,47.393,52.607,60.927,39.073,44.61,408,1.000,bicubic,-48.757,-38.543,-110
poolformerv2_m48.sail_in1k,47.383,52.617,63.960,36.040,73.35,224,1.000,bicubic,-47.797,-35.220,+84
coatnet_rmlp_1_rw_224.sw_in1k,47.370,52.630,61.437,38.563,41.69,224,0.950,bicubic,-48.130,-37.813,+15
vit_base_patch16_224.orig_in21k_ft_in1k,47.347,52.653,61.620,38.380,86.57,224,0.900,bicubic,-47.853,-37.610,+77
xcit_small_24_p8_224.fb_in1k,47.290,52.710,60.993,39.007,47.63,224,1.000,bicubic,-48.610,-38.187,-60
efficientformer_l3.snap_dist_in1k,47.240,52.760,63.417,36.583,31.41,224,0.950,bicubic,-47.970,-35.893,+69
tresnet_m.miil_in21k_ft_in1k,47.227,52.773,62.000,38.000,31.39,224,0.875,bilinear,-48.153,-37.150,+38
wide_resnet101_2.tv2_in1k,47.207,52.793,61.877,38.123,126.89,224,0.965,bilinear,-48.033,-37.323,+62
tf_efficientnet_b6.aa_in1k,47.203,52.797,63.110,36.890,43.04,528,0.942,bicubic,-49.097,-36.450,-143
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,47.187,52.813,63.387,36.613,44.18,224,0.875,bilinear,-47.953,-35.833,+82
swin_base_patch4_window12_384.ms_in1k,47.183,52.817,62.027,37.973,87.90,384,1.000,bicubic,-49.197,-37.393,-166
resnetrs270.tf_in1k,47.127,52.873,62.000,38.000,129.86,352,1.000,bicubic,-48.933,-37.480,-100
tf_efficientnet_b4.aa_in1k,47.073,52.927,62.860,37.140,19.34,380,0.922,bicubic,-48.517,-36.470,-9
regnety_320.tv2_in1k,47.070,52.930,62.047,37.953,145.05,224,0.965,bicubic,-48.490,-37.343,-5
vit_base_patch16_rpn_224.sw_in1k,47.067,52.933,62.393,37.607,86.54,224,0.900,bicubic,-47.763,-36.697,+147
rexnetr_200.sw_in12k_ft_in1k,47.053,52.947,63.973,36.027,16.52,288,1.000,bicubic,-48.257,-35.507,+39
convnextv2_tiny.fcmae_ft_in1k,47.043,52.957,62.503,37.497,28.64,288,1.000,bicubic,-48.787,-36.817,-60
swinv2_small_window8_256.ms_in1k,47.020,52.980,62.300,37.700,49.73,256,0.900,bicubic,-48.710,-37.060,-38
xcit_small_12_p8_224.fb_in1k,46.973,53.027,60.527,39.473,26.21,224,1.000,bicubic,-48.447,-38.663,+15
xcit_large_24_p16_224.fb_in1k,46.940,53.060,60.663,39.337,189.10,224,1.000,bicubic,-48.020,-38.167,+117
coat_small.in1k,46.937,53.063,61.307,38.693,21.69,224,0.900,bicubic,-48.253,-37.973,+61
xception65p.ra3_in1k,46.923,53.077,61.087,38.913,39.82,299,0.940,bicubic,-48.737,-38.183,-28
maxvit_tiny_rw_224.sw_in1k,46.903,53.097,60.890,39.110,29.06,224,0.950,bicubic,-48.837,-38.550,-45
resnet101d.ra2_in1k,46.890,53.110,62.340,37.660,44.57,320,1.000,bicubic,-48.850,-36.870,-48
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,46.837,53.163,64.117,35.883,28.29,224,0.900,bicubic,-47.963,-35.173,+143
pvt_v2_b2_li.in1k,46.813,53.187,62.490,37.510,22.55,224,0.900,bicubic,-48.407,-36.770,+46
resnet152.tv2_in1k,46.807,53.193,61.070,38.930,60.19,224,0.965,bilinear,-48.233,-38.110,+94
regnety_640.seer_ft_in1k,46.710,53.290,63.233,36.767,281.38,384,1.000,bicubic,-49.350,-36.267,-117
seresnext101_64x4d.gluon_in1k,46.650,53.350,61.283,38.717,88.23,224,0.875,bicubic,-48.000,-37.687,+170
convnextv2_nano.fcmae_ft_in22k_in1k,46.607,53.393,62.957,37.043,15.62,288,1.000,bicubic,-48.813,-36.353,+3
twins_pcpvt_large.in1k,46.607,53.393,62.270,37.730,60.99,224,0.900,bicubic,-49.113,-37.220,-49
convnext_tiny.fb_in1k,46.587,53.413,63.187,36.813,28.59,288,1.000,bicubic,-48.623,-36.123,+42
swin_base_patch4_window7_224.ms_in1k,46.543,53.457,61.580,38.420,87.77,224,0.900,bicubic,-49.357,-37.860,-89
resnet152.a1h_in1k,46.540,53.460,60.403,39.597,60.19,288,1.000,bicubic,-49.210,-38.877,-61
efficientformerv2_s2.snap_dist_in1k,46.527,53.473,61.720,38.280,12.71,224,0.950,bicubic,-48.593,-37.480,+66
regnetv_064.ra3_in1k,46.480,53.520,62.257,37.743,30.58,288,1.000,bicubic,-49.300,-37.163,-66
crossvit_15_dagger_408.in1k,46.467,53.533,60.483,39.517,28.50,408,1.000,bicubic,-49.363,-38.907,-79
xcit_medium_24_p8_224.fb_in1k,46.467,53.533,59.653,40.347,84.32,224,1.000,bicubic,-49.393,-39.427,-83
resnetrs200.tf_in1k,46.437,53.563,61.067,38.933,93.21,320,1.000,bicubic,-49.913,-38.483,-189
swin_s3_small_224.ms_in1k,46.400,53.600,60.893,39.107,49.74,224,0.900,bicubic,-49.430,-38.287,-81
coatnet_1_rw_224.sw_in1k,46.393,53.607,60.063,39.937,41.72,224,0.950,bicubic,-49.217,-39.157,-41
gcvit_tiny.in1k,46.363,53.637,61.633,38.367,28.22,224,0.875,bicubic,-49.297,-37.697,-49
rexnet_300.nav_in1k,46.360,53.640,62.697,37.303,34.71,224,0.875,bicubic,-49.180,-36.593,-32
fbnetv3_g.ra2_in1k,46.337,53.663,62.390,37.610,16.62,288,0.950,bilinear,-48.783,-36.730,+55
sequencer2d_m.in1k,46.297,53.703,60.920,39.080,38.31,224,0.875,bicubic,-49.283,-38.330,-40
tresnet_xl.miil_in1k,46.290,53.710,61.937,38.063,78.44,224,0.875,bilinear,-48.790,-37.353,+62
xcit_tiny_24_p8_224.fb_dist_in1k,46.283,53.717,60.590,39.410,12.11,224,1.000,bicubic,-49.167,-38.770,-17
xcit_tiny_24_p8_384.fb_dist_in1k,46.260,53.740,60.730,39.270,12.11,384,1.000,bicubic,-49.970,-38.670,-170
deit_small_distilled_patch16_224.fb_in1k,46.173,53.827,62.423,37.577,22.44,224,0.900,bicubic,-48.427,-36.607,+156
gernet_m.idstcv_in1k,46.170,53.830,62.687,37.313,21.14,224,0.875,bilinear,-48.370,-36.233,+170
regnety_160.deit_in1k,46.167,53.833,61.823,38.177,83.59,288,1.000,bicubic,-49.703,-37.617,-103
crossvit_base_240.in1k,46.120,53.880,60.207,39.793,105.03,240,0.875,bicubic,-48.950,-38.773,+60
resnest50d_1s4x24d.in1k,46.100,53.900,62.373,37.627,25.68,224,0.875,bicubic,-48.280,-36.697,+201
swinv2_cr_small_ns_224.sw_in1k,46.100,53.900,60.800,39.200,49.70,224,0.900,bicubic,-49.610,-38.600,-67
poolformerv2_m36.sail_in1k,46.043,53.957,62.247,37.753,56.08,224,1.000,bicubic,-49.007,-37.033,+63
tf_efficientnet_b0.ns_jft_in1k,46.037,53.963,63.273,36.727,5.29,224,0.875,bicubic,-47.713,-35.657,+294
poolformerv2_s36.sail_in1k,46.030,53.970,62.247,37.753,30.79,224,1.000,bicubic,-48.670,-36.833,+126
resnet51q.ra2_in1k,46.027,53.973,60.910,39.090,35.70,288,1.000,bilinear,-49.173,-38.370,+21
nest_base_jx.goog_in1k,46.027,53.973,60.100,39.900,67.72,224,0.875,bicubic,-49.513,-39.120,-46
vit_small_patch16_224.augreg_in21k_ft_in1k,46.020,53.980,61.837,38.163,22.05,224,0.900,bicubic,-48.870,-37.233,+90
vit_relpos_medium_patch16_224.sw_in1k,45.980,54.020,61.033,38.967,38.75,224,0.900,bicubic,-49.210,-38.187,+22
regnety_080.ra3_in1k,45.980,54.020,60.850,39.150,39.18,288,1.000,bicubic,-49.890,-38.580,-111
deit3_small_patch16_224.fb_in1k,45.940,54.060,58.873,41.127,22.06,224,0.900,bicubic,-48.750,-40.307,+127
resnest50d.in1k,45.933,54.067,62.620,37.380,27.48,224,0.875,bilinear,-48.667,-36.530,+143
convnext_nano.in12k_ft_in1k,45.900,54.100,62.693,37.307,15.59,288,1.000,bicubic,-49.450,-36.757,-18
crossvit_18_240.in1k,45.900,54.100,60.347,39.653,43.27,240,0.875,bicubic,-49.170,-38.683,+44
levit_384.fb_dist_in1k,45.880,54.120,61.683,38.317,39.13,224,0.900,bicubic,-49.330,-37.477,+10
regnety_032.ra_in1k,45.880,54.120,61.543,38.457,19.44,288,1.000,bicubic,-49.580,-37.847,-41
levit_conv_384.fb_dist_in1k,45.870,54.130,61.690,38.310,39.13,224,0.900,bicubic,-49.340,-37.590,+9
twins_svt_base.in1k,45.863,54.137,60.973,39.027,56.07,224,0.900,bicubic,-49.697,-38.257,-60
twins_pcpvt_base.in1k,45.853,54.147,61.327,38.673,43.83,224,0.900,bicubic,-49.617,-37.793,-49
convnext_tiny_hnf.a2h_in1k,45.853,54.147,60.180,39.820,28.59,288,1.000,bicubic,-49.397,-39.020,-4
crossvit_18_dagger_240.in1k,45.850,54.150,59.917,40.083,44.27,240,0.875,bicubic,-49.330,-39.243,+14
regnetz_c16.ra3_in1k,45.790,54.210,62.737,37.263,13.46,320,1.000,bicubic,-49.590,-36.683,-31
vit_relpos_medium_patch16_rpn_224.sw_in1k,45.743,54.257,60.983,39.017,38.73,224,0.900,bicubic,-49.317,-38.217,+39
vit_srelpos_medium_patch16_224.sw_in1k,45.723,54.277,61.073,38.927,38.74,224,0.900,bicubic,-49.217,-37.967,+63
crossvit_15_dagger_240.in1k,45.703,54.297,60.080,39.920,28.21,240,0.875,bicubic,-49.287,-39.110,+54
regnetx_320.tv2_in1k,45.673,54.327,60.233,39.767,107.81,224,0.965,bicubic,-49.607,-39.057,-16
convmixer_1536_20.in1k,45.663,54.337,61.743,38.257,51.63,224,0.960,bicubic,-49.307,-37.327,+55
gc_efficientnetv2_rw_t.agc_in1k,45.657,54.343,60.193,39.807,13.68,288,1.000,bicubic,-49.633,-39.187,-20
flexivit_small.1200ep_in1k,45.623,54.377,59.900,40.100,22.06,240,0.950,bicubic,-49.557,-39.220,+4
dm_nfnet_f0.dm_in1k,45.617,54.383,61.277,38.723,71.49,256,0.900,bicubic,-50.073,-38.073,-93
efficientnetv2_rw_t.ra2_in1k,45.607,54.393,60.177,39.823,13.65,288,1.000,bicubic,-49.453,-39.043,+30
seresnext101_32x4d.gluon_in1k,45.600,54.400,61.160,38.840,48.96,224,0.875,bicubic,-48.830,-37.840,+160
xcit_tiny_24_p16_384.fb_dist_in1k,45.580,54.420,60.507,39.493,12.12,384,1.000,bicubic,-49.910,-38.853,-64
xcit_medium_24_p16_224.fb_in1k,45.537,54.463,59.007,40.993,84.40,224,1.000,bicubic,-49.593,-39.933,+12
xcit_small_24_p16_224.fb_in1k,45.513,54.487,58.880,41.120,47.67,224,1.000,bicubic,-49.547,-40.190,+29
resnext101_64x4d.c1_in1k,45.450,54.550,59.033,40.967,83.46,288,1.000,bicubic,-50.080,-40.257,-73
regnety_320.seer_ft_in1k,45.427,54.573,62.227,37.773,145.05,384,1.000,bicubic,-50.373,-37.163,-124
resnet152d.gluon_in1k,45.427,54.573,60.080,39.920,60.21,224,0.875,bicubic,-49.003,-39.010,+155
nfnet_l0.ra2_in1k,45.417,54.583,62.083,37.917,35.07,288,1.000,bicubic,-49.963,-37.127,-49
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,45.413,54.587,62.023,37.977,25.03,224,0.875,bilinear,-49.297,-37.217,+90
xcit_small_12_p16_224.fb_in1k,45.413,54.587,59.403,40.597,26.25,224,1.000,bicubic,-49.407,-39.657,+70
resnetv2_50x1_bit.goog_distilled_in1k,45.410,54.590,62.300,37.700,25.55,224,0.875,bicubic,-50.000,-37.020,-59
cs3se_edgenet_x.c2ns_in1k,45.393,54.607,60.447,39.553,50.72,320,1.000,bicubic,-50.607,-38.903,-170
resnet101.tv2_in1k,45.370,54.630,60.060,39.940,44.55,224,0.965,bilinear,-49.470,-38.970,+64
nest_small_jx.goog_in1k,45.350,54.650,59.040,40.960,38.35,224,0.875,bicubic,-50.190,-40.280,-84
tf_efficientnet_b7.aa_in1k,45.303,54.697,61.727,38.273,66.35,600,0.949,bicubic,-50.767,-37.613,-189
pvt_v2_b2.in1k,45.280,54.720,60.613,39.387,25.36,224,0.900,bicubic,-49.730,-38.427,+27
resnet61q.ra2_in1k,45.280,54.720,59.407,40.593,36.85,288,1.000,bicubic,-49.850,-39.853,-3
cs3edgenet_x.c2_in1k,45.257,54.743,60.260,39.740,47.82,288,1.000,bicubic,-50.203,-39.060,-73
nasnetalarge.tf_in1k,45.223,54.777,57.893,42.107,88.75,331,0.911,bicubic,-49.927,-41.237,-11
focalnet_tiny_lrf.ms_in1k,45.217,54.783,61.300,38.700,28.65,224,0.900,bicubic,-49.973,-37.920,-20
tresnet_xl.miil_in1k_448,45.210,54.790,61.437,38.563,78.44,448,0.875,bilinear,-50.300,-37.903,-87
flexivit_small.600ep_in1k,45.207,54.793,59.407,40.593,22.06,240,0.950,bicubic,-50.053,-39.973,-41
convit_small.fb_in1k,45.200,54.800,60.487,39.513,27.78,224,0.875,bicubic,-49.720,-38.693,+38
swin_small_patch4_window7_224.ms_in1k,45.183,54.817,60.357,39.643,49.61,224,0.900,bicubic,-50.527,-38.933,-121
sequencer2d_s.in1k,45.113,54.887,60.067,39.933,27.65,224,0.875,bicubic,-50.347,-39.193,-79
tf_efficientnet_b3.aa_in1k,45.103,54.897,60.643,39.357,12.23,300,0.904,bicubic,-49.807,-38.467,+37
rexnet_200.nav_in1k,45.057,54.943,62.323,37.677,16.37,224,0.875,bicubic,-49.613,-36.767,+87
maxxvit_rmlp_nano_rw_256.sw_in1k,45.050,54.950,59.663,40.337,16.78,256,0.950,bicubic,-50.290,-39.677,-61
resnetrs152.tf_in1k,44.950,55.050,59.690,40.310,86.62,320,1.000,bicubic,-51.010,-39.660,-178
deit_base_patch16_224.fb_in1k,44.870,55.130,59.177,40.823,86.57,224,0.900,bicubic,-50.150,-39.993,+12
flexivit_small.300ep_in1k,44.853,55.147,59.363,40.637,22.06,240,0.950,bicubic,-50.297,-39.787,-24
focalnet_tiny_srf.ms_in1k,44.833,55.167,61.037,38.963,28.43,224,0.900,bicubic,-50.217,-38.103,+2
coatnet_bn_0_rw_224.sw_in1k,44.803,55.197,60.913,39.087,27.44,224,0.950,bicubic,-50.177,-38.327,+16
deit_base_patch16_384.fb_in1k,44.770,55.230,59.600,40.400,86.86,384,1.000,bicubic,-50.870,-39.820,-121
resmlp_36_224.fb_distilled_in1k,44.763,55.237,61.073,38.927,44.69,224,0.875,bicubic,-49.797,-38.087,+99
cait_xxs36_384.fb_dist_in1k,44.760,55.240,59.387,40.613,17.37,384,1.000,bicubic,-50.480,-39.933,-51
resnet101.a1h_in1k,44.727,55.273,59.120,40.880,44.55,288,1.000,bicubic,-50.853,-40.150,-114
gernet_l.idstcv_in1k,44.717,55.283,58.947,41.053,31.08,256,0.875,bilinear,-50.213,-40.253,+20
tf_efficientnet_b2.ap_in1k,44.710,55.290,60.687,39.313,9.11,260,0.890,bicubic,-49.560,-38.403,+153
resmlp_24_224.fb_distilled_in1k,44.707,55.293,61.463,38.537,30.02,224,0.875,bicubic,-49.623,-37.627,+142
xcit_tiny_24_p16_224.fb_dist_in1k,44.707,55.293,59.417,40.583,12.12,224,1.000,bicubic,-49.533,-39.543,+157
regnety_032.tv2_in1k,44.653,55.347,60.900,39.100,19.44,224,0.965,bicubic,-50.217,-38.240,+29
ecaresnetlight.miil_in1k,44.580,55.420,60.383,39.617,30.16,288,0.950,bicubic,-49.950,-38.797,+95
swinv2_tiny_window16_256.ms_in1k,44.570,55.430,59.573,40.427,28.35,256,0.900,bicubic,-50.760,-39.577,-76
vit_relpos_small_patch16_224.sw_in1k,44.553,55.447,60.210,39.790,21.98,224,0.900,bicubic,-50.127,-38.710,+64
gmlp_s16_224.ra3_in1k,44.470,55.530,58.617,41.383,19.42,224,0.875,bicubic,-49.030,-40.223,+261
resnetv2_101.a1h_in1k,44.427,55.573,58.697,41.303,44.54,288,1.000,bicubic,-51.143,-40.573,-123
regnety_160.tv2_in1k,44.417,55.583,59.227,40.773,83.59,224,0.965,bicubic,-50.743,-40.023,-43
inception_resnet_v2.tf_ens_adv_in1k,44.393,55.607,58.117,41.883,55.84,299,0.897,bicubic,-49.727,-40.853,+170
tresnet_l.miil_in1k,44.387,55.613,59.947,40.053,55.99,224,0.875,bilinear,-50.513,-39.083,+15
maxxvitv2_nano_rw_256.sw_in1k,44.373,55.627,58.830,41.170,23.70,256,0.950,bicubic,-51.057,-40.360,-102
resnext101_32x4d.gluon_in1k,44.283,55.717,59.060,40.940,44.18,224,0.875,bicubic,-49.847,-39.790,+164
gcvit_xtiny.in1k,44.247,55.753,59.973,40.027,19.98,224,0.875,bicubic,-50.773,-39.187,-12
regnety_080_tv.tv2_in1k,44.180,55.820,58.787,41.213,39.38,224,0.965,bicubic,-51.120,-40.573,-81
maxvit_rmlp_nano_rw_256.sw_in1k,44.180,55.820,58.243,41.757,15.50,256,0.950,bicubic,-51.260,-40.817,-106
poolformer_m48.sail_in1k,44.160,55.840,59.153,40.847,73.47,224,0.950,bicubic,-50.940,-39.947,-35
regnetz_c16_evos.ch_in1k,44.143,55.857,61.073,38.927,13.49,320,0.950,bicubic,-51.497,-38.167,-145
vit_srelpos_small_patch16_224.sw_in1k,44.130,55.870,59.693,40.307,21.97,224,0.900,bicubic,-50.420,-39.457,+78
resnetv2_101x1_bit.goog_in21k_ft_in1k,44.127,55.873,61.950,38.050,44.54,448,1.000,bilinear,-51.193,-37.420,-89
crossvit_15_240.in1k,44.113,55.887,59.143,40.857,27.53,240,0.875,bicubic,-50.587,-39.987,+42
convnextv2_nano.fcmae_ft_in1k,44.100,55.900,60.167,39.833,15.62,288,1.000,bicubic,-51.040,-39.133,-51
pit_b_224.in1k,44.080,55.920,58.023,41.977,73.76,224,0.900,bicubic,-50.730,-41.237,+22
resnet152s.gluon_in1k,44.067,55.933,58.710,41.290,60.32,224,0.875,bicubic,-50.653,-40.120,+34
resnet50.fb_ssl_yfcc100m_ft_in1k,44.020,55.980,61.887,38.113,25.56,224,0.875,bilinear,-50.300,-37.263,+120
poolformer_m36.sail_in1k,44.010,55.990,59.033,40.967,56.17,224,0.950,bicubic,-51.020,-40.067,-26
inception_resnet_v2.tf_in1k,44.003,55.997,57.920,42.080,55.84,299,0.897,bicubic,-50.357,-40.880,+111
pnasnet5large.tf_in1k,43.943,56.057,56.740,43.260,86.06,331,0.911,bicubic,-51.417,-42.390,-104
resnext101_64x4d.gluon_in1k,43.913,56.087,58.707,41.293,83.46,224,0.875,bicubic,-50.437,-40.363,+113
wide_resnet50_2.tv2_in1k,43.910,56.090,59.630,40.370,68.88,224,0.965,bilinear,-50.900,-39.350,+11
pit_s_224.in1k,43.910,56.090,58.640,41.360,23.46,224,0.900,bicubic,-50.680,-40.500,+58
coatnext_nano_rw_224.sw_in1k,43.907,56.093,58.663,41.337,14.70,224,0.900,bicubic,-50.943,-40.537,+4
coat_lite_small.in1k,43.813,56.187,57.127,42.873,19.84,224,0.900,bicubic,-51.257,-41.993,-44
mobilevitv2_200.cvnets_in22k_ft_in1k,43.810,56.190,59.490,40.510,18.45,256,0.888,bicubic,-51.240,-39.660,-36
regnetv_040.ra3_in1k,43.783,56.217,58.453,41.547,20.64,288,1.000,bicubic,-51.947,-40.927,-178
tnt_s_patch16_224,43.777,56.223,59.220,40.780,23.76,224,0.900,bicubic,-50.783,-39.920,+58
swinv2_cr_small_224.sw_in1k,43.767,56.233,57.717,42.283,49.70,224,0.900,bicubic,-51.643,-41.343,-122
cspresnext50.ra_in1k,43.763,56.237,60.150,39.850,20.57,256,0.887,bilinear,-50.477,-38.900,+120
cait_xxs36_224.fb_dist_in1k,43.757,56.243,58.740,41.260,17.30,224,1.000,bicubic,-50.153,-40.350,+169
pit_xs_distilled_224.in1k,43.723,56.277,60.663,39.337,11.00,224,0.900,bicubic,-49.557,-38.057,+257
swin_s3_tiny_224.ms_in1k,43.717,56.283,59.507,40.493,28.33,224,0.900,bicubic,-51.203,-39.593,-20
tf_efficientnetv2_s.in1k,43.713,56.287,58.597,41.403,21.46,384,1.000,bicubic,-51.997,-40.783,-181
rexnet_150.nav_in1k,43.690,56.310,60.923,39.077,9.73,224,0.875,bicubic,-50.580,-37.897,+108
xcit_tiny_12_p8_224.fb_dist_in1k,43.643,56.357,58.477,41.523,6.71,224,1.000,bicubic,-51.047,-40.283,+21
edgenext_small.usi_in1k,43.637,56.363,59.893,40.107,5.59,320,1.000,bicubic,-51.193,-39.517,-6
tf_efficientnet_b5.in1k,43.620,56.380,60.130,39.870,30.39,456,0.934,bicubic,-52.250,-39.260,-214
efficientformer_l1.snap_dist_in1k,43.593,56.407,59.957,40.043,12.29,224,0.950,bicubic,-50.347,-38.973,+155
maxvit_nano_rw_256.sw_in1k,43.523,56.477,57.610,42.390,15.45,256,0.950,bicubic,-51.947,-41.780,-145
wide_resnet50_2.racm_in1k,43.510,56.490,59.060,40.940,68.88,288,0.950,bicubic,-51.620,-40.230,-76
cs3sedarknet_x.c2ns_in1k,43.503,56.497,58.770,41.230,35.40,288,1.000,bicubic,-51.907,-40.660,-137
coatnet_rmlp_nano_rw_224.sw_in1k,43.503,56.497,58.610,41.390,15.15,224,0.900,bicubic,-51.587,-40.560,-67
crossvit_small_240.in1k,43.483,56.517,58.947,41.053,26.86,240,0.875,bicubic,-51.097,-40.103,+37
regnety_016.tv2_in1k,43.433,56.567,59.547,40.453,11.20,224,0.965,bicubic,-50.977,-39.493,+71
resnet101d.gluon_in1k,43.430,56.570,58.627,41.373,44.57,224,0.875,bicubic,-50.750,-40.383,+116
ecaresnet50t.ra2_in1k,43.417,56.583,59.313,40.687,25.57,320,0.950,bicubic,-51.663,-39.827,-70
resnet101s.gluon_in1k,43.370,56.630,58.523,41.477,44.67,224,0.875,bicubic,-50.810,-40.417,+113
cspdarknet53.ra_in1k,43.350,56.650,59.420,40.580,27.64,256,0.887,bilinear,-50.740,-39.560,+125
tf_efficientnet_b4.in1k,43.333,56.667,59.450,40.550,19.34,380,0.922,bicubic,-52.147,-39.820,-158
xcit_tiny_24_p8_224.fb_in1k,43.323,56.677,57.277,42.723,12.11,224,1.000,bicubic,-51.577,-41.743,-34
xcit_tiny_12_p8_384.fb_dist_in1k,43.317,56.683,58.177,41.823,6.71,384,1.000,bicubic,-52.023,-41.133,-133
visformer_small.in1k,43.273,56.727,57.977,42.023,40.22,224,0.900,bicubic,-51.697,-41.193,-50
convmixer_768_32.in1k,43.270,56.730,59.383,40.617,21.11,224,0.960,bicubic,-51.170,-39.497,+57
eca_nfnet_l0.ra2_in1k,43.220,56.780,59.923,40.077,24.14,288,1.000,bicubic,-52.240,-39.357,-159
ecaresnet101d_pruned.miil_in1k,43.220,56.780,58.970,41.030,24.88,288,0.950,bicubic,-51.780,-40.260,-58
regnety_064.ra3_in1k,43.210,56.790,57.247,42.753,30.58,288,1.000,bicubic,-52.580,-42.103,-218
poolformerv2_s24.sail_in1k,43.193,56.807,60.430,39.570,21.34,224,1.000,bicubic,-51.277,-38.580,+46
regnetx_160.tv2_in1k,43.187,56.813,57.480,42.520,54.28,224,0.965,bicubic,-52.023,-41.680,-113
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,43.183,56.817,58.403,41.597,119.42,256,0.900,bicubic,-49.957,-39.907,+246
vit_small_patch32_384.augreg_in21k_ft_in1k,43.150,56.850,59.310,40.690,22.92,384,1.000,bicubic,-51.440,-39.760,+17
resnest26d.gluon_in1k,43.140,56.860,60.633,39.367,17.07,224,0.875,bilinear,-50.090,-38.217,+234
twins_pcpvt_small.in1k,43.120,56.880,58.900,41.100,24.11,224,0.900,bicubic,-51.480,-40.200,+11
regnetx_080.tv2_in1k,43.090,56.910,57.923,42.077,39.57,224,0.965,bicubic,-51.640,-41.107,-15
resmlp_36_224.fb_in1k,43.060,56.940,59.300,40.700,44.69,224,0.875,bicubic,-50.590,-39.650,+170
cspresnet50.ra_in1k,43.053,56.947,59.163,40.837,21.62,256,0.887,bilinear,-50.817,-39.737,+140
coatnet_nano_rw_224.sw_in1k,43.030,56.970,57.927,42.073,15.14,224,0.900,bicubic,-52.020,-41.153,-78
ecaresnet50d.miil_in1k,43.020,56.980,59.417,40.583,25.58,288,0.950,bicubic,-51.650,-39.843,-4
tf_efficientnet_lite4.in1k,42.990,57.010,57.620,42.380,13.01,380,0.920,bilinear,-51.880,-41.480,-43
twins_svt_small.in1k,42.920,57.080,58.460,41.540,24.06,224,0.900,bicubic,-51.840,-40.490,-27
dpn131.mx_in1k,42.917,57.083,57.130,42.870,79.25,224,0.875,bicubic,-50.863,-41.830,+147
mobilevitv2_200.cvnets_in22k_ft_in1k_384,42.913,57.087,58.973,41.027,18.45,384,1.000,bicubic,-52.477,-40.307,-161
resnet152.gluon_in1k,42.900,57.100,57.740,42.260,60.19,224,0.875,bicubic,-51.130,-41.110,+110
fbnetv3_d.ra2_in1k,42.857,57.143,59.687,40.313,10.31,256,0.950,bilinear,-51.013,-39.183,+131
resnet50.tv2_in1k,42.827,57.173,58.570,41.430,25.56,224,0.965,bilinear,-51.773,-40.520,+1
levit_conv_256.fb_dist_in1k,42.823,57.177,57.897,42.103,18.89,224,0.900,bicubic,-51.577,-41.163,+42
levit_256.fb_dist_in1k,42.813,57.187,57.893,42.107,18.89,224,0.900,bicubic,-51.587,-41.167,+40
resnet152c.gluon_in1k,42.813,57.187,57.720,42.280,60.21,224,0.875,bicubic,-51.067,-41.080,+126
tf_efficientnet_b1.ap_in1k,42.800,57.200,58.820,41.180,7.79,240,0.882,bicubic,-50.820,-39.950,+162
resnext50_32x4d.tv2_in1k,42.780,57.220,57.563,42.437,25.03,224,0.965,bilinear,-51.680,-41.337,+26
gcresnet50t.ra2_in1k,42.767,57.233,59.027,40.973,25.90,288,1.000,bicubic,-52.013,-40.093,-41
coatnet_0_rw_224.sw_in1k,42.753,57.247,56.230,43.770,27.44,224,0.950,bicubic,-52.147,-42.960,-63
tresnet_l.miil_in1k_448,42.743,57.257,58.943,41.057,55.99,448,0.875,bilinear,-52.657,-40.457,-174
cs3darknet_x.c2ns_in1k,42.727,57.273,58.190,41.810,35.05,288,1.000,bicubic,-52.553,-41.100,-153
dpn107.mx_in1k,42.710,57.290,57.160,42.840,86.92,224,0.875,bicubic,-51.300,-41.870,+103
seresnext50_32x4d.gluon_in1k,42.683,57.317,58.707,41.293,27.56,224,0.875,bicubic,-51.497,-40.213,+77
tresnet_m.miil_in1k,42.680,57.320,58.157,41.843,31.39,224,0.875,bilinear,-51.400,-40.673,+90
convnext_nano.d1h_in1k,42.677,57.323,57.577,42.423,15.59,288,1.000,bicubic,-52.193,-41.653,-63
resnetaa50d.sw_in12k_ft_in1k,42.610,57.390,58.490,41.510,25.58,288,1.000,bicubic,-52.680,-40.730,-160
xcit_tiny_12_p16_384.fb_dist_in1k,42.593,57.407,58.083,41.917,6.72,384,1.000,bicubic,-51.927,-40.887,+4
regnety_040.ra3_in1k,42.577,57.423,57.007,42.993,20.65,288,1.000,bicubic,-52.913,-42.413,-202
resnext101_32x8d.tv_in1k,42.563,57.437,58.303,41.697,88.79,224,0.875,bilinear,-51.217,-40.547,+125
nf_resnet50.ra2_in1k,42.520,57.480,59.530,40.470,25.56,288,0.940,bicubic,-51.860,-39.290,+30
mobilevitv2_175.cvnets_in22k_ft_in1k,42.517,57.483,58.117,41.883,14.25,256,0.888,bicubic,-52.263,-40.973,-53
seresnext50_32x4d.racm_in1k,42.433,57.567,58.093,41.907,27.56,288,0.950,bicubic,-52.567,-41.107,-96
resnetrs101.tf_in1k,42.417,57.583,57.283,42.717,63.62,288,0.940,bicubic,-52.833,-41.697,-161
poolformer_s36.sail_in1k,42.380,57.620,58.830,41.170,30.86,224,0.900,bicubic,-52.240,-40.290,-25
nest_tiny_jx.goog_in1k,42.320,57.680,57.067,42.933,17.06,224,0.875,bicubic,-52.600,-42.103,-83
tf_efficientnetv2_b3.in1k,42.313,57.687,57.943,42.057,14.36,300,0.904,bicubic,-52.807,-41.257,-129
xcit_tiny_24_p16_224.fb_in1k,42.277,57.723,56.837,43.163,12.12,224,1.000,bicubic,-51.573,-41.913,+109
resnet152.a1_in1k,42.273,57.727,55.530,44.470,60.19,288,1.000,bicubic,-52.817,-43.460,-125
convmixer_1024_20_ks9_p14.in1k,42.267,57.733,59.723,40.277,24.38,224,0.960,bicubic,-50.063,-38.707,+280
deit_small_patch16_224.fb_in1k,42.257,57.743,58.033,41.967,22.05,224,0.900,bicubic,-51.733,-41.007,+88
tf_efficientnet_cc_b1_8e.in1k,42.253,57.747,58.437,41.563,39.72,240,0.882,bicubic,-51.327,-40.253,+145
legacy_senet154.in1k,42.227,57.773,56.620,43.380,115.09,224,0.875,bilinear,-52.503,-42.480,-57
cait_xxs24_384.fb_dist_in1k,42.183,57.817,57.473,42.527,12.03,384,1.000,bicubic,-52.767,-41.657,-98
dpn98.mx_in1k,42.180,57.820,56.597,43.403,61.57,224,0.875,bicubic,-51.750,-42.313,+89
xception41p.ra3_in1k,42.157,57.843,56.883,43.117,26.91,299,0.940,bicubic,-52.903,-42.277,-123
tf_efficientnet_b2.aa_in1k,42.123,57.877,58.197,41.803,9.11,260,0.890,bicubic,-52.097,-40.733,+46
resnet50.b1k_in1k,42.080,57.920,58.153,41.847,25.56,288,1.000,bicubic,-52.430,-40.917,-10
resnext50_32x4d.gluon_in1k,42.050,57.950,57.673,42.327,25.03,224,0.875,bicubic,-51.620,-41.017,+123
convnext_nano_ols.d1h_in1k,42.023,57.977,56.867,43.133,15.65,288,1.000,bicubic,-52.557,-42.243,-30
pvt_v2_b1.in1k,41.960,58.040,59.597,40.403,14.01,224,0.900,bicubic,-51.530,-39.263,+148
xcit_tiny_12_p16_224.fb_dist_in1k,41.940,58.060,57.247,42.753,6.72,224,1.000,bicubic,-51.410,-41.443,+170
efficientnet_b2.ra_in1k,41.937,58.063,58.297,41.703,9.11,288,1.000,bicubic,-52.423,-40.743,+13
mobilevitv2_150.cvnets_in22k_ft_in1k,41.937,58.063,57.913,42.087,10.59,256,0.888,bicubic,-52.743,-41.197,-53
resnet50.b2k_in1k,41.910,58.090,57.673,42.327,25.56,288,1.000,bicubic,-52.390,-41.257,+23
efficientformerv2_s1.snap_dist_in1k,41.883,58.117,57.967,42.033,6.19,224,0.950,bicubic,-51.957,-40.923,+92
gcvit_xxtiny.in1k,41.833,58.167,58.443,41.557,12.00,224,0.875,bicubic,-52.217,-40.637,+60
tf_efficientnet_b3.in1k,41.797,58.203,58.060,41.940,12.23,300,0.904,bicubic,-52.493,-41.040,+21
mobilevitv2_150.cvnets_in22k_ft_in1k_384,41.790,58.210,57.807,42.193,10.59,384,1.000,bicubic,-53.560,-41.313,-203
resnet50d.ra2_in1k,41.787,58.213,58.023,41.977,25.58,288,0.950,bicubic,-53.023,-40.797,-87
resnext50_32x4d.a1h_in1k,41.753,58.247,56.443,43.557,25.03,288,1.000,bicubic,-53.237,-42.717,-123
gcresnext50ts.ch_in1k,41.703,58.297,57.403,42.597,15.67,288,1.000,bicubic,-52.787,-41.607,-22
poolformer_s24.sail_in1k,41.700,58.300,58.463,41.537,21.39,224,0.900,bicubic,-52.690,-40.597,-3
edgenext_small_rw.sw_in1k,41.677,58.323,58.517,41.483,7.83,320,1.000,bicubic,-52.683,-40.533,+3
mobilevitv2_175.cvnets_in22k_ft_in1k_384,41.673,58.327,58.010,41.990,14.25,384,1.000,bicubic,-53.587,-41.150,-194
hrnet_w64.ms_in1k,41.647,58.353,57.123,42.877,128.06,224,0.875,bilinear,-52.183,-41.817,+83
dla102x2.in1k,41.637,58.363,57.977,42.023,41.28,224,0.875,bilinear,-52.353,-40.983,+61
senet154.gluon_in1k,41.627,58.373,56.353,43.647,115.09,224,0.875,bicubic,-53.073,-42.617,-74
seresnet50.ra2_in1k,41.593,58.407,57.987,42.013,28.09,288,0.950,bicubic,-53.147,-41.123,-84
cs3sedarknet_l.c2ns_in1k,41.557,58.443,57.343,42.657,21.91,288,0.950,bicubic,-53.553,-41.867,-160
inception_v4.tf_in1k,41.557,58.443,55.380,44.620,42.68,299,0.875,bicubic,-52.823,-43.690,-8
haloregnetz_b.ra3_in1k,41.533,58.467,57.083,42.917,11.68,224,0.940,bicubic,-52.987,-42.087,-39
convnext_tiny.fb_in22k_ft_in1k,41.533,58.467,55.470,44.530,28.59,288,1.000,bicubic,-51.997,-43.130,+123
swinv2_cr_tiny_ns_224.sw_in1k,41.523,58.477,57.183,42.817,28.33,224,0.900,bicubic,-53.247,-41.927,-94
efficientnet_em.ra2_in1k,41.493,58.507,58.880,41.120,6.90,240,0.882,bicubic,-52.237,-40.050,+86
efficientnet_el.ra_in1k,41.490,58.510,58.293,41.707,10.59,300,0.904,bicubic,-53.180,-40.837,-73
tf_efficientnet_cc_b0_8e.in1k,41.487,58.513,57.363,42.637,24.01,224,0.875,bicubic,-51.383,-41.097,+203
convnextv2_pico.fcmae_ft_in1k,41.477,58.523,58.050,41.950,9.07,288,0.950,bicubic,-53.083,-41.120,-52
halo2botnet50ts_256.a1h_in1k,41.460,58.540,56.193,43.807,22.64,256,0.950,bicubic,-53.550,-42.947,-144
swin_tiny_patch4_window7_224.ms_in1k,41.453,58.547,57.303,42.697,28.29,224,0.900,bicubic,-53.167,-41.847,-71
resnetv2_50d_evos.ah_in1k,41.407,58.593,56.500,43.500,25.59,288,1.000,bicubic,-53.513,-42.600,-131
resnet50.a1h_in1k,41.387,58.613,56.677,43.323,25.56,224,1.000,bicubic,-52.823,-42.243,+16
cait_xxs24_224.fb_dist_in1k,41.370,58.630,57.520,42.480,11.96,224,1.000,bicubic,-52.080,-41.280,+124
swinv2_tiny_window8_256.ms_in1k,41.370,58.630,57.150,42.850,28.35,256,0.900,bicubic,-53.650,-41.820,-153
resnet152.tv_in1k,41.327,58.673,57.517,42.483,60.19,224,0.875,bilinear,-51.913,-41.183,+146
resnet50.ram_in1k,41.297,58.703,55.033,44.967,25.56,288,0.950,bicubic,-52.703,-43.847,+41
dpn68b.ra_in1k,41.293,58.707,55.080,44.920,12.61,288,1.000,bicubic,-52.497,-43.830,+67
cs3darknet_l.c2ns_in1k,41.273,58.727,57.383,42.617,21.16,288,0.950,bicubic,-53.407,-41.847,-89
xception71.tf_in1k,41.270,58.730,55.893,44.107,42.34,299,0.903,bicubic,-52.650,-43.057,+47
gernet_s.idstcv_in1k,41.260,58.740,58.823,41.177,8.17,224,0.875,bilinear,-51.180,-39.677,+224
inception_v3.tf_adv_in1k,41.240,58.760,56.327,43.673,23.83,299,0.875,bicubic,-51.760,-42.493,+168
resnet101.a1_in1k,41.207,58.793,54.273,45.727,44.55,288,1.000,bicubic,-53.733,-44.927,-144
dpn92.mx_in1k,41.203,58.797,56.217,43.783,37.67,224,0.875,bicubic,-52.947,-42.733,+11
resnetv2_50d_gn.ah_in1k,41.113,58.887,56.520,43.480,25.57,288,1.000,bicubic,-54.107,-42.510,-213
resnet50.c1_in1k,41.090,58.910,56.453,43.547,25.56,288,1.000,bicubic,-53.420,-42.547,-58
nf_regnet_b1.ra2_in1k,41.013,58.987,58.143,41.857,10.22,288,0.900,bicubic,-52.877,-40.747,+44
resnet50d.gluon_in1k,40.963,59.037,57.130,42.870,25.58,224,0.875,bicubic,-52.577,-41.580,+96
fbnetv3_b.ra2_in1k,40.960,59.040,58.650,41.350,8.60,256,0.950,bilinear,-52.670,-40.300,+78
resnet152.a3_in1k,40.937,59.063,55.020,44.980,60.19,224,0.950,bicubic,-53.503,-44.160,-48
ecaresnet50d_pruned.miil_in1k,40.910,59.090,57.633,42.367,19.94,288,0.950,bicubic,-53.390,-41.567,-23
inception_v3.gluon_in1k,40.907,59.093,55.627,44.373,23.83,299,0.875,bicubic,-52.653,-43.213,+88
resnetv2_50.a1h_in1k,40.893,59.107,56.383,43.617,25.55,288,1.000,bicubic,-53.797,-42.707,-105
cs3darknet_focus_l.c2ns_in1k,40.890,59.110,56.633,43.367,21.15,288,0.950,bicubic,-53.900,-42.527,-126
levit_conv_192.fb_dist_in1k,40.840,59.160,56.707,43.293,10.95,224,0.900,bicubic,-52.870,-42.113,+64
levit_192.fb_dist_in1k,40.837,59.163,56.710,43.290,10.95,224,0.900,bicubic,-52.873,-42.220,+62
regnety_320.pycls_in1k,40.810,59.190,56.110,43.890,145.05,224,0.875,bicubic,-53.710,-42.850,-75
regnetx_032.tv2_in1k,40.790,59.210,56.627,43.373,15.30,224,0.965,bicubic,-53.730,-42.143,-72
eva02_tiny_patch14_336.mim_in22k_ft_in1k,40.767,59.233,56.063,43.937,5.76,336,1.000,bicubic,-53.683,-43.037,-62
lamhalobotnet50ts_256.a1h_in1k,40.760,59.240,56.130,43.870,22.57,256,0.950,bicubic,-54.050,-43.100,-135
maxvit_rmlp_pico_rw_256.sw_in1k,40.757,59.243,55.203,44.797,7.52,256,0.950,bicubic,-53.453,-43.747,-14
legacy_xception.tf_in1k,40.753,59.247,56.397,43.603,22.86,299,0.897,bicubic,-52.867,-42.403,+69
resnet152.a2_in1k,40.737,59.263,54.280,45.720,60.19,288,1.000,bicubic,-54.233,-44.930,-168
vit_base_patch32_384.augreg_in1k,40.707,59.293,55.187,44.813,88.30,384,1.000,bicubic,-52.443,-43.423,+129
skresnext50_32x4d.ra_in1k,40.703,59.297,56.020,43.980,27.48,224,0.875,bicubic,-53.257,-42.810,+17
resnet101.gluon_in1k,40.693,59.307,56.123,43.877,44.55,224,0.875,bicubic,-53.067,-42.577,+42
hrnet_w40.ms_in1k,40.663,59.337,56.753,43.247,57.56,224,0.875,bilinear,-53.067,-42.047,+46
resmlp_24_224.fb_in1k,40.657,59.343,56.557,43.443,30.02,224,0.875,bicubic,-52.763,-42.273,+98
resnet50.am_in1k,40.620,59.380,57.377,42.623,25.56,224,0.875,bicubic,-53.010,-41.493,+59
resnext50_32x4d.ra_in1k,40.620,59.380,56.347,43.653,25.03,288,0.950,bicubic,-53.710,-42.683,-45
repvgg_b1.rvgg_in1k,40.603,59.397,57.843,42.157,57.42,224,0.875,bilinear,-52.837,-40.977,+90
ese_vovnet39b.ra_in1k,40.590,59.410,56.600,43.400,24.57,288,0.950,bicubic,-53.890,-42.460,-80
halonet50ts.a1h_in1k,40.573,59.427,55.197,44.803,22.73,256,0.940,bicubic,-54.147,-43.863,-133
tf_efficientnet_lite3.in1k,40.567,59.433,56.463,43.537,8.20,300,0.904,bilinear,-53.523,-42.497,-10
mobilevitv2_175.cvnets_in1k,40.537,59.463,56.273,43.727,14.25,256,0.888,bicubic,-53.693,-42.657,-33
xcit_tiny_12_p8_224.fb_in1k,40.537,59.463,55.647,44.353,6.71,224,1.000,bicubic,-53.813,-43.233,-55
dla169.in1k,40.523,59.477,57.247,42.753,53.39,224,0.875,bilinear,-53.267,-41.293,+27
tresnet_m.miil_in1k_448,40.513,59.487,56.690,43.310,31.39,448,0.875,bilinear,-54.147,-42.460,-121
regnetz_b16.ra3_in1k,40.510,59.490,55.997,44.003,9.72,288,1.000,bicubic,-54.160,-43.133,-124
pit_xs_224.in1k,40.497,59.503,56.540,43.460,10.62,224,0.900,bicubic,-52.393,-42.230,+147
resnetaa50.a1h_in1k,40.483,59.517,55.993,44.007,25.56,288,1.000,bicubic,-54.367,-43.127,-163
vit_base_patch16_384.augreg_in1k,40.473,59.527,53.247,46.753,86.86,384,1.000,bicubic,-53.967,-45.773,-81
regnetx_320.pycls_in1k,40.470,59.530,55.653,44.347,107.81,224,0.875,bicubic,-53.760,-43.167,-42
repvgg_b2.rvgg_in1k,40.467,59.533,57.780,42.220,89.02,224,0.875,bilinear,-53.113,-41.000,+52
coat_mini.in1k,40.417,59.583,55.200,44.800,10.34,224,0.900,bicubic,-54.343,-43.890,-152
eca_resnet33ts.ra2_in1k,40.400,59.600,57.337,42.663,19.68,288,1.000,bicubic,-53.850,-41.693,-48
resnet34d.ra2_in1k,40.400,59.600,56.170,43.830,21.82,288,0.950,bicubic,-53.200,-42.590,+45
skresnet34.ra_in1k,40.390,59.610,56.720,43.280,22.28,224,0.875,bicubic,-52.180,-41.800,+168
efficientnet_el_pruned.in1k,40.377,59.623,56.893,43.107,10.59,300,0.904,bicubic,-53.703,-42.127,-24
efficientnet_b2_pruned.in1k,40.373,59.627,56.523,43.477,8.31,260,0.890,bicubic,-53.427,-42.387,+12
wide_resnet101_2.tv_in1k,40.360,59.640,55.793,44.207,126.89,224,0.875,bilinear,-53.360,-43.007,+23
coat_lite_mini.in1k,40.357,59.643,55.697,44.303,11.01,224,0.900,bicubic,-53.113,-43.073,+62
legacy_seresnext101_32x4d.in1k,40.347,59.653,54.817,45.183,48.96,224,0.875,bilinear,-53.773,-43.973,-33
sebotnet33ts_256.a1h_in1k,40.347,59.653,53.223,46.777,13.70,256,0.940,bicubic,-53.963,-45.377,-67
tf_efficientnet_b0.ap_in1k,40.343,59.657,56.803,43.197,5.29,224,0.875,bicubic,-52.277,-41.567,+155
densenet201.tv_in1k,40.283,59.717,56.713,43.287,20.01,224,0.875,bicubic,-52.417,-41.887,+148
regnetx_160.pycls_in1k,40.253,59.747,56.050,43.950,54.28,224,0.875,bicubic,-53.657,-42.840,-9
xception65.tf_in1k,40.253,59.747,55.263,44.737,39.92,299,0.903,bicubic,-53.497,-43.597,+13
resnet101.a2_in1k,40.190,59.810,54.223,45.777,44.55,288,1.000,bicubic,-54.700,-45.047,-188
resnet50.c2_in1k,40.187,59.813,55.247,44.753,25.56,288,1.000,bicubic,-54.083,-43.793,-67
resnet50.ra_in1k,40.180,59.820,56.197,43.803,25.56,288,0.950,bicubic,-53.920,-42.643,-39
poolformerv2_s12.sail_in1k,40.163,59.837,57.450,42.550,11.89,224,1.000,bicubic,-52.727,-41.080,+126
resnetblur50.bt_in1k,40.157,59.843,56.187,43.813,25.56,288,0.950,bicubic,-54.013,-42.823,-50
mobilevitv2_200.cvnets_in1k,40.140,59.860,55.517,44.483,18.45,256,0.888,bicubic,-54.380,-43.393,-120
darknetaa53.c2ns_in1k,40.120,59.880,55.777,44.223,36.02,288,1.000,bilinear,-54.090,-43.223,-57
hrnet_w48.ms_in1k,40.107,59.893,56.623,43.377,77.47,224,0.875,bilinear,-53.903,-42.307,-32
vit_base_patch16_224.sam_in1k,40.097,59.903,55.427,44.573,86.57,224,0.900,bicubic,-53.793,-43.323,-17
resnet50_gn.a1h_in1k,40.060,59.940,54.857,45.143,25.56,288,0.950,bicubic,-54.560,-44.193,-149
legacy_seresnet152.in1k,40.053,59.947,55.830,44.170,66.82,224,0.875,bilinear,-53.357,-43.000,+58
resnet50.d_in1k,40.043,59.957,54.717,45.283,25.56,288,1.000,bicubic,-54.427,-44.283,-116
hrnet_w30.ms_in1k,40.037,59.963,57.120,42.880,37.71,224,0.875,bilinear,-53.373,-41.730,+57
regnetx_080.pycls_in1k,39.990,60.010,55.950,44.050,39.57,224,0.875,bicubic,-53.800,-42.910,-9
seresnet33ts.ra2_in1k,39.987,60.013,56.403,43.597,19.78,288,1.000,bicubic,-54.273,-42.597,-76
tf_efficientnet_b1.aa_in1k,39.967,60.033,56.123,43.877,7.79,240,0.882,bicubic,-53.753,-42.687,+1
resnet101c.gluon_in1k,39.957,60.043,55.283,44.717,44.57,224,0.875,bicubic,-53.733,-43.477,+6
convnext_pico_ols.d1_in1k,39.887,60.113,55.610,44.390,9.06,288,1.000,bicubic,-54.123,-43.420,-40
resmlp_12_224.fb_distilled_in1k,39.833,60.167,57.430,42.570,15.35,224,0.875,bicubic,-53.037,-41.190,+113
tf_efficientnetv2_b0.in1k,39.800,60.200,56.287,43.713,7.14,224,0.875,bicubic,-53.260,-42.413,+80
res2net50_26w_8s.in1k,39.800,60.200,54.900,45.100,48.40,224,0.875,bilinear,-53.610,-43.850,+52
darknet53.c2ns_in1k,39.740,60.260,55.283,44.717,41.61,288,1.000,bicubic,-54.620,-43.767,-104
res2net101_26w_4s.in1k,39.720,60.280,54.550,45.450,45.21,224,0.875,bilinear,-53.810,-44.020,+23
lambda_resnet50ts.a1h_in1k,39.720,60.280,54.373,45.627,21.54,256,0.950,bicubic,-54.850,-44.537,-147
hrnet_w44.ms_in1k,39.690,60.310,55.330,44.670,67.06,224,0.875,bilinear,-53.930,-43.630,+6
regnetx_120.pycls_in1k,39.687,60.313,55.643,44.357,46.11,224,0.875,bicubic,-54.573,-43.527,-88
vit_small_patch32_224.augreg_in21k_ft_in1k,39.677,60.323,55.253,44.747,22.88,224,0.900,bicubic,-52.463,-43.267,+155
resmlp_big_24_224.fb_in1k,39.637,60.363,54.830,45.170,129.14,224,0.875,bicubic,-54.633,-44.120,-91
vit_small_patch16_384.augreg_in1k,39.630,60.370,54.237,45.763,22.20,384,1.000,bicubic,-54.980,-44.903,-165
mixnet_xl.ra_in1k,39.623,60.377,55.873,44.127,11.90,224,0.875,bicubic,-54.607,-43.177,-85
xception41.tf_in1k,39.607,60.393,55.013,44.987,26.97,299,0.903,bicubic,-53.873,-43.737,+24
densenet161.tv_in1k,39.603,60.397,56.130,43.870,28.68,224,0.875,bicubic,-53.287,-42.660,+96
dla102x.in1k,39.567,60.433,56.320,43.680,26.31,224,0.875,bilinear,-53.973,-42.530,+12
tf_efficientnetv2_b1.in1k,39.567,60.433,55.337,44.663,8.14,240,0.882,bicubic,-54.143,-43.453,-15
xcit_tiny_12_p16_224.fb_in1k,39.540,60.460,55.050,44.950,6.72,224,1.000,bicubic,-52.940,-43.380,+129
sehalonet33ts.ra2_in1k,39.540,60.460,54.027,45.973,13.69,256,0.940,bicubic,-54.980,-45.143,-146
convnext_pico.d1_in1k,39.520,60.480,55.317,44.683,9.05,288,0.950,bicubic,-54.510,-43.693,-65
tf_efficientnet_b2.in1k,39.513,60.487,56.107,43.893,9.11,260,0.890,bicubic,-54.197,-42.683,-20
rexnet_130.nav_in1k,39.480,60.520,56.637,43.363,7.56,224,0.875,bicubic,-54.200,-42.063,-15
hrnet_w32.ms_in1k,39.473,60.527,56.123,43.877,41.23,224,0.875,bilinear,-53.487,-42.727,+77
levit_128.fb_dist_in1k,39.447,60.553,55.350,44.650,9.21,224,0.900,bicubic,-53.583,-43.360,+62
levit_conv_128.fb_dist_in1k,39.447,60.553,55.337,44.663,9.21,224,0.900,bicubic,-53.583,-43.363,+62
resnetv2_50x1_bit.goog_in21k_ft_in1k,39.433,60.567,57.853,42.147,25.55,448,1.000,bilinear,-55.307,-41.327,-206
ecaresnet50t.a1_in1k,39.417,60.583,53.680,46.320,25.57,288,1.000,bicubic,-55.473,-45.380,-232
regnety_064.pycls_in1k,39.393,60.607,55.770,44.230,30.58,224,0.875,bicubic,-54.737,-43.260,-87
regnety_120.pycls_in1k,39.347,60.653,55.277,44.723,51.82,224,0.875,bicubic,-54.663,-43.543,-69
gcresnet33ts.ra2_in1k,39.327,60.673,55.887,44.113,19.88,288,1.000,bicubic,-55.023,-43.213,-123
mobilevitv2_150.cvnets_in1k,39.323,60.677,55.230,44.770,10.59,256,0.888,bicubic,-54.727,-43.670,-77
tf_efficientnet_el.in1k,39.297,60.703,55.377,44.623,10.59,300,0.904,bicubic,-55.053,-43.583,-127
resnet101.tv_in1k,39.293,60.707,55.790,44.210,44.55,224,0.875,bilinear,-53.587,-42.870,+81
resnet50s.gluon_in1k,39.257,60.743,55.023,44.977,25.68,224,0.875,bicubic,-54.313,-43.817,-10
regnety_160.pycls_in1k,39.250,60.750,55.447,44.553,83.59,224,0.875,bicubic,-54.880,-43.573,-93
resnet101.a3_in1k,39.250,60.750,53.707,46.293,44.55,224,0.950,bicubic,-54.610,-45.053,-54
inception_v3.tf_in1k,39.223,60.777,54.310,45.690,23.83,299,0.875,bicubic,-53.967,-44.350,+36
resnext50_32x4d.a1_in1k,39.183,60.817,53.360,46.640,25.03,288,1.000,bicubic,-55.197,-45.420,-138
densenet169.tv_in1k,39.170,60.830,55.860,44.140,14.15,224,0.875,bicubic,-53.130,-42.750,+119
tf_efficientnetv2_b2.in1k,39.167,60.833,54.567,45.433,10.10,260,0.890,bicubic,-54.893,-44.363,-88
resnext50d_32x4d.bt_in1k,39.100,60.900,54.343,45.657,25.05,288,0.950,bicubic,-55.300,-44.707,-147
legacy_seresnet101.in1k,39.040,60.960,55.003,44.997,49.33,224,0.875,bilinear,-54.230,-43.737,+23
efficientnet_b1_pruned.in1k,39.007,60.993,55.633,44.367,6.33,240,0.882,bicubic,-53.973,-43.097,+56
repvgg_b1g4.rvgg_in1k,38.980,61.020,56.353,43.647,39.97,224,0.875,bilinear,-54.050,-42.247,+41
crossvit_9_dagger_240.in1k,38.967,61.033,54.870,45.130,8.78,240,0.875,bicubic,-53.793,-43.780,+81
resnet50.a1_in1k,38.963,61.037,53.243,46.757,25.56,288,1.000,bicubic,-55.257,-45.507,-116
inception_v3.tv_in1k,38.943,61.057,53.873,46.127,23.83,299,0.875,bicubic,-53.957,-44.857,+63
regnety_080.pycls_in1k,38.910,61.090,55.190,44.810,39.18,224,0.875,bicubic,-54.970,-43.810,-71
res2net101d.in1k,38.907,61.093,53.053,46.947,45.23,224,0.875,bilinear,-55.623,-45.927,-183
legacy_seresnext50_32x4d.in1k,38.890,61.110,54.577,45.423,27.56,224,0.875,bilinear,-54.560,-44.203,-8
dla102.in1k,38.833,61.167,55.310,44.690,33.27,224,0.875,bilinear,-54.417,-43.480,+15
visformer_tiny.in1k,38.830,61.170,55.023,44.977,10.32,224,0.900,bicubic,-54.150,-43.647,+44
regnety_040.pycls_in1k,38.823,61.177,55.580,44.420,20.65,224,0.875,bicubic,-54.807,-43.330,-42
regnetx_040.pycls_in1k,38.710,61.290,55.357,44.643,22.12,224,0.875,bicubic,-54.980,-43.573,-49
res2net50_14w_8s.in1k,38.710,61.290,54.073,45.927,25.06,224,0.875,bilinear,-54.320,-44.747,+33
dpn68.mx_in1k,38.687,61.313,54.687,45.313,12.61,224,0.875,bicubic,-53.613,-43.903,+101
regnetx_032.pycls_in1k,38.680,61.320,55.160,44.840,15.30,224,0.875,bicubic,-54.560,-43.560,+11
res2net50_26w_6s.in1k,38.680,61.320,53.773,46.227,37.05,224,0.875,bilinear,-54.910,-44.857,-40
resnet33ts.ra2_in1k,38.667,61.333,55.167,44.833,19.68,288,1.000,bicubic,-55.263,-43.713,-88
wide_resnet50_2.tv_in1k,38.650,61.350,54.463,45.537,68.88,224,0.875,bilinear,-54.810,-44.487,-19
regnetx_016.tv2_in1k,38.620,61.380,54.733,45.267,9.19,224,0.965,bicubic,-55.400,-44.197,-103
SelecSls60.in1k,38.613,61.387,55.613,44.387,30.67,224,0.875,bicubic,-54.387,-42.877,+31
dla60x.in1k,38.607,61.393,55.400,44.600,17.35,224,0.875,bilinear,-54.583,-43.320,+8
densenetblur121d.ra_in1k,38.600,61.400,55.640,44.360,8.00,288,0.950,bicubic,-54.430,-43.060,+25
tf_efficientnet_b0.aa_in1k,38.587,61.413,55.977,44.023,5.29,224,0.875,bicubic,-53.813,-42.493,+87
dla60_res2net.in1k,38.583,61.417,54.577,45.423,20.85,224,0.875,bilinear,-54.787,-44.253,-9
SelecSls60b.in1k,38.557,61.443,55.313,44.687,32.77,224,0.875,bicubic,-54.943,-43.467,-34
repvgg_a2.rvgg_in1k,38.553,61.447,55.767,44.233,28.21,224,0.875,bilinear,-54.127,-42.513,+63
seresnet50.a1_in1k,38.537,61.463,53.413,46.587,28.09,288,1.000,bicubic,-55.623,-45.437,-130
dpn68b.mx_in1k,38.530,61.470,55.183,44.817,12.61,224,0.875,bicubic,-54.260,-43.337,+51
hardcorenas_f.miil_green_in1k,38.513,61.487,55.653,44.347,8.20,224,0.875,bilinear,-54.467,-42.967,+27
resnet32ts.ra2_in1k,38.503,61.497,55.497,44.503,17.96,288,1.000,bicubic,-55.087,-43.243,-53
dla60_res2next.in1k,38.443,61.557,54.950,45.050,17.03,224,0.875,bilinear,-55.137,-44.120,-51
resmlp_12_224.fb_in1k,38.440,61.560,56.327,43.673,15.35,224,0.875,bicubic,-53.680,-42.243,+93
tf_efficientnet_cc_b0_4e.in1k,38.430,61.570,55.177,44.823,13.31,224,0.875,bicubic,-54.390,-43.263,+44
regnetx_064.pycls_in1k,38.420,61.580,54.993,45.007,26.21,224,0.875,bicubic,-55.220,-44.047,-66
resnet50.gluon_in1k,38.410,61.590,54.830,45.170,25.56,224,0.875,bicubic,-54.150,-43.720,+65
resnet50d.a1_in1k,38.403,61.597,52.860,47.140,25.58,288,1.000,bicubic,-55.997,-46.200,-183
regnety_008_tv.tv2_in1k,38.343,61.657,54.267,45.733,6.43,224,0.965,bicubic,-54.817,-44.413,-4
hrnet_w18.ms_in1k,38.267,61.733,55.660,44.340,21.30,224,0.875,bilinear,-54.503,-42.960,+43
tinynet_a.in1k,38.227,61.773,55.187,44.813,6.19,192,0.875,bicubic,-54.573,-43.373,+38
ecaresnet50t.a3_in1k,38.227,61.773,53.650,46.350,25.57,224,0.950,bicubic,-55.633,-45.200,-100
resnet34.a1_in1k,38.220,61.780,52.373,47.627,21.80,288,1.000,bicubic,-54.780,-46.257,+11
densenet121.ra_in1k,38.177,61.823,55.140,44.860,7.98,288,0.950,bicubic,-54.523,-43.500,+46
mixnet_l.ft_in1k,38.173,61.827,54.800,45.200,7.33,224,0.875,bicubic,-55.067,-43.950,-16
regnety_032.pycls_in1k,38.170,61.830,54.370,45.630,19.44,224,0.875,bicubic,-55.290,-44.580,-46
poolformer_s12.sail_in1k,38.157,61.843,56.207,43.793,11.92,224,0.900,bicubic,-54.343,-42.193,+57
hardcorenas_e.miil_green_in1k,38.157,61.843,55.173,44.827,8.07,224,0.875,bilinear,-54.783,-43.497,+16
efficientnet_b1.ft_in1k,38.080,61.920,54.000,46.000,7.79,256,1.000,bicubic,-54.940,-44.710,+2
ecaresnet50t.a2_in1k,38.053,61.947,52.967,47.033,25.57,288,1.000,bicubic,-56.517,-46.073,-233
gmixer_24_224.ra3_in1k,38.050,61.950,52.077,47.923,24.72,224,0.875,bicubic,-54.630,-46.453,+41
vit_base_patch16_224.augreg_in1k,38.043,61.957,50.710,49.290,86.57,224,0.900,bicubic,-55.307,-48.050,-31
coat_lite_tiny.in1k,38.033,61.967,53.463,46.537,5.72,224,0.900,bicubic,-54.827,-45.167,+24
resnetrs50.tf_in1k,37.970,62.030,53.303,46.697,35.69,224,0.910,bicubic,-56.060,-45.437,-138
resnext50_32x4d.a2_in1k,37.933,62.067,52.353,47.647,25.03,288,1.000,bicubic,-56.287,-46.687,-164
mobilevitv2_125.cvnets_in1k,37.893,62.107,54.067,45.933,7.48,256,0.888,bicubic,-55.587,-44.773,-59
resnet50c.gluon_in1k,37.880,62.120,54.117,45.883,25.58,224,0.875,bicubic,-55.030,-44.583,+11
hardcorenas_c.miil_green_in1k,37.857,62.143,55.717,44.283,5.52,224,0.875,bilinear,-54.493,-42.623,+57
efficientformerv2_s0.snap_dist_in1k,37.820,62.180,54.047,45.953,3.60,224,0.950,bicubic,-54.050,-44.323,+77
res2net50_26w_4s.in1k,37.810,62.190,53.080,46.920,25.70,224,0.875,bilinear,-55.370,-45.580,-26
efficientnet_es.ra_in1k,37.783,62.217,54.957,45.043,5.44,224,0.875,bicubic,-55.137,-43.603,+4
resnest14d.gluon_in1k,37.773,62.227,56.473,43.527,10.61,224,0.875,bilinear,-53.377,-41.857,+102
convnext_femto.d1_in1k,37.733,62.267,54.100,45.900,5.22,288,0.950,bicubic,-55.707,-44.550,-58
resnext50_32x4d.tv_in1k,37.727,62.273,54.103,45.897,25.03,224,0.875,bilinear,-55.173,-44.217,+5
pit_ti_distilled_224.in1k,37.707,62.293,55.643,44.357,5.10,224,0.900,bicubic,-53.023,-42.607,+111
ecaresnet26t.ra2_in1k,37.667,62.333,54.357,45.643,16.01,320,0.950,bicubic,-56.273,-44.673,-137
seresnet50.a2_in1k,37.667,62.333,52.373,47.627,28.09,288,1.000,bicubic,-56.783,-46.517,-224
vit_base_patch32_224.augreg_in1k,37.563,62.437,51.810,48.190,88.22,224,0.900,bicubic,-53.027,-45.910,+111
hardcorenas_d.miil_green_in1k,37.530,62.470,54.710,45.290,7.50,224,0.875,bilinear,-55.080,-43.800,+28
res2next50.in1k,37.483,62.517,52.853,47.147,24.67,224,0.875,bilinear,-55.667,-45.787,-33
convnextv2_femto.fcmae_ft_in1k,37.477,62.523,53.507,46.493,5.23,288,0.950,bicubic,-56.273,-45.463,-116
resnet50.bt_in1k,37.420,62.580,53.860,46.140,25.56,288,0.950,bicubic,-56.540,-45.070,-146
hrnet_w18.ms_aug_in1k,37.337,62.663,54.123,45.877,21.30,224,0.950,bilinear,-56.143,-44.857,-77
lambda_resnet26t.c1_in1k,37.300,62.700,53.560,46.440,10.96,256,0.940,bicubic,-56.130,-45.170,-65
convnext_femto_ols.d1_in1k,37.253,62.747,53.047,46.953,5.23,288,0.950,bicubic,-56.137,-45.863,-59
hardcorenas_b.miil_green_in1k,37.240,62.760,55.037,44.963,5.18,224,0.875,bilinear,-54.680,-43.373,+57
resnet50d.a2_in1k,37.227,62.773,51.743,48.257,25.58,288,1.000,bicubic,-57.233,-47.287,-236
mobilenetv3_large_100.miil_in21k_ft_in1k,37.223,62.777,53.540,46.460,5.48,224,0.875,bilinear,-55.037,-44.700,+45
res2net50d.in1k,37.223,62.777,51.387,48.613,25.72,224,0.875,bilinear,-57.057,-47.473,-203
eca_halonext26ts.c1_in1k,37.170,62.830,53.103,46.897,10.76,256,0.940,bicubic,-56.380,-45.717,-93
cs3darknet_focus_m.c2ns_in1k,37.140,62.860,53.910,46.090,9.30,288,0.950,bicubic,-55.960,-44.840,-39
res2net50_48w_2s.in1k,37.120,62.880,53.327,46.673,25.29,224,0.875,bilinear,-55.660,-45.143,0
lambda_resnet26rpt_256.c1_in1k,37.103,62.897,53.860,46.140,10.99,256,0.940,bicubic,-56.327,-45.020,-75
vit_small_patch16_224.augreg_in1k,37.077,62.923,51.547,48.453,22.05,224,0.900,bicubic,-56.373,-47.233,-80
rexnet_100.nav_in1k,37.063,62.937,54.047,45.953,4.80,224,0.875,bicubic,-55.767,-44.553,-7
bat_resnext26ts.ch_in1k,37.057,62.943,53.743,46.257,10.73,256,0.900,bicubic,-56.063,-44.987,-45
resnet50.a2_in1k,37.047,62.953,51.347,48.653,25.56,288,1.000,bicubic,-57.083,-47.593,-183
dla60.in1k,37.043,62.957,54.213,45.787,22.04,224,0.875,bilinear,-55.597,-44.417,+6
tf_efficientnet_b1.in1k,37.017,62.983,53.413,46.587,7.79,240,0.882,bicubic,-55.923,-45.117,-27
regnety_016.pycls_in1k,37.013,62.987,54.083,45.917,11.20,224,0.875,bicubic,-55.997,-44.587,-38
tf_mixnet_l.in1k,36.967,63.033,52.597,47.403,7.33,224,0.875,bicubic,-56.063,-45.933,-41
botnet26t_256.c1_in1k,36.963,63.037,53.053,46.947,12.49,256,0.950,bicubic,-56.477,-45.737,-85
resnet34.a2_in1k,36.953,63.047,51.433,48.567,21.80,288,1.000,bicubic,-55.617,-47.137,+7
legacy_seresnet50.in1k,36.863,63.137,53.473,46.527,28.09,224,0.875,bilinear,-55.807,-45.177,-1
halonet26t.a1h_in1k,36.843,63.157,52.270,47.730,12.48,256,0.950,bicubic,-56.747,-46.470,-115
densenet121.tv_in1k,36.807,63.193,54.030,45.970,7.98,224,0.875,bicubic,-54.593,-44.220,+57
tf_efficientnet_lite2.in1k,36.797,63.203,53.313,46.687,6.09,260,0.890,bicubic,-55.793,-45.227,+2
mobilenetv2_120d.ra_in1k,36.793,63.207,54.047,45.953,5.83,224,0.875,bicubic,-55.817,-44.383,-2
tf_efficientnet_lite1.in1k,36.727,63.273,53.580,46.420,5.42,240,0.882,bicubic,-55.563,-44.920,+20
hardcorenas_a.miil_green_in1k,36.707,63.293,54.930,45.070,5.26,224,0.875,bilinear,-54.913,-43.360,+44
regnetx_016.pycls_in1k,36.693,63.307,53.307,46.693,9.19,224,0.875,bicubic,-55.837,-45.243,+3
eca_botnext26ts_256.c1_in1k,36.690,63.310,52.493,47.507,10.59,256,0.950,bicubic,-56.660,-46.197,-83
levit_conv_128s.fb_dist_in1k,36.623,63.377,53.137,46.863,7.78,224,0.900,bicubic,-54.877,-45.263,+45
levit_128s.fb_dist_in1k,36.623,63.377,53.130,46.870,7.78,224,0.900,bicubic,-54.887,-45.270,+45
efficientnet_b0.ra_in1k,36.593,63.407,53.477,46.523,5.29,224,0.875,bicubic,-55.887,-45.203,+2
resnext50_32x4d.a3_in1k,36.553,63.447,51.143,48.857,25.03,224,0.950,bicubic,-56.997,-47.537,-120
vit_base_patch32_224.sam_in1k,36.543,63.457,53.043,46.957,88.22,224,0.900,bicubic,-53.327,-44.557,+86
xcit_nano_12_p8_224.fb_dist_in1k,36.513,63.487,52.873,47.127,3.05,224,1.000,bicubic,-55.917,-45.657,+3
cs3darknet_m.c2ns_in1k,36.480,63.520,53.230,46.770,9.31,288,0.950,bicubic,-56.800,-45.560,-85
mobilevitv2_100.cvnets_in1k,36.397,63.603,53.073,46.927,4.90,256,0.888,bicubic,-56.743,-45.687,-71
tf_efficientnet_em.in1k,36.390,63.610,52.843,47.157,6.90,240,0.882,bicubic,-56.800,-45.647,-79
resnet50d.a3_in1k,36.337,63.663,51.330,48.670,25.58,224,0.950,bicubic,-57.073,-47.360,-98
skresnet18.ra_in1k,36.313,63.687,54.183,45.817,11.96,224,0.875,bicubic,-53.867,-43.597,+78
repvgg_b0.rvgg_in1k,36.293,63.707,54.063,45.937,15.82,224,0.875,bilinear,-55.387,-44.387,+26
resnet34.bt_in1k,36.257,63.743,52.757,47.243,21.80,288,0.950,bicubic,-56.023,-45.843,+5
resnet50.tv_in1k,36.183,63.817,52.797,47.203,25.56,224,0.875,bilinear,-55.937,-45.613,+13
legacy_seresnet34.in1k,36.150,63.850,52.570,47.430,21.96,224,0.875,bilinear,-55.340,-45.630,+33
xcit_nano_12_p16_384.fb_dist_in1k,36.140,63.860,53.247,46.753,3.05,384,1.000,bicubic,-56.000,-45.263,+8
coat_tiny.in1k,36.117,63.883,51.047,48.953,5.50,224,0.900,bicubic,-57.393,-47.633,-127
resnet34.tv_in1k,36.070,63.930,53.537,46.463,21.80,224,0.875,bilinear,-54.230,-44.433,+66
deit_tiny_distilled_patch16_224.fb_in1k,36.023,63.977,54.247,45.753,5.91,224,0.900,bicubic,-55.057,-44.023,+48
mobilenetv2_140.ra_in1k,35.997,64.003,53.963,46.037,6.11,224,0.875,bicubic,-56.053,-44.287,+9
convnextv2_atto.fcmae_ft_in1k,35.990,64.010,51.183,48.817,3.71,288,0.950,bicubic,-56.930,-47.497,-56
resnet50.a3_in1k,35.950,64.050,50.487,49.513,25.56,224,0.950,bicubic,-56.990,-48.173,-60
tf_efficientnet_lite0.in1k,35.940,64.060,53.490,46.510,4.65,224,0.875,bicubic,-55.340,-44.350,+30
SelecSls42b.in1k,35.823,64.177,52.473,47.527,32.46,224,0.875,bicubic,-56.657,-46.157,-17
xcit_nano_12_p8_384.fb_dist_in1k,35.797,64.203,52.297,47.703,3.05,384,1.000,bicubic,-57.503,-46.563,-105
seresnext26ts.ch_in1k,35.780,64.220,53.460,46.540,10.39,288,1.000,bicubic,-57.160,-45.120,-68
resnet34.gluon_in1k,35.780,64.220,52.183,47.817,21.80,224,0.875,bicubic,-55.310,-45.987,+40
convnext_atto.d2_in1k,35.773,64.227,52.323,47.677,3.70,288,0.950,bicubic,-56.997,-46.337,-46
seresnet50.a3_in1k,35.757,64.243,51.163,48.837,28.09,224,0.950,bicubic,-56.603,-47.167,-17
resnet26t.ra2_in1k,35.727,64.273,53.583,46.417,16.01,320,1.000,bicubic,-57.193,-45.107,-66
efficientnet_lite0.ra_in1k,35.647,64.353,53.667,46.333,4.65,224,0.875,bicubic,-55.613,-44.573,+24
dla34.in1k,35.637,64.363,52.797,47.203,15.74,224,0.875,bilinear,-55.583,-45.373,+24
mixnet_m.ft_in1k,35.633,64.367,52.430,47.570,5.01,224,0.875,bicubic,-56.637,-45.930,-13
resnet18.fb_ssl_yfcc100m_ft_in1k,35.613,64.387,53.753,46.247,11.69,224,0.875,bilinear,-55.087,-44.277,+41
mobilenetv3_rw.rmsp_in1k,35.543,64.457,53.707,46.293,5.48,224,0.875,bicubic,-56.007,-44.573,+10
regnetx_008.tv2_in1k,35.533,64.467,51.477,48.523,7.26,224,0.965,bicubic,-56.957,-46.953,-32
convnext_atto_ols.a2_in1k,35.403,64.597,51.390,48.610,3.70,288,0.950,bicubic,-57.577,-47.140,-82
efficientnet_es_pruned.in1k,35.387,64.613,52.837,47.163,5.44,224,0.875,bicubic,-56.303,-45.573,-1
mobilenetv2_110d.ra_in1k,35.307,64.693,52.870,47.130,4.52,224,0.875,bicubic,-56.023,-45.320,+13
tf_mixnet_m.in1k,35.197,64.803,50.990,49.010,5.01,224,0.875,bicubic,-57.013,-47.430,-18
hrnet_w18_small_v2.ms_in1k,35.163,64.837,52.423,47.577,15.60,224,0.875,bilinear,-55.997,-45.917,+19
xcit_nano_12_p16_224.fb_dist_in1k,35.120,64.880,52.547,47.453,3.05,224,1.000,bicubic,-55.070,-45.203,+46
resnet34.a3_in1k,35.033,64.967,50.503,49.497,21.80,224,0.950,bicubic,-55.207,-47.377,+42
convit_tiny.fb_in1k,35.027,64.973,51.777,48.223,5.71,224,0.875,bicubic,-55.523,-46.413,+34
gcresnext26ts.ch_in1k,35.023,64.977,51.493,48.507,10.48,288,1.000,bicubic,-57.717,-47.117,-59
eca_resnext26ts.ch_in1k,34.930,65.070,52.373,47.627,10.30,288,1.000,bicubic,-57.820,-46.337,-61
regnety_004.tv2_in1k,34.923,65.077,51.263,48.737,4.34,224,0.965,bicubic,-56.697,-46.907,-6
tinynet_b.in1k,34.867,65.133,52.003,47.997,3.73,188,0.875,bicubic,-56.253,-46.057,+16
resnext26ts.ra2_in1k,34.847,65.153,52.710,47.290,10.30,288,1.000,bicubic,-57.533,-45.680,-38
regnety_008.pycls_in1k,34.780,65.220,51.737,48.263,6.26,224,0.875,bicubic,-57.110,-46.673,-17
pit_ti_224.in1k,34.670,65.330,52.160,47.840,4.85,224,0.900,bicubic,-55.750,-45.850,+31
tf_efficientnet_b0.in1k,34.627,65.373,51.140,48.860,5.29,224,0.875,bicubic,-57.653,-47.410,-33
crossvit_9_240.in1k,34.620,65.380,51.770,48.230,8.55,240,0.875,bicubic,-56.440,-46.530,+16
mobilenetv3_large_100.ra_in1k,34.610,65.390,52.840,47.160,5.48,224,0.875,bicubic,-56.860,-45.480,-5
mixer_b16_224.goog_in21k_ft_in1k,34.427,65.573,48.107,51.893,59.88,224,0.875,bicubic,-56.713,-49.293,+8
pvt_v2_b0.in1k,34.400,65.600,53.103,46.897,3.67,224,0.900,bicubic,-54.570,-44.587,+49
tf_efficientnet_es.in1k,34.257,65.743,51.357,48.643,5.44,224,0.875,bicubic,-57.853,-47.073,-29
fbnetc_100.rmsp_in1k,34.253,65.747,51.190,48.810,5.57,224,0.875,bilinear,-57.027,-46.900,-4
resnet18d.ra2_in1k,34.220,65.780,51.767,48.233,11.71,288,0.950,bicubic,-56.580,-46.393,+12
regnety_006.pycls_in1k,34.153,65.847,51.253,48.747,6.06,224,0.875,bicubic,-57.407,-47.177,-16
resnet18.a1_in1k,33.973,66.027,49.410,50.590,11.69,288,1.000,bicubic,-56.227,-48.350,+26
tf_mobilenetv3_large_100.in1k,33.960,66.040,51.470,48.530,5.48,224,0.875,bilinear,-57.460,-46.790,-12
regnetx_008.pycls_in1k,33.803,66.197,50.543,49.457,7.26,224,0.875,bicubic,-57.377,-47.827,-4
mnasnet_100.rmsp_in1k,33.780,66.220,51.170,48.830,4.38,224,0.875,bicubic,-57.430,-47.050,-6
lcnet_100.ra2_in1k,33.773,66.227,52.090,47.910,2.95,224,0.875,bicubic,-55.137,-45.290,+42
semnasnet_075.rmsp_in1k,33.770,66.230,52.403,47.597,2.91,224,0.875,bicubic,-56.450,-45.547,+20
ese_vovnet19b_dw.ra_in1k,33.753,66.247,50.930,49.070,6.54,288,0.950,bicubic,-59.007,-47.560,-84
regnetx_004_tv.tv2_in1k,33.710,66.290,49.803,50.197,5.50,224,0.965,bicubic,-57.450,-48.307,-7
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,33.630,66.370,50.687,49.313,6.36,384,1.000,bicubic,-58.100,-47.743,-33
mobilevit_s.cvnets_in1k,33.627,66.373,49.297,50.703,5.58,256,0.900,bicubic,-59.523,-49.483,-136
xcit_nano_12_p8_224.fb_in1k,33.580,66.420,50.220,49.780,3.05,224,1.000,bicubic,-57.520,-48.010,-6
vit_tiny_patch16_384.augreg_in21k_ft_in1k,33.543,66.457,51.083,48.917,5.79,384,1.000,bicubic,-59.877,-47.727,-164
semnasnet_100.rmsp_in1k,33.503,66.497,50.787,49.213,3.89,224,0.875,bicubic,-58.167,-47.503,-33
spnasnet_100.rmsp_in1k,33.490,66.510,51.280,48.720,4.42,224,0.875,bilinear,-57.100,-46.670,+2
mixnet_s.ft_in1k,33.473,66.527,50.997,49.003,4.13,224,0.875,bicubic,-58.297,-47.313,-40
crossvit_tiny_240.in1k,33.353,66.647,49.897,50.103,7.01,240,0.875,bicubic,-57.177,-48.053,+4
mobilevitv2_075.cvnets_in1k,33.340,66.660,50.110,49.890,2.87,256,0.888,bicubic,-58.630,-48.190,-47
vgg19_bn.tv_in1k,33.240,66.760,50.777,49.223,143.68,224,0.875,bilinear,-57.750,-47.323,-8
ghostnet_100.in1k,33.190,66.810,51.160,48.840,5.18,224,0.875,bilinear,-57.260,-46.660,+2
regnetx_006.pycls_in1k,33.147,66.853,50.243,49.757,6.20,224,0.875,bicubic,-57.643,-47.847,-8
edgenext_x_small.in1k,33.110,66.890,49.003,50.997,2.34,288,1.000,bicubic,-58.470,-49.187,-38
seresnext26t_32x4d.bt_in1k,33.087,66.913,50.270,49.730,16.81,288,0.950,bicubic,-60.263,-48.390,-164
resnet18.tv_in1k,33.057,66.943,51.173,48.827,11.69,224,0.875,bilinear,-55.093,-45.947,+29
seresnext26d_32x4d.bt_in1k,32.970,67.030,49.830,50.170,16.81,288,0.950,bicubic,-60.090,-48.880,-143
xcit_nano_12_p16_224.fb_in1k,32.953,67.047,49.977,50.023,3.05,224,1.000,bicubic,-56.017,-47.433,+21
legacy_seresnext26_32x4d.in1k,32.770,67.230,49.237,50.763,16.79,224,0.875,bicubic,-59.830,-49.173,-90
hrnet_w18_small.ms_in1k,32.653,67.347,50.590,49.410,13.19,224,0.875,bilinear,-57.217,-47.300,+2
deit_tiny_patch16_224.fb_in1k,32.653,67.347,50.260,49.740,5.72,224,0.900,bicubic,-56.957,-47.700,+8
legacy_seresnet18.in1k,32.600,67.400,50.317,49.683,11.78,224,0.875,bicubic,-56.660,-47.373,+11
mobilenetv2_100.ra_in1k,32.523,67.477,50.803,49.197,3.50,224,0.875,bicubic,-57.327,-47.027,+2
regnetx_004.pycls_in1k,32.513,67.487,49.340,50.660,5.16,224,0.875,bicubic,-56.947,-48.420,+6
resnet26d.bt_in1k,32.420,67.580,49.997,50.003,16.01,288,0.950,bicubic,-60.130,-48.653,-91
resnet18.gluon_in1k,32.413,67.587,49.740,50.260,11.69,224,0.875,bicubic,-56.247,-47.350,+15
regnety_004.pycls_in1k,32.320,67.680,49.467,50.533,4.34,224,0.875,bicubic,-58.450,-48.603,-21
resnet18.a2_in1k,32.223,67.777,47.887,52.113,11.69,288,1.000,bicubic,-57.257,-49.743,+1
tf_mixnet_s.in1k,32.190,67.810,48.530,51.470,4.13,224,0.875,bicubic,-59.490,-49.710,-57
resnet26.bt_in1k,32.173,67.827,49.443,50.557,16.00,288,0.950,bicubic,-59.947,-49.107,-71
vit_tiny_patch16_224.augreg_in21k_ft_in1k,32.020,67.980,49.027,50.973,5.72,224,0.900,bicubic,-59.900,-49.313,-66
tf_mobilenetv3_large_075.in1k,31.850,68.150,49.117,50.883,3.99,224,0.875,bilinear,-58.480,-48.753,-16
tf_mobilenetv3_large_minimal_100.in1k,31.600,68.400,49.353,50.647,3.92,224,0.875,bilinear,-57.560,-47.957,+3
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,30.790,69.210,47.663,52.337,6.34,224,0.900,bicubic,-58.550,-50.037,-2
tinynet_c.in1k,30.513,69.487,48.480,51.520,2.46,184,0.875,bicubic,-57.887,-48.780,+7
lcnet_075.ra2_in1k,30.373,69.627,48.770,51.230,2.36,224,0.875,bicubic,-56.587,-47.780,+17
vgg16_bn.tv_in1k,30.357,69.643,47.280,52.720,138.37,224,0.875,bilinear,-60.183,-50.710,-25
resnet18.a3_in1k,30.097,69.903,46.337,53.663,11.69,224,0.950,bicubic,-56.973,-50.323,+13
edgenext_xx_small.in1k,29.750,70.250,46.487,53.513,1.33,288,1.000,bicubic,-60.050,-51.013,-13
regnety_002.pycls_in1k,29.710,70.290,46.830,53.170,3.16,224,0.875,bicubic,-58.480,-50.610,+4
mobilevit_xs.cvnets_in1k,29.590,70.410,46.047,53.953,2.32,256,0.900,bicubic,-61.620,-52.013,-51
mobilenetv3_small_100.lamb_in1k,29.050,70.950,47.200,52.800,2.54,224,0.875,bicubic,-57.130,-49.250,+13
mnasnet_small.lamb_in1k,28.953,71.047,47.287,52.713,2.03,224,0.875,bicubic,-56.527,-48.713,+14
vgg13_bn.tv_in1k,28.863,71.137,46.730,53.270,133.05,224,0.875,bilinear,-60.337,-50.800,-9
resnet10t.c3_in1k,28.853,71.147,46.903,53.097,5.44,224,0.950,bicubic,-57.827,-49.827,+9
regnetx_002.pycls_in1k,28.840,71.160,45.450,54.550,2.68,224,0.875,bicubic,-58.520,-51.550,+2
mobilenetv2_050.lamb_in1k,28.660,71.340,46.593,53.407,1.97,224,0.875,bicubic,-56.330,-49.027,+13
vgg19.tv_in1k,28.580,71.420,45.167,54.833,143.67,224,0.875,bilinear,-61.100,-52.383,-21
mobilevitv2_050.cvnets_in1k,28.567,71.433,45.193,54.807,1.37,256,0.888,bicubic,-60.463,-52.397,-12
dla60x_c.in1k,28.443,71.557,46.210,53.790,1.32,224,0.875,bilinear,-58.687,-50.930,0
vgg11_bn.tv_in1k,28.430,71.570,46.460,53.540,132.87,224,0.875,bilinear,-59.970,-50.790,-8
tinynet_d.in1k,27.960,72.040,45.877,54.123,2.34,152,0.875,bicubic,-57.480,-50.143,+6
vgg16.tv_in1k,27.877,72.123,44.680,55.320,138.36,224,0.875,bilinear,-61.493,-52.840,-22
resnet14t.c3_in1k,27.557,72.443,44.697,55.303,10.08,224,0.950,bicubic,-61.693,-52.743,-20
tf_mobilenetv3_small_100.in1k,27.280,72.720,44.407,55.593,2.54,224,0.875,bilinear,-58.660,-51.993,+1
mixer_l16_224.goog_in21k_ft_in1k,26.860,73.140,37.913,62.087,208.20,224,0.875,bicubic,-60.130,-56.157,-4
mobilenetv3_small_075.lamb_in1k,26.537,73.463,43.883,56.117,2.04,224,0.875,bicubic,-57.583,-51.637,+6
vgg11.tv_in1k,26.537,73.463,43.470,56.530,132.86,224,0.875,bilinear,-60.813,-53.630,-9
mobilevit_xxs.cvnets_in1k,26.340,73.660,43.050,56.950,1.27,256,0.900,bicubic,-61.590,-54.140,-13
vgg13.tv_in1k,26.273,73.727,43.353,56.647,133.05,224,0.875,bilinear,-61.297,-53.757,-13
dla46x_c.in1k,26.220,73.780,43.770,56.230,1.07,224,0.875,bilinear,-59.210,-52.660,-2
lcnet_050.ra2_in1k,26.210,73.790,44.570,55.430,1.88,224,0.875,bicubic,-56.820,-50.430,+2
tf_mobilenetv3_small_075.in1k,26.197,73.803,43.630,56.370,2.04,224,0.875,bilinear,-58.303,-52.250,-1
dla46_c.in1k,25.513,74.487,43.783,56.217,1.30,224,0.875,bilinear,-59.187,-52.427,-3
tf_mobilenetv3_small_minimal_100.in1k,25.103,74.897,42.940,57.060,2.04,224,0.875,bilinear,-57.567,-52.080,0
tinynet_e.in1k,23.357,76.643,41.070,58.930,2.04,106,0.875,bicubic,-56.463,-52.900,0
mobilenetv3_small_050.lamb_in1k,21.740,78.260,38.753,61.247,1.59,224,0.875,bicubic,-56.350,-54.257,0
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-real.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,91.129,8.871,98.713,1.287,305.08,448,1.000,bicubic,+1.077,-0.335,0
eva_giant_patch14_336.clip_ft_in1k,91.058,8.942,98.602,1.399,"1,013.01",336,1.000,bicubic,+1.592,-0.224,+5
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,91.020,8.980,98.685,1.315,305.08,448,1.000,bicubic,+1.054,-0.327,-1
eva_giant_patch14_560.m30m_ft_in22k_in1k,90.969,9.031,98.672,1.328,"1,014.45",560,1.000,bicubic,+1.183,-0.320,-1
eva02_large_patch14_448.mim_in22k_ft_in1k,90.922,9.078,98.683,1.317,305.08,448,1.000,bicubic,+1.298,-0.267,-1
eva_giant_patch14_336.m30m_ft_in22k_in1k,90.907,9.093,98.661,1.339,"1,013.01",336,1.000,bicubic,+1.341,-0.291,0
eva_large_patch14_336.in22k_ft_in1k,90.903,9.098,98.785,1.215,304.53,336,1.000,bicubic,+2.233,+0.063,+6
eva_giant_patch14_224.clip_ft_in1k,90.900,9.100,98.680,1.319,"1,012.56",224,0.900,bicubic,+2.020,+0.000,+1
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,90.896,9.104,98.802,1.198,87.12,448,1.000,bicubic,+2.204,+0.078,+2
eva02_large_patch14_448.mim_m38m_ft_in1k,90.888,9.113,98.653,1.347,305.08,448,1.000,bicubic,+1.318,-0.269,-5
eva_large_patch14_336.in22k_ft_in22k_in1k,90.862,9.138,98.715,1.285,304.53,336,1.000,bicubic,+1.654,-0.139,-3
eva02_base_patch14_448.mim_in22k_ft_in1k,90.800,9.200,98.742,1.258,87.12,448,1.000,bicubic,+2.550,+0.178,+14
caformer_b36.sail_in22k_ft_in1k_384,90.781,9.219,98.860,1.140,98.75,384,1.000,bicubic,+2.723,+0.278,+21
beit_large_patch16_512.in22k_ft_in22k_in1k,90.687,9.313,98.753,1.247,305.67,512,1.000,bicubic,+2.091,+0.097,+1
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,90.678,9.322,98.813,1.187,200.13,384,1.000,bicubic,+2.372,+0.231,+8
regnety_1280.swag_ft_in1k,90.659,9.341,98.819,1.181,644.81,384,1.000,bicubic,+2.429,+0.133,+12
convnext_xxlarge.clip_laion2b_soup_ft_in1k,90.642,9.358,98.806,1.194,846.47,256,1.000,bicubic,+2.038,+0.099,-3
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,90.633,9.367,98.755,1.245,200.13,384,1.000,bicubic,+2.785,+0.309,+24
volo_d5_512.sail_in1k,90.614,9.386,98.698,1.302,296.09,512,1.150,bicubic,+3.556,+0.728,+48
beit_large_patch16_384.in22k_ft_in22k_in1k,90.606,9.394,98.766,1.234,305.00,384,1.000,bicubic,+2.204,+0.158,-1
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,90.584,9.416,98.617,1.383,116.14,384,1.000,bicubic,+2.756,+0.245,+22
volo_d5_448.sail_in1k,90.580,9.420,98.685,1.315,295.91,448,1.150,bicubic,+3.628,+0.747,+52
tf_efficientnet_l2.ns_jft_in1k,90.561,9.439,98.775,1.226,480.31,800,0.960,bicubic,+2.209,+0.127,-2
maxvit_base_tf_512.in21k_ft_in1k,90.559,9.441,98.702,1.298,119.88,512,1.000,bicubic,+2.341,+0.174,+6
eva_large_patch14_196.in22k_ft_in22k_in1k,90.555,9.445,98.698,1.302,304.14,196,1.000,bicubic,+1.979,+0.040,-8
vit_large_patch14_clip_336.openai_ft_in12k_in1k,90.544,9.456,98.687,1.313,304.53,336,1.000,bicubic,+2.276,+0.161,-2
convnextv2_huge.fcmae_ft_in22k_in1k_512,90.542,9.458,98.710,1.290,660.29,512,1.000,bicubic,+1.684,-0.038,-17
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,90.540,9.460,98.806,1.194,200.13,320,1.000,bicubic,+2.582,+0.331,+8
tf_efficientnet_l2.ns_jft_in1k_475,90.537,9.463,98.710,1.290,480.31,475,0.936,bicubic,+2.303,+0.164,-2
eva_large_patch14_196.in22k_ft_in1k,90.533,9.467,98.772,1.228,304.14,196,1.000,bicubic,+2.599,+0.274,+7
volo_d4_448.sail_in1k,90.512,9.488,98.591,1.409,193.41,448,1.150,bicubic,+3.720,+0.707,+52
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,90.501,9.499,98.631,1.369,632.46,336,1.000,bicubic,+1.909,-0.031,-16
maxvit_xlarge_tf_512.in21k_ft_in1k,90.499,9.501,98.576,1.424,475.77,512,1.000,bicubic,+1.961,-0.068,-15
convnextv2_huge.fcmae_ft_in22k_in1k_384,90.497,9.503,98.695,1.304,660.29,384,1.000,bicubic,+1.827,-0.043,-22
convnext_xlarge.fb_in22k_ft_in1k_384,90.495,9.505,98.768,1.232,350.20,384,1.000,bicubic,+2.743,+0.212,+9
caformer_m36.sail_in22k_ft_in1k_384,90.458,9.542,98.668,1.332,56.20,384,1.000,bicubic,+3.008,+0.360,+18
convformer_b36.sail_in22k_ft_in1k_384,90.448,9.552,98.772,1.228,99.88,384,1.000,bicubic,+2.846,+0.338,+10
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,90.444,9.556,98.749,1.251,116.09,384,1.000,bicubic,+2.980,+0.375,+14
caformer_b36.sail_in22k_ft_in1k,90.431,9.569,98.764,1.236,98.75,224,1.000,bicubic,+3.009,+0.438,+16
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,90.428,9.572,98.640,1.360,304.53,336,1.000,bicubic,+2.251,+0.070,-8
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,90.418,9.582,98.644,1.356,632.05,224,1.000,bicubic,+2.162,+0.092,-16
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,90.407,9.593,98.734,1.266,87.92,384,1.000,bicubic,+3.311,+0.500,+23
vit_large_patch14_clip_224.openai_ft_in12k_in1k,90.379,9.621,98.659,1.341,304.20,224,1.000,bicubic,+2.203,+0.115,-11
maxvit_xlarge_tf_384.in21k_ft_in1k,90.379,9.621,98.582,1.418,475.32,384,1.000,bicubic,+2.065,+0.038,-21
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,90.377,9.623,98.648,1.351,73.88,384,1.000,bicubic,+2.995,+0.337,+12
beit_base_patch16_384.in22k_ft_in22k_in1k,90.367,9.633,98.725,1.275,86.74,384,1.000,bicubic,+3.567,+0.589,+35
convnextv2_large.fcmae_ft_in22k_in1k_384,90.367,9.633,98.663,1.337,197.96,384,1.000,bicubic,+2.169,+0.135,-16
maxvit_large_tf_512.in21k_ft_in1k,90.362,9.638,98.642,1.358,212.33,512,1.000,bicubic,+2.136,+0.044,-19
maxvit_base_tf_384.in21k_ft_in1k,90.360,9.640,98.683,1.317,119.65,384,1.000,bicubic,+2.438,+0.139,-12
convnextv2_base.fcmae_ft_in22k_in1k_384,90.360,9.640,98.670,1.330,88.72,384,1.000,bicubic,+2.716,+0.254,-3
beitv2_large_patch16_224.in1k_ft_in22k_in1k,90.354,9.646,98.587,1.413,304.43,224,0.950,bicubic,+1.960,-0.011,-31
vit_large_patch14_clip_336.laion2b_ft_in1k,90.332,9.668,98.597,1.403,304.53,336,1.000,bicubic,+2.476,+0.229,-12
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,90.330,9.670,98.770,1.230,88.59,384,1.000,bicubic,+3.196,+0.548,+10
maxvit_large_tf_384.in21k_ft_in1k,90.315,9.685,98.687,1.313,212.03,384,1.000,bicubic,+2.329,+0.119,-19
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,90.311,9.689,98.668,1.332,304.20,224,1.000,bicubic,+2.419,+0.258,-16
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,90.309,9.691,98.651,1.349,200.13,256,1.000,bicubic,+2.975,+0.433,+2
vit_large_patch14_clip_224.openai_ft_in1k,90.309,9.691,98.640,1.360,304.20,224,1.000,bicubic,+2.457,+0.214,-16
caformer_s36.sail_in22k_ft_in1k_384,90.305,9.695,98.794,1.206,39.30,384,1.000,bicubic,+3.449,+0.582,+19
convnext_base.fb_in22k_ft_in1k_384,90.283,9.717,98.800,1.200,88.59,384,1.000,bicubic,+3.487,+0.536,+23
convnext_large.fb_in22k_ft_in1k_384,90.279,9.721,98.659,1.341,197.77,384,1.000,bicubic,+2.807,+0.273,-9
deit3_large_patch16_384.fb_in22k_ft_in1k,90.247,9.753,98.625,1.375,304.76,384,1.000,bicubic,+2.527,+0.113,-16
dm_nfnet_f6.dm_in1k,90.241,9.759,98.625,1.375,438.36,576,0.956,bicubic,+3.879,+0.729,+43
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,90.226,9.774,98.766,1.234,93.59,320,1.000,bicubic,+3.502,+0.590,+22
deit3_huge_patch14_224.fb_in22k_ft_in1k,90.226,9.774,98.640,1.360,632.13,224,1.000,bicubic,+3.040,+0.380,-1
regnety_320.swag_ft_in1k,90.219,9.781,98.764,1.236,145.05,384,1.000,bicubic,+3.383,+0.402,+14
vit_base_patch16_clip_384.laion2b_ft_in1k,90.213,9.787,98.704,1.296,86.86,384,1.000,bicubic,+3.593,+0.696,+22
vit_large_patch16_384.augreg_in21k_ft_in1k,90.213,9.787,98.661,1.339,304.72,384,1.000,bicubic,+3.129,+0.359,-1
convformer_m36.sail_in22k_ft_in1k_384,90.204,9.796,98.651,1.349,57.05,384,1.000,bicubic,+3.312,+0.535,+8
convnextv2_huge.fcmae_ft_in1k,90.204,9.796,98.548,1.452,660.29,288,1.000,bicubic,+3.624,+0.576,+22
tf_efficientnetv2_l.in21k_ft_in1k,90.202,9.798,98.719,1.281,118.52,480,1.000,bicubic,+3.400,+0.583,+10
vit_base_patch16_clip_384.openai_ft_in12k_in1k,90.200,9.800,98.651,1.349,86.86,384,0.950,bicubic,+3.176,+0.471,-2
convformer_b36.sail_in22k_ft_in1k,90.189,9.811,98.695,1.304,99.88,224,1.000,bicubic,+3.191,+0.523,-3
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,90.189,9.811,98.585,1.415,86.86,384,1.000,bicubic,+2.981,+0.550,-13
cait_m48_448.fb_dist_in1k,90.189,9.811,98.484,1.516,356.46,448,1.000,bicubic,+3.697,+0.732,+25
vit_huge_patch14_clip_224.laion2b_ft_in1k,90.181,9.819,98.544,1.456,632.05,224,1.000,bicubic,+2.593,+0.326,-27
volo_d3_448.sail_in1k,90.174,9.826,98.550,1.450,86.63,448,1.000,bicubic,+3.674,+0.840,+20
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,90.157,9.843,98.614,1.386,196.74,384,1.000,bicubic,+2.693,+0.364,-24
beit_large_patch16_224.in22k_ft_in22k_in1k,90.149,9.851,98.725,1.275,304.43,224,0.900,bicubic,+2.671,+0.421,-28
beitv2_large_patch16_224.in1k_ft_in1k,90.100,9.900,98.439,1.561,304.43,224,0.950,bicubic,+2.688,+0.205,-23
tf_efficientnet_b7.ns_jft_in1k,90.098,9.902,98.617,1.383,66.35,600,0.949,bicubic,+3.258,+0.523,-2
vit_large_patch14_clip_224.laion2b_ft_in1k,90.098,9.902,98.557,1.443,304.20,224,1.000,bicubic,+2.816,+0.311,-21
convnextv2_base.fcmae_ft_in22k_in1k,90.068,9.932,98.676,1.324,88.72,288,1.000,bicubic,+3.070,+0.508,-11
convnext_xlarge.fb_in22k_ft_in1k,90.066,9.934,98.619,1.381,350.20,288,1.000,bicubic,+2.736,+0.291,-24
caformer_b36.sail_in1k_384,90.063,9.937,98.514,1.486,98.75,384,1.000,bicubic,+3.655,+0.700,+18
cait_m36_384.fb_dist_in1k,90.051,9.949,98.495,1.505,271.22,384,1.000,bicubic,+3.991,+0.765,+39
convnextv2_large.fcmae_ft_in22k_in1k,90.034,9.966,98.629,1.371,197.96,288,1.000,bicubic,+2.550,+0.273,-37
seresnextaa101d_32x8d.sw_in12k_ft_in1k,90.029,9.971,98.685,1.315,93.59,288,1.000,bicubic,+3.545,+0.655,+11
tf_efficientnetv2_m.in21k_ft_in1k,90.023,9.977,98.666,1.334,54.14,480,1.000,bicubic,+4.023,+0.720,+38
convformer_s36.sail_in22k_ft_in1k_384,90.023,9.977,98.619,1.381,40.01,384,1.000,bicubic,+3.645,+0.635,+15
swin_large_patch4_window12_384.ms_in22k_ft_in1k,90.019,9.981,98.661,1.339,196.74,384,1.000,bicubic,+2.887,+0.427,-26
deit3_large_patch16_224.fb_in22k_ft_in1k,90.008,9.992,98.661,1.339,304.37,224,1.000,bicubic,+3.026,+0.425,-19
convnext_base.clip_laiona_augreg_ft_in1k_384,90.001,9.998,98.548,1.452,88.59,384,1.000,bicubic,+3.499,+0.580,+3
swin_base_patch4_window12_384.ms_in22k_ft_in1k,89.997,10.003,98.700,1.300,87.90,384,1.000,bicubic,+3.559,+0.634,+8
vit_base_patch16_384.augreg_in21k_ft_in1k,89.989,10.011,98.678,1.322,86.86,384,1.000,bicubic,+3.993,+0.676,+34
maxvit_base_tf_512.in1k,89.974,10.026,98.435,1.565,119.88,512,1.000,bicubic,+3.372,+0.517,-6
caformer_m36.sail_in1k_384,89.933,10.067,98.454,1.546,56.20,384,1.000,bicubic,+3.767,+0.634,+20
convnextv2_large.fcmae_ft_in1k,89.931,10.069,98.559,1.441,197.96,288,1.000,bicubic,+3.813,+0.737,+22
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,89.927,10.073,98.414,1.586,116.14,224,0.950,bicubic,+3.033,+0.400,-23
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,89.925,10.075,98.505,1.494,196.74,256,0.900,bicubic,+2.973,+0.400,-26
regnety_160.swag_ft_in1k,89.918,10.082,98.646,1.354,83.59,384,1.000,bicubic,+3.908,+0.592,+26
convnext_small.fb_in22k_ft_in1k_384,89.916,10.084,98.685,1.315,50.22,384,1.000,bicubic,+4.138,+0.795,+41
efficientnet_b5.sw_in12k_ft_in1k,89.910,10.090,98.567,1.433,30.39,448,1.000,bicubic,+4.018,+0.831,+32
deit3_base_patch16_384.fb_in22k_ft_in1k,89.891,10.110,98.602,1.399,86.88,384,1.000,bicubic,+3.153,+0.487,-18
xcit_large_24_p8_384.fb_dist_in1k,89.886,10.114,98.384,1.616,188.93,384,1.000,bicubic,+3.890,+0.694,+25
volo_d5_224.sail_in1k,89.880,10.120,98.490,1.510,295.46,224,0.960,bicubic,+3.810,+0.915,+18
convnext_large.fb_in22k_ft_in1k,89.876,10.124,98.593,1.407,197.77,288,1.000,bicubic,+2.850,+0.389,-38
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,89.867,10.133,98.644,1.356,87.92,256,0.900,bicubic,+3.599,+0.762,+2
convnext_base.fb_in22k_ft_in1k,89.858,10.142,98.691,1.309,88.59,288,1.000,bicubic,+3.584,+0.599,0
caformer_m36.sail_in22k_ft_in1k,89.854,10.146,98.585,1.415,56.20,224,1.000,bicubic,+3.260,+0.561,-19
cait_s36_384.fb_dist_in1k,89.835,10.165,98.427,1.573,68.37,384,1.000,bicubic,+4.381,+0.949,+52
coatnet_2_rw_224.sw_in12k_ft_in1k,89.831,10.169,98.527,1.473,73.87,224,0.950,bicubic,+3.263,+0.633,-19
convformer_m36.sail_in22k_ft_in1k,89.818,10.182,98.548,1.452,57.05,224,1.000,bicubic,+3.672,+0.698,+6
xcit_medium_24_p8_384.fb_dist_in1k,89.816,10.184,98.365,1.635,84.32,384,1.000,bicubic,+4.000,+0.773,+25
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,89.811,10.188,98.668,1.332,88.59,256,1.000,bicubic,+3.441,+0.684,-10
volo_d4_224.sail_in1k,89.811,10.188,98.427,1.573,192.96,224,0.960,bicubic,+3.939,+0.955,+20
maxvit_large_tf_512.in1k,89.799,10.201,98.330,1.670,212.33,512,1.000,bicubic,+3.273,+0.450,-23
vit_large_r50_s32_384.augreg_in21k_ft_in1k,89.792,10.208,98.520,1.480,329.09,384,1.000,bicubic,+3.610,+0.598,-3
volo_d2_384.sail_in1k,89.788,10.212,98.403,1.597,58.87,384,1.000,bicubic,+3.746,+0.829,+7
swin_large_patch4_window7_224.ms_in22k_ft_in1k,89.786,10.214,98.640,1.360,196.53,224,0.900,bicubic,+3.474,+0.738,-13
tf_efficientnet_b6.ns_jft_in1k,89.782,10.218,98.508,1.492,43.04,528,0.942,bicubic,+3.326,+0.624,-20
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,89.775,10.225,98.469,1.531,73.88,224,0.950,bicubic,+3.271,+0.575,-27
tf_efficientnetv2_xl.in21k_ft_in1k,89.771,10.229,98.288,1.712,208.12,512,1.000,bicubic,+3.023,+0.274,-38
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,89.752,10.248,98.484,1.516,116.09,224,0.950,bicubic,+3.110,+0.464,-36
beitv2_base_patch16_224.in1k_ft_in22k_in1k,89.743,10.257,98.585,1.415,86.53,224,0.900,bicubic,+3.269,+0.532,-25
dm_nfnet_f5.dm_in1k,89.741,10.259,98.441,1.559,377.21,544,0.954,bicubic,+3.643,+0.753,-5
dm_nfnet_f4.dm_in1k,89.737,10.263,98.409,1.591,316.07,512,0.951,bicubic,+3.901,+0.745,+10
xcit_small_24_p8_384.fb_dist_in1k,89.735,10.265,98.420,1.580,47.63,384,1.000,bicubic,+4.181,+0.850,+28
regnety_160.lion_in12k_ft_in1k,89.724,10.276,98.608,1.392,83.59,288,1.000,bicubic,+3.738,+0.772,+2
caformer_s18.sail_in22k_ft_in1k_384,89.720,10.280,98.580,1.420,26.34,384,1.000,bicubic,+4.306,+0.878,+36
vit_base_patch8_224.augreg2_in21k_ft_in1k,89.718,10.283,98.510,1.490,86.58,224,0.900,bicubic,+3.499,+0.678,-20
convformer_m36.sail_in1k_384,89.718,10.283,98.435,1.565,57.05,384,1.000,bicubic,+4.138,+0.893,+24
vit_base_patch16_clip_384.openai_ft_in1k,89.707,10.293,98.508,1.492,86.86,384,1.000,bicubic,+3.503,+0.634,-20
caformer_s36.sail_in22k_ft_in1k,89.701,10.300,98.638,1.362,39.30,224,1.000,bicubic,+3.909,+0.814,+8
volo_d1_384.sail_in1k,89.698,10.302,98.290,1.710,26.78,384,1.000,bicubic,+4.454,+1.096,+45
deit3_large_patch16_384.fb_in1k,89.688,10.312,98.390,1.610,304.76,384,1.000,bicubic,+3.876,+0.792,+4
caformer_s36.sail_in1k_384,89.679,10.321,98.324,1.676,39.30,384,1.000,bicubic,+3.939,+0.652,+10
xcit_large_24_p16_384.fb_dist_in1k,89.662,10.338,98.403,1.597,189.10,384,1.000,bicubic,+3.908,+0.865,+7
convformer_b36.sail_in1k_384,89.658,10.342,98.379,1.621,99.88,384,1.000,bicubic,+3.916,+0.855,+7
tf_efficientnet_b5.ns_jft_in1k,89.651,10.349,98.482,1.518,30.39,456,0.934,bicubic,+3.563,+0.728,-18
dm_nfnet_f3.dm_in1k,89.647,10.353,98.463,1.537,254.92,416,0.940,bicubic,+3.961,+0.893,+9
convnext_base.clip_laion2b_augreg_ft_in1k,89.645,10.355,98.463,1.537,88.59,256,1.000,bicubic,+3.487,+0.783,-24
regnety_2560.seer_ft_in1k,89.632,10.368,98.399,1.601,"1,282.60",384,1.000,bicubic,+4.480,+0.963,+47
convnext_small.in12k_ft_in1k_384,89.598,10.402,98.480,1.520,50.22,384,1.000,bicubic,+3.416,+0.558,-30
maxvit_base_tf_384.in1k,89.589,10.411,98.320,1.680,119.65,384,1.000,bicubic,+3.287,+0.522,-37
tf_efficientnet_b8.ap_in1k,89.579,10.421,98.305,1.695,87.41,672,0.954,bicubic,+4.215,+1.013,+28
regnety_160.sw_in12k_ft_in1k,89.570,10.430,98.546,1.454,83.59,288,1.000,bicubic,+3.584,+0.712,-15
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,89.564,10.436,98.418,1.582,86.57,224,0.950,bicubic,+3.390,+0.662,-32
maxvit_tiny_tf_512.in1k,89.562,10.438,98.335,1.665,31.05,512,1.000,bicubic,+3.898,+0.751,+2
convformer_s18.sail_in22k_ft_in1k_384,89.553,10.447,98.531,1.469,26.77,384,1.000,bicubic,+4.555,+0.961,+55
volo_d3_224.sail_in1k,89.553,10.447,98.373,1.627,86.33,224,0.960,bicubic,+4.139,+1.097,+16
maxvit_large_tf_384.in1k,89.553,10.447,98.183,1.817,212.03,384,1.000,bicubic,+3.325,+0.493,-40
tf_efficientnetv2_l.in1k,89.540,10.460,98.339,1.661,118.52,480,1.000,bicubic,+3.876,+0.865,-1
flexivit_large.1200ep_in1k,89.534,10.466,98.414,1.586,304.36,240,0.950,bicubic,+3.888,+0.872,-1
xcit_large_24_p8_224.fb_dist_in1k,89.517,10.483,98.217,1.783,188.93,224,1.000,bicubic,+4.115,+0.815,+14
flexivit_large.600ep_in1k,89.508,10.492,98.392,1.608,304.36,240,0.950,bicubic,+3.970,+0.904,+2
xcit_small_12_p8_384.fb_dist_in1k,89.508,10.492,98.307,1.693,26.21,384,1.000,bicubic,+4.430,+1.025,+41
cait_s24_384.fb_dist_in1k,89.504,10.496,98.362,1.638,47.06,384,1.000,bicubic,+4.456,+1.016,+43
convformer_s36.sail_in22k_ft_in1k,89.496,10.505,98.454,1.546,40.01,224,1.000,bicubic,+4.082,+0.886,+8
convnextv2_tiny.fcmae_ft_in22k_in1k_384,89.485,10.515,98.484,1.516,28.64,384,1.000,bicubic,+4.379,+0.856,+33
convformer_s36.sail_in1k_384,89.481,10.520,98.369,1.631,40.01,384,1.000,bicubic,+4.103,+0.893,+10
xcit_medium_24_p16_384.fb_dist_in1k,89.481,10.520,98.296,1.704,84.40,384,1.000,bicubic,+4.059,+0.890,+3
convnext_tiny.in12k_ft_in1k_384,89.472,10.528,98.505,1.494,28.59,384,1.000,bicubic,+4.350,+0.900,+28
deit3_base_patch16_224.fb_in22k_ft_in1k,89.446,10.554,98.557,1.443,86.59,224,1.000,bicubic,+3.746,+0.811,-15
vit_base_patch16_224.augreg2_in21k_ft_in1k,89.444,10.556,98.441,1.559,86.57,224,0.900,bicubic,+4.348,+0.911,+31
maxvit_small_tf_512.in1k,89.442,10.558,98.356,1.644,69.13,512,1.000,bicubic,+3.360,+0.592,-43
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,89.440,10.560,98.403,1.597,88.34,448,1.000,bicubic,+3.662,+0.765,-23
regnety_120.sw_in12k_ft_in1k,89.436,10.564,98.537,1.462,51.82,288,1.000,bicubic,+4.036,+0.956,+2
vit_base_patch8_224.augreg_in21k_ft_in1k,89.436,10.564,98.484,1.516,86.58,224,0.900,bicubic,+3.638,+0.694,-28
deit_base_distilled_patch16_384.fb_in1k,89.434,10.566,98.439,1.561,87.63,384,1.000,bicubic,+4.008,+1.109,-6
tf_efficientnet_b7.ap_in1k,89.429,10.571,98.345,1.655,66.35,600,0.949,bicubic,+4.307,+1.093,+21
vit_base_patch16_clip_224.laion2b_ft_in1k,89.427,10.573,98.469,1.531,86.57,224,1.000,bicubic,+3.961,+0.895,-10
convnextv2_base.fcmae_ft_in1k,89.421,10.579,98.360,1.640,88.72,288,1.000,bicubic,+3.947,+0.976,-12
caformer_b36.sail_in1k,89.410,10.590,98.222,1.778,98.75,224,1.000,bicubic,+3.904,+0.912,-14
vit_base_patch16_clip_224.openai_ft_in12k_in1k,89.401,10.598,98.394,1.606,86.57,224,0.950,bicubic,+3.462,+0.666,-42
hrnet_w48_ssld.paddle_in1k,89.401,10.598,98.379,1.621,77.47,288,1.000,bilinear,+4.924,+1.145,+68
beit_base_patch16_224.in22k_ft_in22k_in1k,89.395,10.605,98.529,1.471,86.53,224,0.900,bicubic,+4.183,+0.871,+8
regnetz_e8.ra3_in1k,89.378,10.622,98.459,1.542,57.70,320,1.000,bicubic,+4.342,+1.186,+24
deit3_small_patch16_384.fb_in22k_ft_in1k,89.363,10.637,98.382,1.618,22.21,384,1.000,bicubic,+4.539,+0.894,+38
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,89.348,10.652,98.490,1.510,39.03,384,0.950,bicubic,+3.816,+0.852,-23
tf_efficientnetv2_m.in1k,89.348,10.652,98.326,1.674,54.14,480,1.000,bicubic,+4.144,+0.962,+5
tf_efficientnet_b8.ra_in1k,89.348,10.652,98.305,1.695,87.41,672,0.954,bicubic,+3.980,+0.911,-8
tf_efficientnet_b6.ap_in1k,89.344,10.656,98.283,1.717,43.04,528,0.942,bicubic,+4.558,+1.147,+37
volo_d2_224.sail_in1k,89.335,10.665,98.213,1.787,58.68,224,0.960,bicubic,+4.133,+1.023,+3
caformer_s18.sail_in1k_384,89.331,10.669,98.294,1.706,26.34,384,1.000,bicubic,+4.305,+0.936,+18
eva02_small_patch14_336.mim_in22k_ft_in1k,89.327,10.673,98.379,1.621,22.13,336,1.000,bicubic,+3.611,+0.747,-38
vit_large_patch16_224.augreg_in21k_ft_in1k,89.318,10.682,98.394,1.606,304.33,224,0.900,bicubic,+3.484,+0.576,-49
flexivit_large.300ep_in1k,89.305,10.694,98.324,1.676,304.36,240,0.950,bicubic,+4.015,+0.924,-11
tf_efficientnet_b4.ns_jft_in1k,89.301,10.699,98.343,1.657,19.34,380,0.922,bicubic,+4.139,+0.873,0
convnext_small.fb_in22k_ft_in1k,89.299,10.701,98.358,1.642,50.22,288,1.000,bicubic,+4.037,+0.676,-11
xcit_small_24_p16_384.fb_dist_in1k,89.295,10.705,98.326,1.674,47.67,384,1.000,bicubic,+4.205,+1.014,+6
xcit_medium_24_p8_224.fb_dist_in1k,89.293,10.707,98.185,1.815,84.32,224,1.000,bicubic,+4.219,+0.911,+7
beitv2_base_patch16_224.in1k_ft_in1k,89.271,10.729,98.262,1.738,86.53,224,0.900,bicubic,+3.677,+0.756,-39
deit3_huge_patch14_224.fb_in1k,89.218,10.782,98.164,1.836,632.13,224,0.900,bicubic,+3.994,+0.804,-11
coat_lite_medium_384.in1k,89.209,10.791,98.219,1.781,44.57,384,1.000,bicubic,+4.331,+0.847,+18
xcit_small_24_p8_224.fb_dist_in1k,89.199,10.801,98.243,1.757,47.63,224,1.000,bicubic,+4.331,+1.053,+18
xcit_small_12_p16_384.fb_dist_in1k,89.197,10.803,98.217,1.783,26.25,384,1.000,bicubic,+4.485,+1.099,+26
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,89.194,10.806,98.356,1.644,88.30,384,1.000,bicubic,+3.828,+0.700,-25
dm_nfnet_f2.dm_in1k,89.192,10.808,98.228,1.772,193.78,352,0.920,bicubic,+3.998,+0.882,-11
swin_base_patch4_window7_224.ms_in22k_ft_in1k,89.173,10.827,98.433,1.567,87.77,224,0.900,bicubic,+3.901,+0.869,-22
vit_base_patch16_clip_224.openai_ft_in1k,89.167,10.833,98.269,1.732,86.57,224,0.900,bicubic,+3.875,+0.831,-25
eca_nfnet_l2.ra3_in1k,89.147,10.852,98.315,1.685,56.72,384,1.000,bicubic,+4.445,+1.133,+21
cait_xs24_384.fb_dist_in1k,89.147,10.852,98.292,1.708,26.67,384,1.000,bicubic,+5.085,+1.408,+83
convformer_s18.sail_in1k_384,89.124,10.876,98.127,1.873,26.77,384,1.000,bicubic,+4.722,+1.016,+55
maxvit_tiny_tf_384.in1k,89.109,10.891,98.211,1.789,30.98,384,1.000,bicubic,+4.009,+0.833,-12
maxvit_small_tf_384.in1k,89.109,10.891,98.164,1.836,69.02,384,1.000,bicubic,+3.569,+0.702,-48
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,89.107,10.893,98.189,1.810,468.53,224,0.875,bilinear,+4.009,+0.751,-12
tf_efficientnet_b7.ra_in1k,89.083,10.917,98.185,1.815,66.35,600,0.949,bicubic,+4.151,+0.977,+1
ecaresnet269d.ra2_in1k,89.073,10.927,98.234,1.766,102.09,352,1.000,bicubic,+4.107,+1.012,-2
regnety_1280.seer_ft_in1k,89.064,10.936,98.157,1.843,644.81,384,1.000,bicubic,+4.632,+1.065,+41
vit_base_patch32_clip_384.openai_ft_in12k_in1k,89.051,10.949,98.279,1.721,88.30,384,0.950,bicubic,+3.835,+0.873,-27
xcit_large_24_p16_224.fb_dist_in1k,89.045,10.955,98.059,1.941,189.10,224,1.000,bicubic,+4.129,+0.929,-1
convnext_small.in12k_ft_in1k,89.024,10.976,98.243,1.757,50.22,288,1.000,bicubic,+3.694,+0.697,-38
dm_nfnet_f1.dm_in1k,89.019,10.981,98.254,1.746,132.63,320,0.910,bicubic,+4.317,+0.987,+11
resmlp_big_24_224.fb_in22k_ft_in1k,89.019,10.981,98.215,1.785,129.14,224,0.875,bicubic,+4.621,+1.103,+45
xcit_small_12_p8_224.fb_dist_in1k,89.011,10.989,98.076,1.924,26.21,224,1.000,bicubic,+4.775,+1.204,+56
convnext_large.fb_in1k,88.994,11.006,98.040,1.960,197.77,288,1.000,bicubic,+4.148,+0.826,-1
caformer_m36.sail_in1k,88.990,11.011,98.016,1.984,56.20,224,1.000,bicubic,+3.758,+0.816,-36
efficientnetv2_rw_m.agc_in1k,88.987,11.013,98.217,1.783,53.24,416,1.000,bicubic,+4.175,+1.067,0
regnety_1280.swag_lc_in1k,88.951,11.049,98.230,1.770,644.81,224,0.965,bicubic,+2.969,+0.380,-87
regnetz_d8_evos.ch_in1k,88.951,11.049,98.181,1.819,23.46,320,1.000,bicubic,+4.825,+1.169,+59
regnetz_040_h.ra3_in1k,88.949,11.051,98.209,1.791,28.94,320,1.000,bicubic,+4.457,+1.451,+19
edgenext_base.in21k_ft_in1k,88.942,11.057,98.279,1.721,18.51,320,1.000,bicubic,+4.888,+1.083,+65
tf_efficientnet_b5.ap_in1k,88.940,11.060,98.164,1.836,30.39,456,0.934,bicubic,+4.682,+1.190,+44
mvitv2_large.fb_in1k,88.936,11.064,97.965,2.035,217.99,224,0.900,bicubic,+3.692,+0.751,-44
caformer_s18.sail_in22k_ft_in1k,88.932,11.068,98.311,1.689,26.34,224,1.000,bicubic,+4.856,+1.113,+58
deit3_base_patch16_384.fb_in1k,88.923,11.077,98.042,1.958,86.88,384,1.000,bicubic,+3.851,+0.790,-27
deit3_medium_patch16_224.fb_in22k_ft_in1k,88.921,11.079,98.294,1.706,38.85,224,1.000,bicubic,+4.369,+1.106,+4
volo_d1_224.sail_in1k,88.915,11.085,98.027,1.973,26.63,224,0.960,bicubic,+4.753,+1.251,+50
convnext_tiny.in12k_ft_in1k,88.906,11.094,98.309,1.691,28.59,288,1.000,bicubic,+4.456,+0.969,+15
tf_efficientnetv2_s.in21k_ft_in1k,88.898,11.102,98.277,1.723,21.46,384,1.000,bicubic,+4.610,+1.025,+34
vit_base_patch16_224.augreg_in21k_ft_in1k,88.870,11.130,98.230,1.770,86.57,224,0.900,bicubic,+4.338,+0.938,+3
convnext_tiny.fb_in22k_ft_in1k_384,88.855,11.145,98.296,1.704,28.59,384,1.000,bicubic,+4.767,+1.152,+50
regnetz_d8.ra3_in1k,88.853,11.147,98.189,1.810,23.37,320,1.000,bicubic,+4.799,+1.194,+55
convformer_b36.sail_in1k,88.851,11.149,97.878,2.122,99.88,224,1.000,bicubic,+4.033,+0.932,-17
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,88.842,11.158,97.869,2.131,41.72,224,0.950,bicubic,+3.932,+0.911,-24
resnetrs420.tf_in1k,88.838,11.162,98.031,1.968,191.89,416,1.000,bicubic,+3.832,+0.907,-33
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,88.834,11.166,98.051,1.949,194.03,224,0.875,bilinear,+4.668,+0.853,+40
resnetrs270.tf_in1k,88.829,11.171,98.136,1.864,129.86,352,1.000,bicubic,+4.401,+1.168,+14
vit_small_r26_s32_384.augreg_in21k_ft_in1k,88.817,11.184,98.343,1.657,36.47,384,1.000,bicubic,+4.769,+1.015,+49
swin_small_patch4_window7_224.ms_in22k_ft_in1k,88.817,11.184,98.328,1.672,49.61,224,0.900,bicubic,+5.519,+1.364,+122
vit_base_r50_s16_384.orig_in21k_ft_in1k,88.806,11.194,98.232,1.768,98.95,384,1.000,bicubic,+3.830,+0.942,-36
xcit_medium_24_p16_224.fb_dist_in1k,88.806,11.194,98.038,1.962,84.40,224,1.000,bicubic,+4.520,+1.106,+23
tf_efficientnet_b7.aa_in1k,88.804,11.196,98.057,1.943,66.35,600,0.949,bicubic,+4.390,+1.151,+13
maxxvit_rmlp_small_rw_256.sw_in1k,88.795,11.205,98.064,1.937,66.01,256,0.950,bicubic,+4.171,+0.996,-17
seresnet152d.ra2_in1k,88.787,11.213,98.172,1.828,66.84,320,1.000,bicubic,+4.427,+1.132,+15
xcit_tiny_24_p8_384.fb_dist_in1k,88.787,11.213,98.155,1.845,12.11,384,1.000,bicubic,+5.041,+1.445,+71
convformer_m36.sail_in1k,88.787,11.213,97.767,2.233,57.05,224,1.000,bicubic,+4.293,+0.901,-6
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,88.782,11.218,98.151,1.849,88.79,224,0.875,bilinear,+4.480,+0.975,+15
resnetrs200.tf_in1k,88.763,11.237,98.115,1.885,93.21,320,1.000,bicubic,+4.319,+1.033,-3
convnext_base.fb_in1k,88.763,11.237,97.920,2.080,88.59,288,1.000,bicubic,+4.335,+0.952,+2
deit3_large_patch16_224.fb_in1k,88.763,11.237,97.920,2.080,304.37,224,0.900,bicubic,+3.987,+0.882,-30
tf_efficientnet_b6.aa_in1k,88.759,11.241,98.066,1.934,43.04,528,0.942,bicubic,+4.643,+1.186,+29
resnetrs350.tf_in1k,88.757,11.243,98.031,1.968,163.96,384,1.000,bicubic,+4.043,+1.039,-32
rexnetr_300.sw_in12k_ft_in1k,88.746,11.254,98.339,1.661,34.81,288,1.000,bicubic,+4.202,+1.083,-22
caformer_s36.sail_in1k,88.746,11.254,98.023,1.977,39.30,224,1.000,bicubic,+4.238,+1.027,-16
convnextv2_tiny.fcmae_ft_in22k_in1k,88.744,11.256,98.194,1.806,28.64,288,1.000,bicubic,+4.328,+0.934,-1
vit_base_patch16_224_miil.in21k_ft_in1k,88.740,11.260,98.025,1.975,86.54,224,0.875,bilinear,+4.474,+1.221,+9
edgenext_base.usi_in1k,88.735,11.265,98.147,1.853,18.51,320,1.000,bicubic,+4.777,+1.377,+36
regnetz_040.ra3_in1k,88.731,11.269,98.091,1.909,27.12,320,1.000,bicubic,+4.491,+1.159,+11
convformer_s18.sail_in22k_ft_in1k,88.727,11.273,98.194,1.806,26.77,224,1.000,bicubic,+4.989,+1.146,+60
resnetv2_152x2_bit.goog_in21k_ft_in1k,88.725,11.275,98.311,1.689,236.34,448,1.000,bilinear,+4.215,+1.319,-25
regnety_160.deit_in1k,88.703,11.297,98.068,1.932,83.59,288,1.000,bicubic,+5.013,+1.286,+64
davit_base.msft_in1k,88.701,11.299,97.874,2.127,87.95,224,0.950,bicubic,+4.059,+0.854,-37
regnety_640.seer_ft_in1k,88.674,11.326,98.166,1.834,281.38,384,1.000,bicubic,+4.766,+1.244,+33
vit_small_patch16_384.augreg_in21k_ft_in1k,88.652,11.348,98.230,1.770,22.20,384,1.000,bicubic,+4.850,+1.132,+45
regnetz_d32.ra3_in1k,88.652,11.348,98.078,1.922,27.58,320,0.950,bicubic,+4.630,+1.210,+25
flexivit_base.1200ep_in1k,88.648,11.352,97.935,2.065,86.59,240,0.950,bicubic,+3.970,+0.939,-42
mvitv2_base.fb_in1k,88.644,11.356,97.826,2.174,51.47,224,0.900,bicubic,+4.194,+0.968,-23
regnety_080.ra3_in1k,88.635,11.365,97.965,2.035,39.18,288,1.000,bicubic,+4.709,+1.075,+26
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,88.633,11.367,98.189,1.810,38.86,256,0.950,bicubic,+4.187,+1.346,-24
davit_small.msft_in1k,88.631,11.369,97.953,2.047,49.75,224,0.950,bicubic,+4.379,+1.013,-3
eca_nfnet_l1.ra2_in1k,88.624,11.376,98.132,1.868,41.41,320,1.000,bicubic,+4.614,+1.106,+20
swinv2_base_window16_256.ms_in1k,88.586,11.414,97.908,2.092,87.92,256,0.900,bicubic,+3.986,+0.818,-44
maxvit_base_tf_224.in1k,88.584,11.416,97.848,2.152,119.47,224,0.950,bicubic,+3.724,+0.860,-60
regnety_320.seer_ft_in1k,88.573,11.427,98.106,1.894,145.05,384,1.000,bicubic,+5.239,+1.400,+83
resnetaa101d.sw_in12k_ft_in1k,88.573,11.427,98.076,1.924,44.57,288,1.000,bicubic,+4.449,+0.970,+4
resnetv2_152x4_bit.goog_in21k_ft_in1k,88.554,11.446,98.192,1.808,936.53,480,1.000,bilinear,+3.638,+0.754,-69
resnet200d.ra2_in1k,88.554,11.446,97.961,2.039,64.69,320,1.000,bicubic,+4.590,+1.137,+16
xcit_small_24_p16_224.fb_dist_in1k,88.545,11.455,98.004,1.996,47.67,224,1.000,bicubic,+4.671,+1.268,+21
flexivit_base.600ep_in1k,88.545,11.455,97.933,2.067,86.59,240,0.950,bicubic,+4.023,+0.997,-44
seresnextaa101d_32x8d.ah_in1k,88.537,11.463,98.002,1.998,93.59,288,1.000,bicubic,+3.971,+0.926,-51
maxvit_rmlp_small_rw_224.sw_in1k,88.522,11.478,97.773,2.227,64.90,224,0.900,bicubic,+4.030,+0.763,-41
resnest269e.in1k,88.520,11.480,98.027,1.973,110.93,416,0.928,bicubic,+4.010,+0.593,-46
coatnet_rmlp_2_rw_224.sw_in1k,88.513,11.487,97.566,2.434,73.88,224,0.950,bicubic,+3.907,+0.826,-56
efficientformerv2_l.snap_dist_in1k,88.507,11.493,97.961,2.039,26.32,224,0.950,bicubic,+4.875,+1.401,+46
gcvit_base.in1k,88.501,11.499,97.769,2.231,90.32,224,0.875,bicubic,+4.055,+0.561,-39
swinv2_base_window8_256.ms_in1k,88.498,11.502,97.891,2.109,87.92,256,0.900,bicubic,+4.248,+0.967,-18
seresnext101_32x8d.ah_in1k,88.494,11.506,97.884,2.116,93.57,288,1.000,bicubic,+4.308,+1.010,-12
convformer_s36.sail_in1k,88.486,11.514,97.763,2.237,40.01,224,1.000,bicubic,+4.426,+1.017,-4
crossvit_18_dagger_408.in1k,88.479,11.521,97.895,2.105,44.61,408,1.000,bicubic,+4.281,+1.079,-15
flexivit_base.300ep_in1k,88.479,11.521,97.846,2.154,86.59,240,0.950,bicubic,+4.073,+0.960,-34
efficientnetv2_rw_s.ra2_in1k,88.475,11.525,97.978,2.022,23.94,384,1.000,bicubic,+4.667,+1.246,+18
resnetv2_101x3_bit.goog_in21k_ft_in1k,88.466,11.534,98.157,1.843,387.93,448,1.000,bilinear,+4.028,+0.775,-44
maxvit_large_tf_224.in1k,88.460,11.540,97.809,2.191,211.79,224,0.950,bicubic,+3.518,+0.839,-87
maxvit_small_tf_224.in1k,88.454,11.546,97.880,2.120,68.93,224,0.950,bicubic,+4.028,+1.056,-42
resnetv2_50x3_bit.goog_in21k_ft_in1k,88.447,11.553,98.200,1.800,217.32,448,1.000,bilinear,+4.427,+1.074,-5
cait_s24_224.fb_dist_in1k,88.443,11.557,97.961,2.039,46.92,224,1.000,bicubic,+5.001,+1.387,+47
resmlp_big_24_224.fb_distilled_in1k,88.441,11.559,97.938,2.062,129.14,224,0.875,bicubic,+4.849,+1.287,+37
regnetv_064.ra3_in1k,88.439,11.561,98.061,1.939,30.58,288,1.000,bicubic,+4.723,+1.317,+23
resnest200e.in1k,88.434,11.566,98.040,1.960,70.20,320,0.909,bicubic,+4.588,+1.156,+3
vit_large_r50_s32_224.augreg_in21k_ft_in1k,88.430,11.570,98.081,1.919,328.99,224,0.900,bicubic,+4.008,+0.911,-47
tf_efficientnet_b3.ns_jft_in1k,88.428,11.572,98.031,1.968,12.23,300,0.904,bicubic,+4.372,+1.113,-17
seresnext101d_32x8d.ah_in1k,88.428,11.572,97.961,2.039,93.59,288,1.000,bicubic,+4.070,+1.041,-40
swin_base_patch4_window12_384.ms_in1k,88.426,11.574,97.803,2.197,87.90,384,1.000,bicubic,+3.950,+0.911,-61
tf_efficientnetv2_s.in1k,88.409,11.591,97.927,2.073,21.46,384,1.000,bicubic,+4.509,+1.229,-7
convnext_small.fb_in1k,88.407,11.593,98.012,1.988,50.22,288,1.000,bicubic,+4.707,+1.204,+19
tf_efficientnet_b5.aa_in1k,88.407,11.593,97.931,2.069,30.39,456,0.934,bicubic,+4.719,+1.219,+20
regnety_320.swag_lc_in1k,88.398,11.602,98.117,1.883,145.05,224,0.965,bicubic,+3.852,+0.675,-76
vit_base_patch16_384.orig_in21k_ft_in1k,88.389,11.611,98.160,1.840,86.86,384,1.000,bicubic,+4.189,+0.940,-35
regnetz_c16_evos.ch_in1k,88.381,11.619,98.040,1.960,13.49,320,0.950,bicubic,+5.749,+1.566,+112
tresnet_v2_l.miil_in21k_ft_in1k,88.379,11.621,97.923,2.077,46.17,224,0.875,bilinear,+4.479,+1.429,-12
swinv2_small_window16_256.ms_in1k,88.372,11.628,97.848,2.152,49.73,256,0.900,bicubic,+4.148,+1.070,-40
efficientnet_b4.ra2_in1k,88.366,11.634,97.961,2.039,19.34,384,1.000,bicubic,+4.952,+1.363,+34
resnet152d.ra2_in1k,88.362,11.638,97.931,2.069,60.21,320,1.000,bicubic,+4.678,+1.191,+14
maxvit_rmlp_tiny_rw_256.sw_in1k,88.355,11.645,97.820,2.180,29.15,256,0.950,bicubic,+4.131,+0.952,-42
tf_efficientnet_b4.ap_in1k,88.347,11.653,97.888,2.112,19.34,380,0.922,bicubic,+5.097,+1.494,+47
deit3_small_patch16_224.fb_in22k_ft_in1k,88.336,11.664,98.127,1.873,22.06,224,1.000,bicubic,+5.262,+1.352,+64
regnety_064.ra3_in1k,88.323,11.677,97.865,2.135,30.58,288,1.000,bicubic,+4.603,+1.143,+3
convnextv2_nano.fcmae_ft_in22k_in1k_384,88.315,11.685,97.938,2.062,15.62,384,1.000,bicubic,+4.941,+1.194,+35
crossvit_15_dagger_408.in1k,88.313,11.687,97.874,2.127,28.50,408,1.000,bicubic,+4.471,+1.096,-16
tf_efficientnet_b5.ra_in1k,88.308,11.692,97.918,2.082,30.39,456,0.934,bicubic,+4.498,+1.164,-12
cs3se_edgenet_x.c2ns_in1k,88.296,11.704,97.935,2.065,50.72,320,1.000,bicubic,+4.754,+1.263,+14
deit3_small_patch16_384.fb_in1k,88.296,11.704,97.888,2.112,22.21,384,1.000,bicubic,+4.866,+1.214,+22
pvt_v2_b4.in1k,88.285,11.715,97.816,2.184,62.56,224,0.900,bicubic,+4.575,+1.142,-1
efficientformer_l7.snap_dist_in1k,88.278,11.722,97.882,2.118,82.23,224,0.950,bicubic,+4.896,+1.346,+28
mvitv2_small.fb_in1k,88.264,11.736,97.692,2.308,34.87,224,0.900,bicubic,+4.494,+1.116,-12
resnetrs152.tf_in1k,88.249,11.751,97.737,2.263,86.62,320,1.000,bicubic,+4.547,+1.125,-3
deit3_base_patch16_224.fb_in1k,88.244,11.756,97.816,2.184,86.59,224,0.900,bicubic,+4.458,+1.228,-16
xcit_small_12_p16_224.fb_dist_in1k,88.240,11.760,97.844,2.156,26.25,224,1.000,bicubic,+4.912,+1.428,+31
gcvit_small.in1k,88.217,11.783,97.788,2.212,51.09,224,0.875,bicubic,+4.325,+1.130,-30
regnetv_040.ra3_in1k,88.212,11.788,97.972,2.028,20.64,288,1.000,bicubic,+5.014,+1.314,+34
deit_base_distilled_patch16_224.fb_in1k,88.212,11.788,97.910,2.090,87.34,224,0.900,bicubic,+4.826,+1.422,+20
xception65p.ra3_in1k,88.189,11.811,97.788,2.212,39.82,299,0.940,bicubic,+5.053,+1.680,+39
swinv2_small_window8_256.ms_in1k,88.172,11.828,97.777,2.223,49.73,256,0.900,bicubic,+4.318,+1.133,-32
caformer_s18.sail_in1k,88.163,11.837,97.728,2.272,26.34,224,1.000,bicubic,+4.507,+1.212,-5
xcit_tiny_24_p16_384.fb_dist_in1k,88.161,11.839,97.942,2.058,12.12,384,1.000,bicubic,+5.591,+1.666,+100
xcit_large_24_p8_224.fb_in1k,88.161,11.839,97.391,2.609,188.93,224,1.000,bicubic,+3.767,+0.727,-76
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,88.153,11.848,98.051,1.949,236.34,384,1.000,bicubic,+4.319,+0.925,-32
coat_lite_medium.in1k,88.144,11.856,97.899,2.101,44.57,224,0.900,bicubic,+4.544,+1.171,-6
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,88.144,11.856,97.859,2.142,88.79,224,0.875,bilinear,+5.446,+1.715,+73
cait_xxs36_384.fb_dist_in1k,88.140,11.860,97.903,2.097,17.37,384,1.000,bicubic,+5.936,+1.759,+141
dm_nfnet_f0.dm_in1k,88.131,11.869,97.906,2.095,71.49,256,0.900,bicubic,+4.645,+1.338,-1
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,88.112,11.888,97.965,2.035,44.18,224,0.875,bilinear,+4.880,+1.207,+21
pit_b_distilled_224.in1k,88.108,11.892,97.654,2.346,74.79,224,0.900,bicubic,+4.342,+1.188,-29
xcit_tiny_12_p8_384.fb_dist_in1k,88.103,11.897,97.925,2.075,6.71,384,1.000,bicubic,+5.715,+1.705,+109
pvt_v2_b5.in1k,88.103,11.897,97.694,2.306,81.96,224,0.900,bicubic,+4.361,+1.060,-26
pvt_v2_b3.in1k,88.099,11.901,97.775,2.225,45.24,224,0.900,bicubic,+4.979,+1.221,+30
rexnetr_200.sw_in12k_ft_in1k,88.097,11.903,98.006,1.994,16.52,288,1.000,bicubic,+4.961,+1.370,+23
xception65.ra3_in1k,88.069,11.931,97.750,2.250,39.92,299,0.940,bicubic,+4.887,+1.158,+18
hrnet_w18_ssld.paddle_in1k,88.067,11.933,97.824,2.176,21.30,288,1.000,bilinear,+6.021,+1.574,+148
swin_s3_base_224.ms_in1k,88.046,11.954,97.662,2.338,71.13,224,0.900,bicubic,+4.126,+0.990,-55
xcit_tiny_24_p8_224.fb_dist_in1k,88.037,11.963,97.818,2.182,12.11,224,1.000,bicubic,+5.471,+1.760,+86
convnextv2_tiny.fcmae_ft_in1k,88.035,11.965,97.859,2.142,28.64,288,1.000,bicubic,+4.571,+1.141,-11
resnet152.a1h_in1k,88.033,11.967,97.696,2.304,60.19,288,1.000,bicubic,+4.583,+1.158,-12
focalnet_base_srf.ms_in1k,88.033,11.967,97.656,2.344,88.15,224,0.900,bicubic,+4.215,+0.974,-46
regnety_160.swag_lc_in1k,88.031,11.969,98.044,1.956,83.59,224,0.965,bicubic,+4.249,+0.764,-43
maxvit_tiny_tf_224.in1k,88.027,11.973,97.816,2.184,30.92,224,0.950,bicubic,+4.625,+1.226,-9
focalnet_base_lrf.ms_in1k,88.003,11.997,97.609,2.391,88.75,224,0.900,bicubic,+4.165,+1.001,-53
gcvit_tiny.in1k,88.001,11.999,97.722,2.278,28.22,224,0.875,bicubic,+4.617,+1.324,-7
eca_nfnet_l0.ra2_in1k,87.980,12.020,97.869,2.131,24.14,288,1.000,bicubic,+5.402,+1.375,+75
tf_efficientnet_b5.in1k,87.973,12.027,97.933,2.067,30.39,456,0.934,bicubic,+4.797,+1.397,+6
cs3sedarknet_x.c2ns_in1k,87.973,12.027,97.794,2.205,35.40,288,1.000,bicubic,+5.315,+1.445,+57
nfnet_l0.ra2_in1k,87.969,12.031,97.867,2.133,35.07,288,1.000,bicubic,+5.217,+1.351,+45
efficientformer_l3.snap_dist_in1k,87.963,12.037,97.709,2.291,31.41,224,0.950,bicubic,+5.413,+1.461,+76
xcit_small_24_p8_224.fb_in1k,87.956,12.044,97.581,2.419,47.63,224,1.000,bicubic,+4.122,+0.949,-58
tf_efficientnet_b4.aa_in1k,87.954,12.046,97.739,2.261,19.34,380,0.922,bicubic,+4.936,+1.439,+20
regnetz_c16.ra3_in1k,87.952,12.048,97.782,2.218,13.46,320,1.000,bicubic,+5.318,+1.460,+54
regnety_032.ra_in1k,87.945,12.055,97.897,2.103,19.44,288,1.000,bicubic,+5.217,+1.481,+42
coatnet_1_rw_224.sw_in1k,87.943,12.057,97.453,2.547,41.72,224,0.950,bicubic,+4.347,+1.071,-35
resnet101d.ra2_in1k,87.935,12.065,97.910,2.090,44.57,320,1.000,bicubic,+4.919,+1.458,+17
regnety_040.ra3_in1k,87.933,12.067,97.880,2.120,20.65,288,1.000,bicubic,+4.889,+1.382,+12
mobilevitv2_200.cvnets_in22k_ft_in1k_384,87.933,12.067,97.822,2.178,18.45,384,1.000,bicubic,+4.533,+1.240,-22
swinv2_cr_small_ns_224.sw_in1k,87.933,12.067,97.668,2.332,49.70,224,0.900,bicubic,+4.435,+1.184,-34
focalnet_small_lrf.ms_in1k,87.930,12.069,97.696,2.304,50.34,224,0.900,bicubic,+4.436,+1.116,-34
resnetv2_101.a1h_in1k,87.924,12.076,97.651,2.349,44.54,288,1.000,bicubic,+4.922,+1.197,+13
vit_base_patch32_384.augreg_in21k_ft_in1k,87.909,12.091,98.010,1.990,88.30,384,1.000,bicubic,+4.559,+1.172,-20
sequencer2d_l.in1k,87.907,12.093,97.703,2.297,54.30,224,0.875,bicubic,+4.513,+1.207,-27
twins_svt_large.in1k,87.903,12.097,97.581,2.419,99.27,224,0.900,bicubic,+4.225,+0.991,-49
coatnet_rmlp_1_rw_224.sw_in1k,87.892,12.108,97.624,2.376,41.69,224,0.950,bicubic,+4.530,+1.174,-24
swin_base_patch4_window7_224.ms_in1k,87.866,12.134,97.564,2.436,87.77,224,0.900,bicubic,+4.260,+1.112,-48
twins_pcpvt_large.in1k,87.864,12.136,97.859,2.142,60.99,224,0.900,bicubic,+4.734,+1.257,-7
maxvit_tiny_rw_224.sw_in1k,87.856,12.144,97.643,2.357,29.06,224,0.950,bicubic,+4.352,+1.139,-44
swin_s3_small_224.ms_in1k,87.849,12.151,97.431,2.568,49.74,224,0.900,bicubic,+4.093,+0.980,-67
convformer_s18.sail_in1k,87.845,12.155,97.551,2.449,26.77,224,1.000,bicubic,+4.859,+1.301,+6
mobilevitv2_175.cvnets_in22k_ft_in1k_384,87.841,12.159,97.728,2.272,14.25,384,1.000,bicubic,+4.903,+1.302,+7
deit_base_patch16_384.fb_in1k,87.841,12.159,97.506,2.494,86.86,384,1.000,bicubic,+4.735,+1.138,-6
ecaresnet101d.miil_in1k,87.839,12.161,97.899,2.101,44.57,288,0.950,bicubic,+4.855,+1.357,+4
convnext_nano.in12k_ft_in1k,87.837,12.164,97.888,2.112,15.59,288,1.000,bicubic,+4.975,+1.332,+10
xcit_small_12_p8_224.fb_in1k,87.822,12.178,97.568,2.432,26.21,224,1.000,bicubic,+4.490,+1.084,-30
flexivit_small.1200ep_in1k,87.813,12.187,97.615,2.385,22.06,240,0.950,bicubic,+5.293,+1.491,+52
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,87.809,12.191,97.756,2.244,88.22,224,0.900,bicubic,+4.513,+1.228,-30
focalnet_small_srf.ms_in1k,87.809,12.191,97.575,2.425,49.89,224,0.900,bicubic,+4.393,+1.137,-45
maxxvit_rmlp_nano_rw_256.sw_in1k,87.807,12.193,97.752,2.248,16.78,256,0.950,bicubic,+4.765,+1.402,-9
flexivit_small.600ep_in1k,87.807,12.193,97.579,2.421,22.06,240,0.950,bicubic,+5.445,+1.497,+65
tf_efficientnetv2_b3.in21k_ft_in1k,87.805,12.195,97.893,2.107,14.36,300,0.900,bicubic,+5.135,+1.267,+21
deit3_medium_patch16_224.fb_in1k,87.802,12.198,97.656,2.344,38.85,224,0.900,bicubic,+4.716,+1.360,-16
tresnet_xl.miil_in1k_448,87.798,12.202,97.459,2.541,78.44,448,0.875,bilinear,+4.738,+1.285,-14
resnetv2_50x1_bit.goog_distilled_in1k,87.792,12.208,97.903,2.097,25.55,224,0.875,bicubic,+4.972,+1.389,+2
convnext_tiny.fb_in1k,87.770,12.230,97.585,2.415,28.59,288,1.000,bicubic,+5.072,+0.953,+15
regnety_320.tv2_in1k,87.743,12.257,97.673,2.327,145.05,224,0.965,bicubic,+4.581,+1.257,-31
resnext101_64x4d.tv_in1k,87.740,12.259,97.592,2.408,83.46,224,0.875,bilinear,+4.748,+1.348,-12
convnextv2_nano.fcmae_ft_in22k_in1k,87.732,12.268,97.886,2.114,15.62,288,1.000,bicubic,+5.068,+1.366,+15
twins_pcpvt_base.in1k,87.730,12.270,97.728,2.272,43.83,224,0.900,bicubic,+5.020,+1.382,+8
tresnet_m.miil_in21k_ft_in1k,87.723,12.277,97.517,2.483,31.39,224,0.875,bilinear,+4.649,+1.403,-22
gc_efficientnetv2_rw_t.agc_in1k,87.719,12.281,97.803,2.197,13.68,288,1.000,bicubic,+5.267,+1.511,+44
mvitv2_tiny.fb_in1k,87.719,12.281,97.553,2.447,24.17,224,0.900,bicubic,+5.309,+1.401,+45
maxvit_rmlp_nano_rw_256.sw_in1k,87.715,12.285,97.577,2.423,15.50,256,0.950,bicubic,+4.761,+1.311,-15
resnetv2_101x1_bit.goog_in21k_ft_in1k,87.683,12.317,97.940,2.060,44.54,448,1.000,bilinear,+5.341,+1.420,+54
rexnet_300.nav_in1k,87.683,12.317,97.611,2.389,34.71,224,0.875,bicubic,+4.909,+1.375,-3
swin_small_patch4_window7_224.ms_in1k,87.662,12.338,97.568,2.432,49.61,224,0.900,bicubic,+4.454,+1.252,-45
efficientnetv2_rw_t.ra2_in1k,87.651,12.349,97.683,2.317,13.65,288,1.000,bicubic,+5.301,+1.492,+50
twins_svt_base.in1k,87.651,12.349,97.525,2.474,56.07,224,0.900,bicubic,+4.529,+1.109,-36
mobilevitv2_150.cvnets_in22k_ft_in1k_384,87.649,12.351,97.647,2.353,10.59,384,1.000,bicubic,+5.063,+1.333,+18
coat_small.in1k,87.647,12.354,97.530,2.470,21.69,224,0.900,bicubic,+5.285,+1.322,+43
maxxvitv2_nano_rw_256.sw_in1k,87.638,12.362,97.525,2.474,23.70,256,0.950,bicubic,+4.528,+1.201,-37
pnasnet5large.tf_in1k,87.636,12.364,97.487,2.513,86.06,331,0.911,bicubic,+4.854,+1.447,-12
resnet101.a1h_in1k,87.634,12.366,97.558,2.442,44.55,288,1.000,bicubic,+4.856,+1.248,-12
resnetaa50d.sw_in12k_ft_in1k,87.623,12.377,97.803,2.197,25.58,288,1.000,bicubic,+5.023,+1.305,+8
flexivit_small.300ep_in1k,87.623,12.377,97.611,2.389,22.06,240,0.950,bicubic,+5.445,+1.575,+67
cs3edgenet_x.c2_in1k,87.621,12.379,97.654,2.346,47.82,288,1.000,bicubic,+4.913,+1.284,-8
xcit_medium_24_p8_224.fb_in1k,87.612,12.388,97.201,2.799,84.32,224,1.000,bicubic,+3.864,+0.801,-104
swinv2_tiny_window16_256.ms_in1k,87.610,12.390,97.560,2.440,28.35,256,0.900,bicubic,+4.806,+1.324,-19
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,87.606,12.394,97.816,2.184,194.03,224,0.875,bilinear,+4.270,+0.970,-66
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,87.600,12.400,97.649,2.351,25.03,224,0.875,bilinear,+5.425,+1.427,+62
nest_base_jx.goog_in1k,87.597,12.403,97.521,2.479,67.72,224,0.875,bicubic,+4.063,+1.147,-88
maxvit_nano_rw_256.sw_in1k,87.595,12.405,97.519,2.481,15.45,256,0.950,bicubic,+4.667,+1.299,-32
tf_efficientnet_b4.in1k,87.582,12.418,97.602,2.398,19.34,380,0.922,bicubic,+4.974,+1.850,-2
davit_tiny.msft_in1k,87.565,12.435,97.585,2.415,28.36,224,0.950,bicubic,+4.869,+1.311,-13
levit_384.fb_dist_in1k,87.563,12.437,97.538,2.462,39.13,224,0.900,bicubic,+4.967,+1.520,-1
levit_conv_384.fb_dist_in1k,87.561,12.439,97.538,2.462,39.13,224,0.900,bicubic,+4.971,+1.290,0
sequencer2d_m.in1k,87.559,12.441,97.581,2.419,38.31,224,0.875,bicubic,+4.747,+1.307,-30
convnextv2_nano.fcmae_ft_in1k,87.553,12.447,97.666,2.334,15.62,288,1.000,bicubic,+5.067,+1.440,+14
tf_efficientnet_b2.ns_jft_in1k,87.553,12.447,97.626,2.374,9.11,260,0.890,bicubic,+5.175,+1.372,+23
ecaresnet50t.ra2_in1k,87.544,12.456,97.649,2.351,25.57,320,0.950,bicubic,+5.192,+1.509,+26
regnetx_320.tv2_in1k,87.538,12.462,97.564,2.436,107.81,224,0.965,bicubic,+4.728,+1.356,-33
vit_base_patch32_clip_224.laion2b_ft_in1k,87.529,12.471,97.547,2.453,88.22,224,0.900,bicubic,+4.947,+1.347,-3
efficientformerv2_s2.snap_dist_in1k,87.514,12.486,97.615,2.385,12.71,224,0.950,bicubic,+5.348,+1.705,+50
coatnet_bn_0_rw_224.sw_in1k,87.508,12.492,97.551,2.449,27.44,224,0.950,bicubic,+5.108,+1.365,+14
vit_base_patch16_rpn_224.sw_in1k,87.504,12.496,97.491,2.509,86.54,224,0.900,bicubic,+5.297,+1.493,+42
pvt_v2_b2_li.in1k,87.501,12.499,97.470,2.530,22.55,224,0.900,bicubic,+5.307,+1.378,+44
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,87.497,12.503,97.818,2.182,236.34,224,0.875,bicubic,+4.621,+1.236,-44
edgenext_small.usi_in1k,87.493,12.507,97.587,2.413,5.59,320,1.000,bicubic,+5.929,+1.875,+94
nest_small_jx.goog_in1k,87.493,12.507,97.513,2.487,38.35,224,0.875,bicubic,+4.369,+1.193,-68
vit_relpos_base_patch16_clsgap_224.sw_in1k,87.471,12.529,97.525,2.474,86.43,224,0.900,bicubic,+4.711,+1.353,-37
vit_relpos_base_patch16_224.sw_in1k,87.467,12.533,97.558,2.442,86.43,224,0.900,bicubic,+4.971,+1.419,0
regnety_080_tv.tv2_in1k,87.465,12.535,97.636,2.364,39.38,224,0.965,bicubic,+4.875,+1.618,-17
fbnetv3_g.ra2_in1k,87.446,12.554,97.545,2.455,16.62,288,0.950,bilinear,+5.410,+1.487,+52
resnet61q.ra2_in1k,87.439,12.561,97.598,2.402,36.85,288,1.000,bicubic,+4.915,+1.468,-7
wide_resnet50_2.racm_in1k,87.437,12.563,97.543,2.458,68.88,288,0.950,bicubic,+5.157,+1.479,+24
poolformerv2_m48.sail_in1k,87.435,12.565,97.419,2.581,73.35,224,1.000,bicubic,+4.815,+1.347,-26
efficientnet_b3.ra2_in1k,87.433,12.567,97.681,2.319,12.23,320,1.000,bicubic,+5.191,+1.563,+24
regnetx_160.tv2_in1k,87.433,12.567,97.444,2.556,54.28,224,0.965,bicubic,+4.867,+1.272,-13
resnext101_64x4d.c1_in1k,87.433,12.567,97.444,2.556,83.46,288,1.000,bicubic,+4.277,+1.070,-83
cait_xxs24_384.fb_dist_in1k,87.414,12.586,97.621,2.378,12.03,384,1.000,bicubic,+6.442,+1.981,+143
resnet51q.ra2_in1k,87.397,12.603,97.583,2.417,35.70,288,1.000,bilinear,+5.037,+1.401,+4
cs3darknet_x.c2ns_in1k,87.395,12.605,97.611,2.389,35.05,288,1.000,bicubic,+5.173,+1.381,+22
cs3sedarknet_l.c2ns_in1k,87.395,12.605,97.568,2.432,21.91,288,0.950,bicubic,+5.611,+1.610,+64
coat_lite_small.in1k,87.388,12.612,97.374,2.626,19.84,224,0.900,bicubic,+5.074,+1.526,+9
tresnet_l.miil_in1k_448,87.382,12.618,97.485,2.515,55.99,448,0.875,bilinear,+5.106,+1.507,+15
resnetv2_50d_gn.ah_in1k,87.380,12.620,97.536,2.464,25.57,288,1.000,bicubic,+5.422,+1.608,+47
pvt_v2_b2.in1k,87.377,12.623,97.519,2.481,25.36,224,0.900,bicubic,+5.291,+1.563,+33
sequencer2d_s.in1k,87.377,12.623,97.387,2.613,27.65,224,0.875,bicubic,+5.037,+1.357,+1
swinv2_cr_small_224.sw_in1k,87.377,12.623,97.346,2.654,49.70,224,0.900,bicubic,+4.242,+0.862,-90
xcit_tiny_24_p8_224.fb_in1k,87.373,12.627,97.626,2.374,12.11,224,1.000,bicubic,+5.481,+1.656,+48
vit_relpos_medium_patch16_cls_224.sw_in1k,87.363,12.637,97.451,2.549,38.76,224,0.900,bicubic,+4.791,+1.383,-29
nasnetalarge.tf_in1k,87.360,12.640,97.417,2.583,88.75,331,0.911,bicubic,+4.734,+1.375,-43
crossvit_18_dagger_240.in1k,87.352,12.648,97.453,2.547,44.27,240,0.875,bicubic,+4.836,+1.387,-24
seresnext50_32x4d.racm_in1k,87.350,12.650,97.613,2.387,27.56,288,0.950,bicubic,+5.154,+1.465,+15
wide_resnet101_2.tv2_in1k,87.326,12.674,97.404,2.596,126.89,224,0.965,bilinear,+4.828,+1.386,-25
ecaresnet50d.miil_in1k,87.322,12.678,97.666,2.334,25.58,288,0.950,bicubic,+5.672,+1.784,+58
resnext101_32x8d.tv2_in1k,87.318,12.682,97.560,2.440,88.79,224,0.965,bilinear,+4.486,+1.328,-72
crossvit_18_240.in1k,87.316,12.684,97.487,2.513,43.27,240,0.875,bicubic,+4.920,+1.427,-19
focalnet_tiny_srf.ms_in1k,87.301,12.699,97.414,2.586,28.43,224,0.900,bicubic,+5.167,+1.446,+17
resnest101e.in1k,87.290,12.710,97.562,2.438,48.28,256,0.875,bilinear,+4.406,+1.240,-78
gcvit_xtiny.in1k,87.281,12.719,97.481,2.519,19.98,224,0.875,bicubic,+5.327,+1.513,+33
coatnet_rmlp_nano_rw_224.sw_in1k,87.277,12.723,97.447,2.554,15.15,224,0.900,bicubic,+5.227,+1.567,+21
ecaresnet101d_pruned.miil_in1k,87.273,12.727,97.713,2.287,24.88,288,0.950,bicubic,+5.275,+1.553,+25
vit_relpos_medium_patch16_rpn_224.sw_in1k,87.269,12.731,97.447,2.554,38.73,224,0.900,bicubic,+4.959,+1.475,-10
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,87.249,12.751,97.782,2.218,28.29,224,0.900,bicubic,+6.281,+1.768,+120
resnetrs101.tf_in1k,87.241,12.759,97.455,2.545,63.62,288,0.940,bicubic,+4.957,+1.441,-8
coatnext_nano_rw_224.sw_in1k,87.239,12.761,97.543,2.458,14.70,224,0.900,bicubic,+5.297,+1.627,+28
poolformer_m48.sail_in1k,87.237,12.763,97.318,2.682,73.47,224,0.950,bicubic,+4.755,+1.352,-35
regnety_160.tv2_in1k,87.234,12.765,97.461,2.539,83.59,224,0.965,bicubic,+4.593,+1.245,-64
tresnet_xl.miil_in1k,87.232,12.768,97.397,2.603,78.44,224,0.875,bilinear,+5.158,+1.471,+11
mixer_b16_224.miil_in21k_ft_in1k,87.230,12.770,97.414,2.586,59.88,224,0.875,bilinear,+4.924,+1.694,-16
xcit_tiny_12_p8_224.fb_dist_in1k,87.224,12.776,97.449,2.551,6.71,224,1.000,bicubic,+6.012,+1.847,+89
xcit_tiny_12_p16_384.fb_dist_in1k,87.209,12.791,97.466,2.534,6.72,384,1.000,bicubic,+6.271,+2.052,+114
convit_base.fb_in1k,87.209,12.791,97.284,2.716,86.54,224,0.875,bicubic,+4.919,+1.348,-17
resnetv2_50d_evos.ah_in1k,87.205,12.795,97.421,2.579,25.59,288,1.000,bicubic,+5.205,+1.521,+12
tf_efficientnet_b3.ap_in1k,87.190,12.810,97.382,2.618,12.23,300,0.904,bicubic,+5.364,+1.756,+26
resnet152.tv2_in1k,87.188,12.812,97.387,2.613,60.19,224,0.965,bilinear,+4.900,+1.383,-20
visformer_small.in1k,87.183,12.817,97.323,2.677,40.22,224,0.900,bicubic,+5.081,+1.625,0
vit_base_patch32_clip_224.openai_ft_in1k,87.179,12.821,97.466,2.534,88.22,224,0.900,bicubic,+5.249,+1.500,+18
vit_srelpos_medium_patch16_224.sw_in1k,87.177,12.823,97.310,2.690,38.74,224,0.900,bicubic,+4.937,+1.368,-18
focalnet_tiny_lrf.ms_in1k,87.175,12.825,97.370,2.630,28.65,224,0.900,bicubic,+5.021,+1.422,-7
crossvit_15_dagger_240.in1k,87.164,12.836,97.434,2.566,28.21,240,0.875,bicubic,+4.834,+1.474,-31
convnext_tiny_hnf.a2h_in1k,87.147,12.853,97.280,2.720,28.59,288,1.000,bicubic,+4.563,+1.272,-65
vit_relpos_medium_patch16_224.sw_in1k,87.145,12.855,97.500,2.500,38.75,224,0.900,bicubic,+4.683,+1.418,-50
swin_s3_tiny_224.ms_in1k,87.143,12.857,97.308,2.692,28.33,224,0.900,bicubic,+4.999,+1.354,-10
coatnet_0_rw_224.sw_in1k,87.128,12.872,97.233,2.767,27.44,224,0.950,bicubic,+4.738,+1.397,-46
xcit_small_24_p16_224.fb_in1k,87.119,12.881,97.267,2.733,47.67,224,1.000,bicubic,+4.539,+1.255,-67
swinv2_tiny_window8_256.ms_in1k,87.089,12.911,97.510,2.490,28.35,256,0.900,bicubic,+5.269,+1.516,+15
pit_s_distilled_224.in1k,87.081,12.919,97.365,2.635,24.04,224,0.900,bicubic,+5.273,+1.637,+14
ecaresnet50t.a1_in1k,87.081,12.919,97.122,2.878,25.57,288,1.000,bicubic,+4.953,+1.480,-12
xception41p.ra3_in1k,87.059,12.941,97.207,2.793,26.91,299,0.940,bicubic,+5.087,+1.411,-1
mobilevitv2_200.cvnets_in22k_ft_in1k,87.057,12.943,97.431,2.568,18.45,256,0.888,bicubic,+4.725,+1.490,-42
resnext50_32x4d.a1h_in1k,87.057,12.943,97.331,2.669,25.03,288,1.000,bicubic,+5.043,+1.397,-7
regnetz_b16.ra3_in1k,87.047,12.953,97.410,2.590,9.72,288,1.000,bicubic,+6.319,+1.890,+115
crossvit_15_240.in1k,87.044,12.956,97.423,2.577,27.53,240,0.875,bicubic,+5.510,+1.731,+31
convit_small.fb_in1k,87.042,12.958,97.350,2.650,27.78,224,0.875,bicubic,+5.622,+1.606,+44
gcresnet50t.ra2_in1k,87.029,12.970,97.391,2.609,25.90,288,1.000,bicubic,+5.573,+1.673,+39
tf_efficientnetv2_b3.in1k,87.027,12.973,97.301,2.699,14.36,300,0.904,bicubic,+5.057,+1.489,-6
xcit_small_12_p16_224.fb_in1k,87.012,12.988,97.239,2.761,26.25,224,1.000,bicubic,+5.042,+1.451,-8
poolformerv2_m36.sail_in1k,87.008,12.992,97.284,2.716,56.08,224,1.000,bicubic,+4.792,+1.452,-35
nest_tiny_jx.goog_in1k,87.006,12.994,97.378,2.622,17.06,224,0.875,bicubic,+5.580,+1.760,+36
deit_small_distilled_patch16_224.fb_in1k,87.006,12.994,97.320,2.679,22.44,224,0.900,bicubic,+5.788,+1.936,+56
deit3_small_patch16_224.fb_in1k,87.006,12.994,97.171,2.829,22.06,224,0.900,bicubic,+5.636,+1.715,+45
swinv2_cr_tiny_ns_224.sw_in1k,87.002,12.998,97.280,2.720,28.33,224,0.900,bicubic,+5.200,+1.462,+1
resnet101.a1_in1k,87.000,13.000,96.960,3.040,44.55,288,1.000,bicubic,+4.678,+1.328,-53
coatnet_nano_rw_224.sw_in1k,86.995,13.005,97.248,2.752,15.14,224,0.900,bicubic,+5.297,+1.602,+4
resnet152.a2_in1k,86.995,13.005,96.923,3.077,60.19,288,1.000,bicubic,+4.387,+0.795,-95
resmlp_36_224.fb_distilled_in1k,86.987,13.013,97.276,2.724,44.69,224,0.875,bicubic,+5.839,+1.798,+59
poolformer_m36.sail_in1k,86.955,13.045,97.141,2.859,56.17,224,0.950,bicubic,+4.853,+1.263,-30
mobilevitv2_175.cvnets_in22k_ft_in1k,86.951,13.050,97.335,2.665,14.25,256,0.888,bicubic,+5.013,+1.545,-14
resnet101.a2_in1k,86.942,13.058,96.990,3.010,44.55,288,1.000,bicubic,+4.706,+1.260,-48
regnety_032.tv2_in1k,86.940,13.060,97.410,2.590,19.44,224,0.965,bicubic,+5.184,+1.570,-4
xcit_large_24_p16_224.fb_in1k,86.940,13.060,96.923,3.077,189.10,224,1.000,bicubic,+4.040,+1.037,-132
resnet101.tv2_in1k,86.936,13.065,97.248,2.752,44.55,224,0.965,bilinear,+5.046,+1.480,-15
xcit_medium_24_p16_224.fb_in1k,86.933,13.067,97.103,2.897,84.40,224,1.000,bicubic,+4.293,+1.119,-109
resnetv2_50.a1h_in1k,86.929,13.071,97.340,2.660,25.55,288,1.000,bicubic,+5.531,+1.614,+26
poolformerv2_s36.sail_in1k,86.923,13.077,97.353,2.647,30.79,224,1.000,bicubic,+5.355,+1.663,+4
tnt_s_patch16_224,86.914,13.086,97.361,2.639,23.76,224,0.900,bicubic,+5.378,+1.625,+7
vit_relpos_small_patch16_224.sw_in1k,86.891,13.109,97.489,2.511,21.98,224,0.900,bicubic,+5.429,+1.669,+16
vit_small_patch16_224.augreg_in21k_ft_in1k,86.867,13.133,97.609,2.391,22.05,224,0.900,bicubic,+5.483,+1.473,+24
vit_small_r26_s32_224.augreg_in21k_ft_in1k,86.863,13.137,97.528,2.472,36.43,224,0.900,bicubic,+4.999,+1.506,-21
resnet152.a1_in1k,86.861,13.139,96.793,3.207,60.19,288,1.000,bicubic,+4.129,+1.073,-127
ecaresnetlight.miil_in1k,86.852,13.148,97.508,2.492,30.16,288,0.950,bicubic,+5.444,+1.692,+18
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,86.846,13.154,97.521,2.479,194.03,224,0.875,bilinear,+5.004,+1.429,-23
tf_efficientnet_b3.aa_in1k,86.844,13.156,97.297,2.703,12.23,300,0.904,bicubic,+5.200,+1.575,-10
rexnet_200.nav_in1k,86.842,13.158,97.276,2.724,16.37,224,0.875,bicubic,+5.206,+1.612,-10
convmixer_1536_20.in1k,86.840,13.161,97.355,2.645,51.63,224,0.960,bicubic,+5.478,+1.741,+20
resnet50.fb_swsl_ig1b_ft_in1k,86.831,13.169,97.493,2.507,25.56,224,0.875,bilinear,+5.649,+1.501,+34
deit_base_patch16_224.fb_in1k,86.829,13.171,97.052,2.949,86.57,224,0.900,bicubic,+4.837,+1.316,-40
tresnet_m.miil_in1k_448,86.816,13.184,97.216,2.784,31.39,448,0.875,bilinear,+5.108,+1.642,-21
tf_efficientnet_lite4.in1k,86.801,13.199,97.265,2.735,13.01,380,0.920,bilinear,+5.269,+1.601,-4
coat_mini.in1k,86.799,13.201,97.156,2.844,10.34,224,0.900,bicubic,+5.529,+1.774,+22
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,86.795,13.205,97.472,2.528,88.79,224,0.875,bilinear,+5.189,+1.432,-15
eva02_tiny_patch14_336.mim_in22k_ft_in1k,86.786,13.214,97.269,2.731,5.76,336,1.000,bicubic,+6.156,+1.743,+84
seresnet50.ra2_in1k,86.778,13.223,97.361,2.639,28.09,288,0.950,bicubic,+5.493,+1.709,+18
vit_base_patch16_224.orig_in21k_ft_in1k,86.773,13.227,97.444,2.556,86.57,224,0.900,bicubic,+4.987,+1.320,-31
convnextv2_pico.fcmae_ft_in1k,86.773,13.227,97.331,2.669,9.07,288,0.950,bicubic,+5.687,+1.851,+41
regnetx_080.tv2_in1k,86.771,13.229,97.197,2.803,39.57,224,0.965,bicubic,+5.233,+1.655,-14
cs3darknet_l.c2ns_in1k,86.767,13.233,97.459,2.541,21.16,288,0.950,bicubic,+5.871,+1.797,+53
resnetaa50.a1h_in1k,86.763,13.237,97.391,2.609,25.56,288,1.000,bicubic,+5.149,+1.589,-23
tresnet_l.miil_in1k,86.763,13.237,97.273,2.727,55.99,224,0.875,bilinear,+5.283,+1.650,-8
resnet50d.ra2_in1k,86.760,13.239,97.372,2.628,25.58,288,0.950,bicubic,+5.404,+1.634,+6
ese_vovnet39b.ra_in1k,86.756,13.244,97.372,2.628,24.57,288,0.950,bicubic,+6.406,+2.006,+98
resnet50_gn.a1h_in1k,86.754,13.246,97.449,2.551,25.56,288,0.950,bicubic,+5.538,+1.821,+15
twins_svt_small.in1k,86.754,13.246,97.182,2.818,24.06,224,0.900,bicubic,+5.076,+1.522,-34
seresnet50.a1_in1k,86.746,13.255,96.951,3.049,28.09,288,1.000,bicubic,+5.644,+1.621,+27
mobilevitv2_150.cvnets_in22k_ft_in1k,86.743,13.257,97.214,2.786,10.59,256,0.888,bicubic,+5.255,+1.546,-16
crossvit_base_240.in1k,86.733,13.267,97.122,2.878,105.03,240,0.875,bicubic,+4.517,+1.198,-82
levit_256.fb_dist_in1k,86.731,13.269,97.254,2.746,18.89,224,0.900,bicubic,+5.217,+1.762,-20
levit_conv_256.fb_dist_in1k,86.726,13.274,97.256,2.744,18.89,224,0.900,bicubic,+5.206,+1.766,-23
ecaresnet50t.a2_in1k,86.726,13.274,97.081,2.919,25.57,288,1.000,bicubic,+5.068,+1.529,-37
cs3darknet_focus_l.c2ns_in1k,86.724,13.276,97.376,2.624,21.15,288,0.950,bicubic,+5.844,+1.694,+43
convnext_nano_ols.d1h_in1k,86.718,13.282,97.047,2.953,15.65,288,1.000,bicubic,+5.118,+1.411,-33
vit_srelpos_small_patch16_224.sw_in1k,86.707,13.293,97.252,2.748,21.97,224,0.900,bicubic,+5.615,+1.920,+21
resnet50.ram_in1k,86.699,13.301,97.199,2.801,25.56,288,0.950,bicubic,+6.723,+2.147,+112
halo2botnet50ts_256.a1h_in1k,86.688,13.312,97.098,2.902,22.64,256,0.950,bicubic,+4.626,+1.464,-74
pit_b_224.in1k,86.688,13.312,96.894,3.107,73.76,224,0.900,bicubic,+4.250,+1.178,-121
crossvit_small_240.in1k,86.686,13.314,97.273,2.727,26.86,240,0.875,bicubic,+5.670,+1.817,+22
resnet50d.a1_in1k,86.671,13.329,96.693,3.307,25.58,288,1.000,bicubic,+5.221,+1.475,-22
ecaresnet50d_pruned.miil_in1k,86.669,13.331,97.429,2.571,19.94,288,0.950,bicubic,+5.879,+1.859,+43
tf_efficientnet_b1.ns_jft_in1k,86.669,13.331,97.382,2.618,7.79,240,0.882,bicubic,+5.275,+1.646,-18
swin_tiny_patch4_window7_224.ms_in1k,86.658,13.342,97.203,2.797,28.29,224,0.900,bicubic,+5.282,+1.659,-17
poolformer_s36.sail_in1k,86.654,13.346,97.152,2.848,30.86,224,0.900,bicubic,+5.224,+1.708,-25
gernet_l.idstcv_in1k,86.643,13.357,97.188,2.812,31.08,256,0.875,bilinear,+5.289,+1.658,-15
efficientnet_el.ra_in1k,86.630,13.370,97.186,2.814,10.59,300,0.904,bicubic,+5.318,+1.650,-15
twins_pcpvt_small.in1k,86.620,13.380,97.344,2.656,24.11,224,0.900,bicubic,+5.524,+1.696,+8
resmlp_24_224.fb_distilled_in1k,86.615,13.385,97.137,2.863,30.02,224,0.875,bicubic,+5.859,+1.913,+39
gcresnext50ts.ch_in1k,86.609,13.391,97.188,2.812,15.67,288,1.000,bicubic,+5.377,+1.644,-13
resnet50.c2_in1k,86.605,13.395,97.338,2.662,25.56,288,1.000,bicubic,+5.735,+1.804,+28
nf_resnet50.ra2_in1k,86.592,13.408,97.295,2.705,25.56,288,0.940,bicubic,+5.952,+1.963,+47
resnest50d_4s2x40d.in1k,86.588,13.412,97.267,2.733,30.42,224,0.875,bicubic,+5.468,+1.707,-2
sebotnet33ts_256.a1h_in1k,86.585,13.415,96.791,3.209,13.70,256,0.940,bicubic,+5.419,+1.623,-8
repvgg_b3.rvgg_in1k,86.579,13.421,97.139,2.861,123.09,224,0.875,bilinear,+6.073,+1.885,+53
efficientnet_b3_pruned.in1k,86.577,13.423,97.184,2.816,9.86,300,0.904,bicubic,+5.725,+1.940,+25
wide_resnet50_2.tv2_in1k,86.570,13.430,97.248,2.752,68.88,224,0.965,bilinear,+4.964,+1.486,-56
sehalonet33ts.ra2_in1k,86.566,13.434,97.002,2.998,13.69,256,0.940,bicubic,+5.610,+1.726,+11
resnet50.a1_in1k,86.541,13.459,96.842,3.158,25.56,288,1.000,bicubic,+5.327,+1.740,-17
resnet50.c1_in1k,86.536,13.464,97.235,2.765,25.56,288,1.000,bicubic,+5.624,+1.683,+12
convnext_nano.d1h_in1k,86.536,13.464,97.182,2.818,15.59,288,1.000,bicubic,+5.054,+1.524,-46
xcit_tiny_24_p16_224.fb_dist_in1k,86.534,13.466,97.218,2.782,12.12,224,1.000,bicubic,+6.080,+2.000,+56
seresnet50.a2_in1k,86.519,13.481,96.987,3.013,28.09,288,1.000,bicubic,+5.413,+1.765,-10
vit_small_patch16_384.augreg_in1k,86.502,13.498,97.182,2.818,22.20,384,1.000,bicubic,+5.386,+1.608,-12
halonet50ts.a1h_in1k,86.487,13.513,97.148,2.852,22.73,256,0.940,bicubic,+4.827,+1.538,-71
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,86.485,13.515,97.474,2.526,44.18,224,0.875,bilinear,+5.561,+1.740,+6
maxvit_rmlp_pico_rw_256.sw_in1k,86.481,13.519,97.201,2.799,7.52,256,0.950,bicubic,+5.967,+1.987,+40
resnet152s.gluon_in1k,86.466,13.534,97.109,2.891,60.32,224,0.875,bicubic,+5.464,+1.699,-6
haloregnetz_b.ra3_in1k,86.466,13.534,96.947,3.053,11.68,224,0.940,bicubic,+5.418,+1.747,-8
mobilevitv2_200.cvnets_in1k,86.457,13.543,96.970,3.030,18.45,256,0.888,bicubic,+5.323,+1.608,-20
seresnet33ts.ra2_in1k,86.455,13.545,97.192,2.808,19.78,288,1.000,bicubic,+5.671,+1.830,+13
resnext50d_32x4d.bt_in1k,86.455,13.545,97.165,2.835,25.05,288,0.950,bicubic,+5.791,+1.745,+26
resnet50.d_in1k,86.455,13.545,97.056,2.944,25.56,288,1.000,bicubic,+5.483,+1.626,-5
resnet50.tv2_in1k,86.442,13.558,97.145,2.855,25.56,224,0.965,bilinear,+5.592,+1.711,+8
resnetv2_50x1_bit.goog_in21k_ft_in1k,86.440,13.560,97.605,2.396,25.55,448,1.000,bilinear,+6.100,+1.924,+48
resnet50.b1k_in1k,86.440,13.560,97.237,2.763,25.56,288,1.000,bicubic,+5.734,+1.805,+18
resnest50d_1s4x24d.in1k,86.432,13.568,97.152,2.848,25.68,224,0.875,bicubic,+5.444,+1.826,-13
poolformerv2_s24.sail_in1k,86.389,13.611,97.150,2.850,21.34,224,1.000,bicubic,+5.641,+1.840,+11
repvgg_b3g4.rvgg_in1k,86.368,13.632,97.047,2.953,83.83,224,0.875,bilinear,+6.152,+1.955,+60
regnety_016.tv2_in1k,86.365,13.634,97.188,2.812,11.20,224,0.965,bicubic,+5.697,+1.858,+17
darknetaa53.c2ns_in1k,86.361,13.639,97.160,2.840,36.02,288,1.000,bilinear,+5.859,+1.838,+28
efficientformer_l1.snap_dist_in1k,86.357,13.643,97.019,2.981,12.29,224,0.950,bicubic,+5.859,+2.031,+28
darknet53.c2ns_in1k,86.353,13.647,97.135,2.865,41.61,288,1.000,bicubic,+5.817,+1.701,+23
lamhalobotnet50ts_256.a1h_in1k,86.353,13.647,97.043,2.957,22.57,256,0.950,bicubic,+4.799,+1.553,-79
legacy_senet154.in1k,86.344,13.656,96.934,3.066,115.09,224,0.875,bilinear,+5.036,+1.442,-52
resnet50.a1h_in1k,86.340,13.660,97.058,2.942,25.56,224,1.000,bicubic,+5.664,+1.752,+9
cait_xxs36_224.fb_dist_in1k,86.329,13.671,97.118,2.882,17.30,224,1.000,bicubic,+6.583,+2.244,+79
tf_efficientnet_b3.in1k,86.323,13.677,96.966,3.034,12.23,300,0.904,bicubic,+5.445,+1.666,-10
gernet_m.idstcv_in1k,86.321,13.679,97.101,2.899,21.14,224,0.875,bilinear,+5.587,+1.915,+1
mobilevitv2_175.cvnets_in1k,86.321,13.679,96.985,3.015,14.25,256,0.888,bicubic,+5.461,+1.729,-10
resnet50d.a2_in1k,86.314,13.686,96.674,3.326,25.58,288,1.000,bicubic,+5.150,+1.590,-44
vit_small_patch32_384.augreg_in21k_ft_in1k,86.310,13.690,97.417,2.583,22.92,384,1.000,bicubic,+5.824,+1.821,+18
efficientnet_b2.ra_in1k,86.310,13.690,96.987,3.013,9.11,288,1.000,bicubic,+5.700,+1.673,+9
gcresnet33ts.ra2_in1k,86.304,13.696,97.058,2.942,19.88,288,1.000,bicubic,+5.704,+1.738,+8
pit_s_224.in1k,86.304,13.696,97.047,2.953,23.46,224,0.900,bicubic,+5.212,+1.477,-37
resnext50_32x4d.a1_in1k,86.284,13.716,96.710,3.290,25.03,288,1.000,bicubic,+4.818,+1.536,-80
resnext50_32x4d.a2_in1k,86.280,13.720,96.684,3.316,25.03,288,1.000,bicubic,+4.976,+1.588,-63
resnet50.b2k_in1k,86.272,13.729,97.071,2.929,25.56,288,1.000,bicubic,+5.818,+1.753,+19
senet154.gluon_in1k,86.272,13.729,96.957,3.042,115.09,224,0.875,bicubic,+5.046,+1.599,-61
resnext50_32x4d.tv2_in1k,86.269,13.731,97.054,2.946,25.03,224,0.965,bilinear,+5.087,+1.714,-55
eca_resnet33ts.ra2_in1k,86.257,13.743,97.150,2.850,19.68,288,1.000,bicubic,+5.585,+1.786,-5
resnest50d.in1k,86.248,13.752,97.069,2.931,27.48,224,0.875,bilinear,+5.284,+1.685,-34
gcvit_xxtiny.in1k,86.244,13.756,97.107,2.893,12.00,224,0.875,bicubic,+6.516,+2.027,+64
regnetx_032.tv2_in1k,86.244,13.756,97.092,2.908,15.30,224,0.965,bicubic,+5.318,+1.814,-33
vit_base_patch16_384.augreg_in1k,86.229,13.771,96.964,3.036,86.86,384,1.000,bicubic,+5.127,+1.844,-51
convmixer_768_32.in1k,86.220,13.780,97.037,2.963,21.11,224,0.960,bicubic,+6.052,+1.963,+35
cspdarknet53.ra_in1k,86.190,13.810,97.007,2.993,27.64,256,0.887,bilinear,+6.128,+1.927,+39
efficientnet_el_pruned.in1k,86.186,13.814,97.028,2.972,10.59,300,0.904,bicubic,+5.886,+1.806,+22
tresnet_m.miil_in1k,86.186,13.814,96.667,3.333,31.39,224,0.875,bilinear,+5.388,+1.811,-25
rexnet_150.nav_in1k,86.171,13.829,97.060,2.940,9.73,224,0.875,bicubic,+5.847,+1.886,+17
inception_v4.tf_in1k,86.163,13.837,96.921,3.079,42.68,299,0.875,bicubic,+6.007,+1.953,+33
inception_resnet_v2.tf_in1k,86.131,13.869,97.045,2.955,55.84,299,0.897,bicubic,+5.669,+1.737,+3
xcit_tiny_12_p8_224.fb_in1k,86.114,13.886,97.088,2.912,6.71,224,1.000,bicubic,+6.425,+2.378,+58
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,86.101,13.899,97.212,2.788,25.03,224,0.875,bilinear,+5.763,+1.814,+12
res2net101d.in1k,86.099,13.901,96.851,3.149,45.23,224,0.875,bilinear,+4.875,+1.499,-76
resnet50.a2_in1k,86.097,13.903,96.701,3.299,25.56,288,1.000,bicubic,+5.325,+1.713,-29
tf_efficientnet_el.in1k,86.082,13.918,96.953,3.047,10.59,300,0.904,bicubic,+5.830,+1.835,+16
mobilevitv2_150.cvnets_in1k,86.082,13.918,96.857,3.143,10.59,256,0.888,bicubic,+5.712,+1.783,+5
cspresnext50.ra_in1k,86.077,13.923,97.107,2.893,20.57,256,0.887,bilinear,+5.521,+1.785,-15
convnext_pico_ols.d1_in1k,86.067,13.933,97.017,2.983,9.06,288,1.000,bicubic,+5.603,+1.771,-6
resnet101s.gluon_in1k,86.064,13.936,97.032,2.968,44.67,224,0.875,bicubic,+5.762,+1.882,+8
edgenext_small_rw.sw_in1k,86.054,13.946,96.928,3.072,7.83,320,1.000,bicubic,+5.596,+1.738,-6
lambda_resnet50ts.a1h_in1k,86.047,13.953,96.742,3.258,21.54,256,0.950,bicubic,+4.891,+1.644,-75
seresnext101_32x4d.gluon_in1k,86.026,13.974,96.972,3.027,48.96,224,0.875,bicubic,+5.134,+1.676,-48
poolformer_s24.sail_in1k,86.013,13.987,97.032,2.968,21.39,224,0.900,bicubic,+5.717,+1.960,+6
convnext_pico.d1_in1k,86.013,13.987,96.934,3.066,9.05,288,0.950,bicubic,+5.599,+1.884,-5
resnetblur50.bt_in1k,85.996,14.004,96.985,3.015,25.56,288,0.950,bicubic,+5.762,+1.751,+10
tf_efficientnet_b2.ap_in1k,85.981,14.019,96.812,3.188,9.11,260,0.890,bicubic,+5.673,+1.784,0
ecaresnet26t.ra2_in1k,85.979,14.021,97.041,2.959,16.01,320,0.950,bicubic,+6.129,+1.951,+28
resnet152.a3_in1k,85.979,14.021,96.851,3.149,60.19,224,0.950,bicubic,+5.433,+1.851,-25
seresnext101_64x4d.gluon_in1k,85.973,14.027,96.981,3.019,88.23,224,0.875,bicubic,+5.077,+1.685,-56
efficientformerv2_s1.snap_dist_in1k,85.962,14.038,96.870,3.130,6.19,224,0.950,bicubic,+6.274,+1.816,+40
vit_base_patch32_224.augreg_in21k_ft_in1k,85.956,14.044,97.128,2.872,88.22,224,0.900,bicubic,+5.240,+1.562,-41
resnext50_32x4d.ra_in1k,85.932,14.068,97.037,2.963,25.03,288,0.950,bicubic,+5.234,+1.647,-40
fbnetv3_d.ra2_in1k,85.930,14.070,97.026,2.974,10.31,256,0.950,bilinear,+6.248,+2.082,+38
resnet152d.gluon_in1k,85.919,14.081,96.814,3.186,60.21,224,0.875,bicubic,+5.443,+1.612,-23
vit_large_patch32_384.orig_in21k_ft_in1k,85.911,14.089,97.372,2.628,306.63,384,1.000,bicubic,+4.401,+1.282,-125
tf_efficientnet_b2.aa_in1k,85.896,14.104,96.864,3.136,9.11,260,0.890,bicubic,+5.812,+1.958,+7
tf_efficientnetv2_b2.in1k,85.885,14.115,96.889,3.111,10.10,260,0.890,bicubic,+5.691,+1.847,+1
vit_base_patch16_224.sam_in1k,85.872,14.128,96.691,3.309,86.57,224,0.900,bicubic,+5.628,+1.935,-4
resnet101d.gluon_in1k,85.864,14.136,96.674,3.326,44.57,224,0.875,bicubic,+5.438,+1.650,-22
repvgg_b2g4.rvgg_in1k,85.862,14.138,96.804,3.196,61.76,224,0.875,bilinear,+6.480,+2.128,+51
mixnet_xl.ra_in1k,85.804,14.196,96.716,3.284,11.90,224,0.875,bicubic,+5.322,+1.780,-31
inception_resnet_v2.tf_ens_adv_in1k,85.763,14.237,96.759,3.241,55.84,299,0.897,bicubic,+5.785,+1.813,+4
tf_efficientnet_lite3.in1k,85.757,14.243,96.891,3.109,8.20,300,0.904,bilinear,+5.949,+1.979,+19
resnext101_32x4d.gluon_in1k,85.753,14.247,96.637,3.363,44.18,224,0.875,bicubic,+5.413,+1.707,-21
xcit_tiny_24_p16_224.fb_in1k,85.746,14.254,96.932,3.068,12.12,224,1.000,bicubic,+6.298,+2.054,+42
legacy_seresnext101_32x4d.in1k,85.742,14.258,96.772,3.228,48.96,224,0.875,bilinear,+5.512,+1.746,-10
resnet101.a3_in1k,85.740,14.260,96.496,3.504,44.55,224,0.950,bicubic,+5.926,+1.890,+14
res2net50d.in1k,85.729,14.271,96.763,3.237,25.72,224,0.875,bilinear,+5.475,+1.727,-18
regnety_320.pycls_in1k,85.729,14.271,96.723,3.277,145.05,224,0.875,bicubic,+4.921,+1.485,-67
cspresnet50.ra_in1k,85.725,14.275,96.802,3.198,21.62,256,0.887,bilinear,+6.135,+2.090,+27
resmlp_big_24_224.fb_in1k,85.701,14.299,96.426,3.574,129.14,224,0.875,bicubic,+4.665,+1.408,-92
resnext101_64x4d.gluon_in1k,85.693,14.307,96.641,3.358,83.46,224,0.875,bicubic,+5.093,+1.649,-51
resnet33ts.ra2_in1k,85.691,14.309,96.755,3.245,19.68,288,1.000,bicubic,+5.965,+1.927,+13
xception71.tf_in1k,85.689,14.311,96.774,3.226,42.34,299,0.903,bicubic,+5.817,+1.846,-2
resnet50.ra_in1k,85.674,14.326,96.889,3.111,25.56,288,0.950,bicubic,+5.838,+1.923,+3
ecaresnet50t.a3_in1k,85.674,14.326,96.727,3.273,25.57,224,0.950,bicubic,+6.122,+2.033,+25
deit_small_patch16_224.fb_in1k,85.672,14.328,96.911,3.089,22.05,224,0.900,bicubic,+5.828,+1.867,-1
dpn107.mx_in1k,85.672,14.328,96.757,3.243,86.92,224,0.875,bicubic,+5.504,+1.815,-18
efficientnet_em.ra2_in1k,85.667,14.333,96.947,3.053,6.90,240,0.882,bicubic,+6.427,+2.153,+46
efficientnet_b2_pruned.in1k,85.644,14.356,96.742,3.258,8.31,260,0.890,bicubic,+5.724,+1.890,-12
resmlp_36_224.fb_in1k,85.623,14.377,96.795,3.205,44.69,224,0.875,bicubic,+5.850,+1.911,+2
mobilevitv2_125.cvnets_in1k,85.578,14.422,96.665,3.335,7.48,256,0.888,bicubic,+5.898,+1.807,+10
resnet50.bt_in1k,85.576,14.425,96.832,3.168,25.56,288,0.950,bicubic,+5.936,+1.940,+12
levit_conv_192.fb_dist_in1k,85.571,14.429,96.742,3.258,10.95,224,0.900,bicubic,+5.733,+1.964,-7
resnet32ts.ra2_in1k,85.569,14.431,96.866,3.134,17.96,288,1.000,bicubic,+6.177,+2.214,+24
levit_192.fb_dist_in1k,85.569,14.431,96.740,3.260,10.95,224,0.900,bicubic,+5.731,+1.956,-7
resnet152c.gluon_in1k,85.567,14.433,96.644,3.356,60.21,224,0.875,bicubic,+5.657,+1.800,-18
pit_xs_distilled_224.in1k,85.565,14.435,96.686,3.314,11.00,224,0.900,bicubic,+6.385,+2.324,+42
tf_efficientnetv2_b1.in1k,85.556,14.444,96.731,3.269,8.14,240,0.882,bicubic,+6.094,+2.009,+17
regnety_120.pycls_in1k,85.550,14.450,96.776,3.224,51.82,224,0.875,bicubic,+5.164,+1.648,-51
fbnetv3_b.ra2_in1k,85.520,14.480,96.857,3.143,8.60,256,0.950,bilinear,+6.378,+2.115,+41
regnetx_320.pycls_in1k,85.520,14.480,96.671,3.329,107.81,224,0.875,bicubic,+5.274,+1.649,-39
convnextv2_femto.fcmae_ft_in1k,85.503,14.497,96.806,3.194,5.23,288,0.950,bicubic,+6.165,+2.246,+22
nf_regnet_b1.ra2_in1k,85.503,14.497,96.789,3.211,10.22,288,0.900,bicubic,+6.197,+2.049,+23
dpn92.mx_in1k,85.503,14.497,96.650,3.350,37.67,224,0.875,bicubic,+5.465,+1.790,-29
regnety_160.pycls_in1k,85.490,14.510,96.616,3.384,83.59,224,0.875,bicubic,+5.192,+1.654,-47
resnet152.gluon_in1k,85.479,14.521,96.550,3.450,60.19,224,0.875,bicubic,+5.785,+1.814,-9
resnetrs50.tf_in1k,85.475,14.525,96.731,3.269,35.69,224,0.910,bicubic,+5.579,+1.759,-27
rexnet_130.nav_in1k,85.469,14.531,96.680,3.320,7.56,224,0.875,bicubic,+5.965,+2.002,+3
dpn131.mx_in1k,85.450,14.550,96.620,3.380,79.25,224,0.875,bicubic,+5.636,+1.920,-20
regnetx_160.pycls_in1k,85.396,14.604,96.650,3.350,54.28,224,0.875,bicubic,+5.530,+1.822,-27
tf_efficientnet_b2.in1k,85.396,14.604,96.586,3.414,9.11,260,0.890,bicubic,+5.792,+1.872,-6
convnext_tiny.fb_in22k_ft_in1k,85.381,14.619,96.804,3.196,28.59,288,1.000,bicubic,+6.483,+2.130,+43
dla102x2.in1k,85.375,14.625,96.629,3.371,41.28,224,0.875,bilinear,+5.931,+1.991,+4
dpn98.mx_in1k,85.351,14.649,96.511,3.489,61.57,224,0.875,bicubic,+5.681,+1.857,-12
regnetx_016.tv2_in1k,85.349,14.651,96.817,3.183,9.19,224,0.965,bicubic,+5.913,+2.049,+2
gmlp_s16_224.ra3_in1k,85.349,14.651,96.644,3.356,19.42,224,0.875,bicubic,+5.705,+2.022,-12
botnet26t_256.c1_in1k,85.332,14.668,96.631,3.369,12.49,256,0.950,bicubic,+6.076,+2.097,+16
seresnext50_32x4d.gluon_in1k,85.328,14.672,96.669,3.331,27.56,224,0.875,bicubic,+5.418,+1.845,-39
skresnext50_32x4d.ra_in1k,85.328,14.672,96.390,3.610,27.48,224,0.875,bicubic,+5.166,+1.750,-49
xception65.tf_in1k,85.315,14.685,96.644,3.356,39.92,299,0.903,bicubic,+5.759,+1.990,-12
resnet101c.gluon_in1k,85.306,14.694,96.411,3.589,44.57,224,0.875,bicubic,+5.764,+1.825,-11
lambda_resnet26t.c1_in1k,85.298,14.702,96.721,3.280,10.96,256,0.940,bicubic,+6.210,+2.130,+22
resnext50_32x4d.a3_in1k,85.298,14.702,96.328,3.672,25.03,224,0.950,bicubic,+6.030,+2.022,+9
regnety_064.pycls_in1k,85.283,14.717,96.648,3.352,30.58,224,0.875,bicubic,+5.569,+1.882,-28
resnet34d.ra2_in1k,85.272,14.728,96.701,3.299,21.82,288,0.950,bicubic,+6.836,+2.357,+62
coat_lite_mini.in1k,85.272,14.728,96.686,3.314,11.01,224,0.900,bicubic,+6.170,+2.078,+18
resmlp_24_224.fb_in1k,85.264,14.736,96.490,3.510,30.02,224,0.875,bicubic,+5.890,+1.944,-6
convnext_femto_ols.d1_in1k,85.253,14.747,96.772,3.228,5.23,288,0.950,bicubic,+6.329,+2.246,+26
cait_xxs24_224.fb_dist_in1k,85.238,14.762,96.714,3.286,11.96,224,1.000,bicubic,+6.854,+2.398,+66
regnety_080.pycls_in1k,85.238,14.762,96.639,3.361,39.18,224,0.875,bicubic,+5.370,+1.807,-48
pvt_v2_b1.in1k,85.210,14.790,96.629,3.371,14.01,224,0.900,bicubic,+6.508,+2.127,+41
xcit_tiny_12_p16_224.fb_dist_in1k,85.200,14.800,96.597,3.403,6.72,224,1.000,bicubic,+6.626,+2.399,+44
halonet26t.a1h_in1k,85.193,14.807,96.454,3.546,12.48,256,0.950,bicubic,+6.087,+2.148,+9
inception_v3.gluon_in1k,85.189,14.811,96.535,3.465,23.83,299,0.875,bicubic,+6.383,+2.161,+28
resnext101_32x8d.tv_in1k,85.189,14.811,96.456,3.544,88.79,224,0.875,bilinear,+5.879,+1.936,-9
convnext_femto.d1_in1k,85.163,14.837,96.704,3.296,5.22,288,0.950,bicubic,+6.447,+2.273,+34
resnet101.gluon_in1k,85.163,14.837,96.366,3.634,44.55,224,0.875,bicubic,+5.853,+1.844,-12
hrnet_w48.ms_in1k,85.155,14.845,96.490,3.510,77.47,224,0.875,bilinear,+5.853,+1.974,-10
regnetx_120.pycls_in1k,85.138,14.862,96.475,3.525,46.11,224,0.875,bicubic,+5.550,+1.733,-32
eca_halonext26ts.c1_in1k,85.131,14.869,96.580,3.420,10.76,256,0.940,bicubic,+5.645,+1.980,-28
resnet50.am_in1k,85.131,14.869,96.569,3.431,25.56,224,0.875,bicubic,+6.133,+2.173,+8
tf_efficientnet_b1.ap_in1k,85.131,14.869,96.407,3.593,7.79,240,0.882,bicubic,+5.851,+2.099,-12
eca_botnext26ts_256.c1_in1k,85.121,14.879,96.511,3.489,10.59,256,0.950,bicubic,+5.853,+1.905,-12
legacy_xception.tf_in1k,85.121,14.879,96.469,3.531,22.86,299,0.897,bicubic,+6.081,+2.087,+4
hrnet_w64.ms_in1k,85.112,14.888,96.738,3.262,128.06,224,0.875,bilinear,+5.638,+2.092,-31
resnet50.fb_ssl_yfcc100m_ft_in1k,85.102,14.899,96.859,3.141,25.56,224,0.875,bilinear,+5.868,+2.033,-11
lambda_resnet26rpt_256.c1_in1k,85.099,14.901,96.565,3.435,10.99,256,0.940,bicubic,+6.131,+2.127,+3
res2net101_26w_4s.in1k,85.097,14.903,96.381,3.619,45.21,224,0.875,bilinear,+5.895,+1.945,-10
tf_efficientnet_cc_b1_8e.in1k,85.065,14.935,96.428,3.572,39.72,240,0.882,bicubic,+5.763,+2.054,-20
dpn68b.ra_in1k,85.061,14.939,96.449,3.551,12.61,288,1.000,bicubic,+5.699,+2.011,-28
resnest26d.gluon_in1k,85.016,14.984,96.639,3.361,17.07,224,0.875,bilinear,+6.532,+2.345,+33
xcit_nano_12_p8_384.fb_dist_in1k,85.014,14.986,96.629,3.371,3.05,384,1.000,bicubic,+7.194,+2.589,+79
resnext50_32x4d.gluon_in1k,85.005,14.995,96.428,3.572,25.03,224,0.875,bicubic,+5.645,+1.998,-30
tf_efficientnet_b0.ns_jft_in1k,84.999,15.001,96.505,3.495,5.29,224,0.875,bicubic,+6.329,+2.131,+20
coat_tiny.in1k,84.971,15.029,96.422,3.578,5.50,224,0.900,bicubic,+6.545,+2.374,+35
regnety_040.pycls_in1k,84.956,15.044,96.612,3.388,20.65,224,0.875,bicubic,+5.724,+1.956,-20
dla169.in1k,84.929,15.071,96.537,3.463,53.39,224,0.875,bilinear,+6.221,+2.191,+14
tf_efficientnet_b1.aa_in1k,84.918,15.082,96.364,3.636,7.79,240,0.882,bicubic,+6.088,+2.166,+2
resnet50d.a3_in1k,84.903,15.097,96.285,3.715,25.58,224,0.950,bicubic,+6.181,+2.049,+9
legacy_seresnext50_32x4d.in1k,84.897,15.103,96.428,3.572,27.56,224,0.875,bilinear,+5.821,+1.996,-14
mobilevitv2_100.cvnets_in1k,84.894,15.106,96.388,3.612,4.90,256,0.888,bicubic,+6.818,+2.218,+53
hrnet_w44.ms_in1k,84.884,15.116,96.428,3.572,67.06,224,0.875,bilinear,+5.992,+2.064,-6
regnety_008_tv.tv2_in1k,84.879,15.120,96.637,3.363,6.43,224,0.965,bicubic,+6.205,+2.251,+10
resnet50s.gluon_in1k,84.879,15.120,96.439,3.561,25.68,224,0.875,bicubic,+6.161,+2.197,+5
regnetx_080.pycls_in1k,84.865,15.136,96.432,3.568,39.57,224,0.875,bicubic,+5.663,+1.880,-28
visformer_tiny.in1k,84.847,15.153,96.507,3.493,10.32,224,0.900,bicubic,+6.685,+2.343,+42
levit_conv_128.fb_dist_in1k,84.847,15.153,96.353,3.647,9.21,224,0.900,bicubic,+6.353,+2.345,+15
levit_128.fb_dist_in1k,84.845,15.155,96.353,3.647,9.21,224,0.900,bicubic,+6.355,+2.341,+15
resnet50d.gluon_in1k,84.837,15.163,96.398,3.602,25.58,224,0.875,bicubic,+5.759,+1.930,-25
res2net50_26w_8s.in1k,84.837,15.163,96.355,3.645,48.40,224,0.875,bilinear,+5.895,+2.061,-17
dla60_res2next.in1k,84.830,15.170,96.405,3.595,17.03,224,0.875,bilinear,+6.396,+2.261,+18
vit_tiny_patch16_384.augreg_in21k_ft_in1k,84.824,15.176,96.714,3.286,5.79,384,1.000,bicubic,+6.400,+2.174,+18
resnet152.tv_in1k,84.824,15.176,96.238,3.762,60.19,224,0.875,bilinear,+6.502,+2.190,+27
dla60_res2net.in1k,84.822,15.178,96.479,3.521,20.85,224,0.875,bilinear,+6.360,+2.277,+12
resnet26t.ra2_in1k,84.822,15.178,96.388,3.612,16.01,320,1.000,bicubic,+6.490,+2.264,+23
mixnet_l.ft_in1k,84.818,15.182,96.328,3.672,7.33,224,0.875,bicubic,+5.852,+2.148,-25
dla102x.in1k,84.807,15.193,96.552,3.448,26.31,224,0.875,bilinear,+6.309,+2.316,+3
hrnet_w18.ms_aug_in1k,84.785,15.214,96.462,3.538,21.30,224,0.950,bilinear,+6.659,+2.410,+33
xception41.tf_in1k,84.785,15.214,96.411,3.589,26.97,299,0.903,bicubic,+6.281,+2.135,0
pit_xs_224.in1k,84.781,15.219,96.499,3.502,10.62,224,0.900,bicubic,+6.601,+2.335,+27
regnetx_064.pycls_in1k,84.771,15.229,96.494,3.506,26.21,224,0.875,bicubic,+5.705,+2.036,-34
resnet34.a1_in1k,84.762,15.238,96.227,3.773,21.80,288,1.000,bicubic,+6.844,+3.313,+43
poolformerv2_s12.sail_in1k,84.756,15.244,96.373,3.627,11.89,224,1.000,bicubic,+6.752,+2.508,+36
gcresnext26ts.ch_in1k,84.756,15.244,96.293,3.707,10.48,288,1.000,bicubic,+6.342,+2.123,+10
hrnet_w40.ms_in1k,84.745,15.255,96.552,3.448,57.56,224,0.875,bilinear,+5.815,+2.088,-32
repvgg_b2.rvgg_in1k,84.726,15.274,96.471,3.529,89.02,224,0.875,bilinear,+5.934,+2.051,-22
res2net50_26w_6s.in1k,84.726,15.274,96.281,3.719,37.05,224,0.875,bilinear,+6.158,+2.159,-10
resmlp_12_224.fb_distilled_in1k,84.719,15.281,96.225,3.775,15.35,224,0.875,bicubic,+6.765,+2.665,+35
vit_base_patch32_384.augreg_in1k,84.717,15.283,96.326,3.674,88.30,384,1.000,bicubic,+5.965,+2.102,-23
legacy_seresnet152.in1k,84.709,15.291,96.424,3.576,66.82,224,0.875,bilinear,+6.049,+2.054,-15
cs3darknet_m.c2ns_in1k,84.694,15.306,96.486,3.514,9.31,288,0.950,bicubic,+7.060,+2.470,+47
SelecSls60b.in1k,84.662,15.338,96.300,3.700,32.77,224,0.875,bicubic,+6.248,+2.266,0
bat_resnext26ts.ch_in1k,84.653,15.347,96.268,3.732,10.73,256,0.900,bicubic,+6.401,+2.170,+9
hrnet_w32.ms_in1k,84.649,15.351,96.417,3.583,41.23,224,0.875,bilinear,+6.205,+2.229,-7
seresnext26d_32x4d.bt_in1k,84.642,15.357,96.261,3.739,16.81,288,0.950,bicubic,+5.829,+2.022,-34
tf_efficientnetv2_b0.in1k,84.625,15.375,96.279,3.721,7.14,224,0.875,bicubic,+6.267,+2.265,+1
efficientnet_b1.ft_in1k,84.611,15.389,96.334,3.666,7.79,256,1.000,bicubic,+5.811,+1.992,-34
regnetx_040.pycls_in1k,84.604,15.396,96.379,3.621,22.12,224,0.875,bicubic,+6.112,+2.137,-16
regnety_032.pycls_in1k,84.600,15.400,96.417,3.583,19.44,224,0.875,bicubic,+5.724,+2.009,-42
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,84.598,15.402,96.014,3.986,119.42,256,0.900,bicubic,+5.114,+1.876,-87
seresnext26t_32x4d.bt_in1k,84.589,15.411,96.381,3.619,16.81,288,0.950,bicubic,+5.845,+2.069,-34
hrnet_w30.ms_in1k,84.583,15.417,96.381,3.619,37.71,224,0.875,bilinear,+6.391,+2.159,+3
efficientnet_es.ra_in1k,84.583,15.417,96.304,3.696,5.44,224,0.875,bicubic,+6.529,+2.380,+13
tf_mixnet_l.in1k,84.572,15.428,96.244,3.756,7.33,224,0.875,bicubic,+5.796,+2.242,-39
wide_resnet101_2.tv_in1k,84.549,15.451,96.351,3.649,126.89,224,0.875,bilinear,+5.711,+2.069,-46
vit_small_patch16_224.augreg_in1k,84.531,15.469,96.272,3.728,22.05,224,0.900,bicubic,+5.685,+1.984,-48
dla60x.in1k,84.529,15.471,96.289,3.711,17.35,224,0.875,bilinear,+6.289,+2.255,-2
legacy_seresnet101.in1k,84.499,15.501,96.326,3.674,49.33,224,0.875,bilinear,+6.113,+2.064,-14
cs3darknet_focus_m.c2ns_in1k,84.482,15.518,96.417,3.583,9.30,288,0.950,bicubic,+7.198,+2.451,+48
seresnext26ts.ch_in1k,84.482,15.518,96.319,3.681,10.39,288,1.000,bicubic,+6.210,+2.225,-10
resnet50.a3_in1k,84.482,15.518,96.003,3.997,25.56,224,0.950,bicubic,+6.434,+2.223,+6
tf_efficientnet_b1.in1k,84.478,15.522,96.076,3.924,7.79,240,0.882,bicubic,+5.918,+1.982,-34
coat_lite_tiny.in1k,84.459,15.541,96.383,3.617,5.72,224,0.900,bicubic,+6.939,+2.461,+30
tf_efficientnet_em.in1k,84.453,15.547,96.185,3.815,6.90,240,0.882,bicubic,+6.331,+2.139,-2
wide_resnet50_2.tv_in1k,84.429,15.571,96.259,3.741,68.88,224,0.875,bilinear,+5.949,+2.173,-30
repvgg_b1.rvgg_in1k,84.420,15.580,96.217,3.783,57.42,224,0.875,bilinear,+6.052,+2.121,-20
efficientnet_b1_pruned.in1k,84.391,15.610,96.140,3.860,6.33,240,0.882,bicubic,+6.148,+2.248,-13
vit_base_patch16_224.augreg_in1k,84.388,15.612,96.042,3.958,86.57,224,0.900,bicubic,+5.234,+1.952,-78
res2net50_26w_4s.in1k,84.359,15.642,96.091,3.909,25.70,224,0.875,bilinear,+6.409,+2.239,+5
hardcorenas_f.miil_green_in1k,84.320,15.680,96.025,3.975,8.20,224,0.875,bilinear,+6.222,+2.219,-7
res2net50_14w_8s.in1k,84.311,15.688,96.076,3.924,25.06,224,0.875,bilinear,+6.153,+2.230,-11
SelecSls60.in1k,84.284,15.716,96.097,3.903,30.67,224,0.875,bicubic,+6.300,+2.265,0
mobilevit_s.cvnets_in1k,84.275,15.725,96.259,3.741,5.58,256,0.900,bicubic,+5.963,+2.115,-23
regnetx_032.pycls_in1k,84.245,15.755,96.242,3.758,15.30,224,0.875,bicubic,+6.077,+2.160,-16
ese_vovnet19b_dw.ra_in1k,84.226,15.774,96.259,3.741,6.54,288,0.950,bicubic,+6.482,+2.471,+9
eca_resnext26ts.ch_in1k,84.224,15.776,96.191,3.809,10.30,288,1.000,bicubic,+6.224,+2.265,-6
convnextv2_atto.fcmae_ft_in1k,84.224,15.776,96.059,3.941,3.71,288,0.950,bicubic,+6.464,+2.333,+7
convnext_atto_ols.a2_in1k,84.220,15.780,96.217,3.783,3.70,288,0.950,bicubic,+7.004,+2.541,+32
resnet50c.gluon_in1k,84.216,15.784,96.163,3.837,25.58,224,0.875,bicubic,+6.204,+2.175,-10
res2next50.in1k,84.213,15.787,96.001,3.999,24.67,224,0.875,bilinear,+5.971,+2.165,-27
dla102.in1k,84.183,15.817,96.206,3.794,33.27,224,0.875,bilinear,+6.161,+2.274,-13
densenetblur121d.ra_in1k,84.171,15.829,96.240,3.760,8.00,288,0.950,bicubic,+6.851,+2.450,+20
rexnet_100.nav_in1k,84.158,15.842,96.247,3.753,4.80,224,0.875,bicubic,+6.296,+2.603,-5
inception_v3.tf_in1k,84.147,15.853,95.913,4.087,23.83,299,0.875,bicubic,+6.285,+2.047,-4
convnext_atto.d2_in1k,84.141,15.859,96.200,3.800,3.70,288,0.950,bicubic,+7.133,+2.498,+35
res2net50_48w_2s.in1k,84.117,15.883,95.963,4.037,25.29,224,0.875,bilinear,+6.603,+2.411,+9
xcit_tiny_12_p16_224.fb_in1k,84.111,15.889,96.236,3.764,6.72,224,1.000,bicubic,+6.971,+2.520,+27
tf_efficientnet_lite2.in1k,84.085,15.915,96.076,3.924,6.09,260,0.890,bicubic,+6.621,+2.326,+8
poolformer_s12.sail_in1k,84.045,15.955,96.178,3.822,11.92,224,0.900,bicubic,+6.811,+2.646,+20
resnet34.a2_in1k,84.045,15.955,95.924,4.076,21.80,288,1.000,bicubic,+6.887,+2.650,+23
efficientnet_b0.ra_in1k,84.034,15.966,95.965,4.035,5.29,224,0.875,bicubic,+6.340,+2.433,-6
crossvit_9_dagger_240.in1k,84.015,15.985,96.084,3.916,8.78,240,0.875,bicubic,+7.035,+2.464,+29
tf_efficientnet_cc_b0_8e.in1k,83.974,16.026,96.067,3.933,24.01,224,0.875,bicubic,+6.068,+2.407,-16
hardcorenas_e.miil_green_in1k,83.968,16.032,95.898,4.101,8.07,224,0.875,bilinear,+6.184,+2.201,-12
gmixer_24_224.ra3_in1k,83.966,16.034,95.854,4.146,24.72,224,0.875,bicubic,+5.940,+2.186,-28
regnety_016.pycls_in1k,83.963,16.037,96.005,3.995,11.20,224,0.875,bicubic,+6.102,+2.287,-17
resnext50_32x4d.tv_in1k,83.947,16.053,95.967,4.033,25.03,224,0.875,bilinear,+6.329,+2.271,-8
resnet50.gluon_in1k,83.940,16.060,96.018,3.982,25.56,224,0.875,bicubic,+6.360,+2.300,-6
densenet161.tv_in1k,83.910,16.090,96.022,3.978,28.68,224,0.875,bicubic,+6.552,+2.380,+2
inception_v3.tf_adv_in1k,83.897,16.103,95.937,4.063,23.83,299,0.875,bicubic,+6.305,+2.207,-10
mobilenetv2_120d.ra_in1k,83.897,16.103,95.903,4.097,5.83,224,0.875,bicubic,+6.595,+2.399,+3
resnet101.tv_in1k,83.857,16.143,95.888,4.112,44.55,224,0.875,bilinear,+6.481,+2.346,-2
tinynet_a.in1k,83.825,16.175,95.817,4.183,6.19,192,0.875,bicubic,+6.167,+2.277,-17
resnet26d.bt_in1k,83.791,16.209,95.960,4.040,16.01,288,0.950,bicubic,+6.383,+2.322,-5
dpn68b.mx_in1k,83.788,16.212,95.980,4.020,12.61,224,0.875,bicubic,+6.270,+2.128,-11
hardcorenas_d.miil_green_in1k,83.767,16.233,95.743,4.257,7.50,224,0.875,bilinear,+6.327,+2.253,-9
inception_v3.tv_in1k,83.765,16.235,95.886,4.114,23.83,299,0.875,bicubic,+6.331,+2.412,-9
xcit_nano_12_p8_224.fb_dist_in1k,83.737,16.263,95.954,4.046,3.05,224,1.000,bicubic,+7.405,+2.860,+35
dla60.in1k,83.712,16.288,95.928,4.072,22.04,224,0.875,bilinear,+6.666,+2.610,+9
repvgg_b1g4.rvgg_in1k,83.699,16.301,96.027,3.973,39.97,224,0.875,bilinear,+6.117,+2.191,-19
resnext26ts.ra2_in1k,83.699,16.301,95.982,4.018,10.30,288,1.000,bicubic,+6.525,+2.518,0
convmixer_1024_20_ks9_p14.in1k,83.682,16.318,95.884,4.116,24.38,224,0.960,bicubic,+6.746,+2.534,+11
legacy_seresnet50.in1k,83.669,16.331,95.984,4.016,28.09,224,0.875,bilinear,+6.025,+2.226,-26
regnetx_008.tv2_in1k,83.669,16.331,95.975,4.025,7.26,224,0.965,bicubic,+6.365,+2.309,-11
tf_efficientnet_b0.ap_in1k,83.652,16.348,95.785,4.215,5.29,224,0.875,bicubic,+6.562,+2.523,+1
skresnet34.ra_in1k,83.645,16.355,95.928,4.072,22.28,224,0.875,bicubic,+6.735,+2.784,+9
tf_efficientnet_cc_b0_4e.in1k,83.643,16.357,95.738,4.262,13.31,224,0.875,bicubic,+6.341,+2.402,-12
seresnet50.a3_in1k,83.618,16.382,95.713,4.287,28.09,224,0.950,bicubic,+6.590,+2.639,+1
densenet121.ra_in1k,83.598,16.402,96.052,3.948,7.98,288,0.950,bicubic,+7.098,+2.684,+19
resmlp_12_224.fb_in1k,83.569,16.431,95.760,4.240,15.35,224,0.875,bicubic,+6.921,+2.582,+9
densenet201.tv_in1k,83.554,16.446,95.809,4.191,20.01,224,0.875,bicubic,+6.268,+2.329,-15
mobilenetv3_large_100.miil_in21k_ft_in1k,83.554,16.446,95.452,4.548,5.48,224,0.875,bilinear,+5.636,+1.686,-46
mixnet_m.ft_in1k,83.532,16.468,95.687,4.313,5.01,224,0.875,bicubic,+6.274,+2.269,-15
legacy_seresnext26_32x4d.in1k,83.524,16.476,95.709,4.292,16.79,224,0.875,bicubic,+6.416,+2.395,-9
gernet_s.idstcv_in1k,83.513,16.487,95.798,4.202,8.17,224,0.875,bilinear,+6.603,+2.482,+1
tf_efficientnet_b0.aa_in1k,83.502,16.498,95.706,4.294,5.29,224,0.875,bicubic,+6.668,+2.486,+1
hrnet_w18.ms_in1k,83.494,16.506,95.909,4.091,21.30,224,0.875,bilinear,+6.742,+2.465,+1
resnet34.bt_in1k,83.468,16.532,95.963,4.037,21.80,288,0.950,bicubic,+6.988,+2.609,+11
SelecSls42b.in1k,83.449,16.551,95.736,4.264,32.46,224,0.875,bicubic,+6.279,+2.344,-17
efficientformerv2_s0.snap_dist_in1k,83.398,16.602,95.817,4.183,3.60,224,0.950,bicubic,+7.280,+2.957,+16
hardcorenas_c.miil_green_in1k,83.353,16.647,95.713,4.287,5.52,224,0.875,bilinear,+6.295,+2.545,-14
tf_efficientnet_lite1.in1k,83.338,16.662,95.647,4.353,5.42,240,0.882,bicubic,+6.694,+2.417,-2
dpn68.mx_in1k,83.195,16.805,95.617,4.383,12.61,224,0.875,bicubic,+6.849,+2.609,+9
tf_mixnet_m.in1k,83.189,16.811,95.469,4.531,5.01,224,0.875,bicubic,+6.235,+2.315,-12
regnetx_016.pycls_in1k,83.180,16.820,95.740,4.260,9.19,224,0.875,bicubic,+6.256,+2.325,-11
tf_efficientnet_es.in1k,83.180,16.820,95.583,4.417,5.44,224,0.875,bicubic,+6.580,+2.381,-4
xcit_nano_12_p16_384.fb_dist_in1k,83.174,16.826,95.755,4.245,3.05,384,1.000,bicubic,+7.716,+3.059,+23
mobilenetv2_140.ra_in1k,83.174,16.826,95.687,4.313,6.11,224,0.875,bicubic,+6.654,+2.821,-1
levit_128s.fb_dist_in1k,83.052,16.948,95.533,4.467,7.78,224,0.900,bicubic,+6.526,+2.661,-4
levit_conv_128s.fb_dist_in1k,83.046,16.954,95.536,4.464,7.78,224,0.900,bicubic,+6.526,+2.550,-3
repvgg_a2.rvgg_in1k,82.994,17.006,95.593,4.407,28.21,224,0.875,bilinear,+6.536,+2.591,-1
resnet50.tv_in1k,82.960,17.040,95.472,4.529,25.56,224,0.875,bilinear,+6.830,+2.614,+3
resnet26.bt_in1k,82.917,17.083,95.726,4.274,16.00,288,0.950,bicubic,+6.551,+2.546,-2
hardcorenas_b.miil_green_in1k,82.870,17.130,95.395,4.605,5.18,224,0.875,bilinear,+6.334,+2.635,-11
mobilevitv2_075.cvnets_in1k,82.796,17.204,95.570,4.430,2.87,256,0.888,bicubic,+7.188,+2.826,+11
densenet169.tv_in1k,82.689,17.311,95.604,4.396,14.15,224,0.875,bicubic,+6.789,+2.576,+5
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,82.680,17.320,95.845,4.155,6.36,384,1.000,bicubic,+6.718,+2.581,+2
regnety_004.tv2_in1k,82.642,17.358,95.499,4.501,4.34,224,0.965,bicubic,+7.044,+2.803,+9
edgenext_x_small.in1k,82.580,17.420,95.459,4.541,2.34,288,1.000,bicubic,+6.892,+2.693,+4
tf_efficientnet_b0.in1k,82.563,17.437,95.418,4.582,5.29,224,0.875,bicubic,+6.031,+2.410,-16
mixnet_s.ft_in1k,82.518,17.482,95.363,4.637,4.13,224,0.875,bicubic,+6.524,+2.093,-3
vit_small_patch32_224.augreg_in21k_ft_in1k,82.516,17.484,95.664,4.336,22.88,224,0.900,bicubic,+6.522,+2.866,-5
regnety_008.pycls_in1k,82.482,17.518,95.491,4.509,6.26,224,0.875,bicubic,+6.180,+2.431,-9
efficientnet_lite0.ra_in1k,82.373,17.627,95.296,4.704,4.65,224,0.875,bicubic,+6.893,+2.774,+6
resnest14d.gluon_in1k,82.356,17.644,95.337,4.663,10.61,224,0.875,bilinear,+6.846,+2.833,+4
hardcorenas_a.miil_green_in1k,82.328,17.672,95.296,4.704,5.26,224,0.875,bilinear,+6.398,+2.786,-6
efficientnet_es_pruned.in1k,82.298,17.702,95.296,4.704,5.44,224,0.875,bicubic,+7.284,+2.850,+13
mobilenetv3_rw.rmsp_in1k,82.264,17.736,95.234,4.766,5.48,224,0.875,bicubic,+6.644,+2.531,-3
semnasnet_100.rmsp_in1k,82.260,17.740,95.222,4.778,3.89,224,0.875,bicubic,+6.810,+2.628,+4
mobilenetv3_large_100.ra_in1k,82.181,17.819,95.190,4.810,5.48,224,0.875,bicubic,+6.415,+2.652,-8
vit_tiny_patch16_224.augreg_in21k_ft_in1k,82.076,17.924,95.474,4.526,5.72,224,0.900,bicubic,+6.622,+2.630,0
mobilenetv2_110d.ra_in1k,82.076,17.924,95.072,4.928,4.52,224,0.875,bicubic,+7.020,+2.886,+7
tf_mixnet_s.in1k,82.036,17.964,95.126,4.874,4.13,224,0.875,bicubic,+6.384,+2.490,-9
repvgg_b0.rvgg_in1k,82.001,17.999,95.104,4.896,15.82,224,0.875,bilinear,+6.859,+2.688,+1
deit_tiny_distilled_patch16_224.fb_in1k,81.993,18.007,95.138,4.862,5.91,224,0.900,bicubic,+7.493,+3.246,+16
mixer_b16_224.goog_in21k_ft_in1k,81.976,18.024,94.451,5.549,59.88,224,0.875,bicubic,+5.374,+2.227,-35
hrnet_w18_small_v2.ms_in1k,81.972,18.029,95.160,4.840,15.60,224,0.875,bilinear,+6.858,+2.746,0
tf_efficientnet_lite0.in1k,81.948,18.052,95.160,4.840,4.65,224,0.875,bicubic,+7.118,+2.988,+5
tinynet_b.in1k,81.880,18.120,94.876,5.124,3.73,188,0.875,bicubic,+6.902,+2.690,+2
tf_mobilenetv3_large_100.in1k,81.843,18.157,95.062,4.938,5.48,224,0.875,bilinear,+6.327,+2.468,-12
pit_ti_distilled_224.in1k,81.779,18.221,95.096,4.904,5.10,224,0.900,bicubic,+7.525,+3.142,+11
densenet121.tv_in1k,81.732,18.268,95.036,4.964,7.98,224,0.875,bicubic,+6.968,+2.882,+3
regnety_006.pycls_in1k,81.715,18.285,95.115,4.885,6.06,224,0.875,bicubic,+6.445,+2.589,-9
regnetx_004_tv.tv2_in1k,81.681,18.319,95.062,4.938,5.50,224,0.965,bicubic,+7.085,+2.884,+5
resnet18d.ra2_in1k,81.679,18.321,95.076,4.923,11.71,288,0.950,bicubic,+7.885,+3.239,+15
dla34.in1k,81.666,18.334,94.867,5.133,15.74,224,0.875,bilinear,+7.028,+2.803,+1
xcit_nano_12_p8_224.fb_in1k,81.647,18.353,95.271,4.729,3.05,224,1.000,bicubic,+7.731,+3.101,+11
crossvit_9_240.in1k,81.615,18.385,94.978,5.022,8.55,240,0.875,bicubic,+7.657,+3.010,+9
fbnetc_100.rmsp_in1k,81.562,18.438,94.970,5.030,5.57,224,0.875,bilinear,+6.432,+2.582,-13
mobilevit_xs.cvnets_in1k,81.549,18.451,95.025,4.975,2.32,256,0.900,bicubic,+6.913,+2.679,-2
legacy_seresnet34.in1k,81.540,18.460,94.893,5.107,21.96,224,0.875,bilinear,+6.732,+2.767,-7
regnetx_008.pycls_in1k,81.485,18.515,95.051,4.949,7.26,224,0.875,bicubic,+6.455,+2.713,-13
resnet34.gluon_in1k,81.483,18.517,94.801,5.199,21.80,224,0.875,bicubic,+6.905,+2.817,-3
mnasnet_100.rmsp_in1k,81.446,18.554,94.914,5.086,4.38,224,0.875,bicubic,+6.796,+2.790,-8
vgg19_bn.tv_in1k,81.438,18.562,94.771,5.229,143.68,224,0.875,bilinear,+7.222,+2.927,-2
vit_base_patch32_224.augreg_in1k,81.143,18.857,94.427,5.572,88.22,224,0.900,bicubic,+6.249,+2.649,-14
convit_tiny.fb_in1k,81.126,18.874,95.036,4.964,5.71,224,0.875,bicubic,+8.014,+3.324,+10
crossvit_tiny_240.in1k,81.092,18.908,94.983,5.017,7.01,240,0.875,bicubic,+7.756,+3.075,+6
resnet18.a1_in1k,81.041,18.959,94.357,5.643,11.69,288,1.000,bicubic,+7.883,+3.331,+7
spnasnet_100.rmsp_in1k,80.880,19.119,94.528,5.472,4.42,224,0.875,bilinear,+6.794,+2.708,-6
resnet34.a3_in1k,80.816,19.184,94.355,5.645,21.80,224,0.950,bicubic,+7.846,+3.247,+10
ghostnet_100.in1k,80.705,19.295,94.293,5.707,5.18,224,0.875,bilinear,+6.723,+2.827,-6
regnety_004.pycls_in1k,80.652,19.348,94.682,5.318,4.34,224,0.875,bicubic,+6.624,+2.932,-8
skresnet18.ra_in1k,80.648,19.352,94.378,5.622,11.96,224,0.875,bicubic,+7.610,+3.206,+4
regnetx_006.pycls_in1k,80.639,19.361,94.530,5.470,6.20,224,0.875,bicubic,+6.771,+2.852,-6
pit_ti_224.in1k,80.597,19.404,94.618,5.383,4.85,224,0.900,bicubic,+7.687,+3.218,+6
resnet18.fb_swsl_ig1b_ft_in1k,80.586,19.414,94.737,5.263,11.69,224,0.875,bilinear,+7.304,+3.005,-2
vgg16_bn.tv_in1k,80.571,19.429,94.600,5.400,138.37,224,0.875,bilinear,+7.201,+3.086,-5
semnasnet_075.rmsp_in1k,80.481,19.519,94.317,5.684,2.91,224,0.875,bicubic,+7.489,+3.178,0
resnet34.tv_in1k,80.383,19.617,94.430,5.570,21.80,224,0.875,bilinear,,,
resnet18.a2_in1k,80.306,19.694,94.101,5.899,11.69,288,1.000,bicubic,+7.934,+3.505,+5
mobilenetv2_100.ra_in1k,80.253,19.747,94.186,5.814,3.50,224,0.875,bicubic,+7.277,+3.176,-2
xcit_nano_12_p16_224.fb_dist_in1k,80.238,19.762,94.353,5.647,3.05,224,1.000,bicubic,+7.928,+3.493,+5
vit_base_patch32_224.sam_in1k,80.214,19.786,93.823,6.177,88.22,224,0.900,bicubic,+6.520,+2.809,-13
resnet18.fb_ssl_yfcc100m_ft_in1k,80.084,19.916,94.592,5.408,11.69,224,0.875,bilinear,+7.490,+3.174,-2
tf_mobilenetv3_large_075.in1k,80.084,19.916,94.184,5.816,3.99,224,0.875,bilinear,+6.652,+2.832,-14
deit_tiny_patch16_224.fb_in1k,80.011,19.988,94.447,5.553,5.72,224,0.900,bicubic,+7.840,+3.335,+4
hrnet_w18_small.ms_in1k,79.550,20.450,93.906,6.093,13.19,224,0.875,bilinear,+7.212,+3.228,-1
vgg19.tv_in1k,79.484,20.516,93.868,6.132,143.67,224,0.875,bilinear,+7.106,+2.994,-4
regnetx_004.pycls_in1k,79.412,20.588,93.849,6.151,5.16,224,0.875,bicubic,+7.014,+3.021,-6
tf_mobilenetv3_large_minimal_100.in1k,79.232,20.768,93.702,6.298,3.92,224,0.875,bilinear,+6.974,+3.064,-2
edgenext_xx_small.in1k,79.175,20.825,93.819,6.181,1.33,288,1.000,bicubic,+7.297,+3.267,+2
legacy_seresnet18.in1k,79.166,20.834,93.776,6.224,11.78,224,0.875,bicubic,+7.400,+3.444,+3
resnet14t.c3_in1k,79.140,20.860,93.567,6.433,10.08,224,0.950,bicubic,+6.886,+3.265,-4
vgg16.tv_in1k,79.038,20.962,93.644,6.356,138.36,224,0.875,bilinear,+7.446,+3.260,+2
vgg13_bn.tv_in1k,78.995,21.005,93.661,6.339,133.05,224,0.875,bilinear,+7.407,+3.283,+2
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,78.991,21.009,93.898,6.102,6.34,224,0.900,bicubic,+7.193,+3.074,-2
lcnet_100.ra2_in1k,78.897,21.103,93.548,6.452,2.95,224,0.875,bicubic,+6.801,+3.186,-6
pvt_v2_b0.in1k,78.756,21.244,93.836,6.164,3.67,224,0.900,bicubic,+8.096,+3.640,+2
tinynet_c.in1k,78.444,21.556,93.127,6.873,2.46,184,0.875,bicubic,+7.204,+3.393,-1
resnet18.gluon_in1k,78.372,21.628,93.129,6.871,11.69,224,0.875,bicubic,+7.534,+3.375,-1
mobilevitv2_050.cvnets_in1k,78.114,21.887,93.571,6.429,1.37,256,0.888,bicubic,+7.975,+3.651,+2
vgg11_bn.tv_in1k,77.956,22.044,93.232,6.768,132.87,224,0.875,bilinear,+7.573,+3.424,-1
xcit_nano_12_p16_224.fb_in1k,77.904,22.096,93.437,6.563,3.05,224,1.000,bicubic,+7.942,+3.675,+1
regnety_002.pycls_in1k,77.420,22.580,92.905,7.095,3.16,224,0.875,bicubic,+7.142,+3.377,-2
resnet18.tv_in1k,77.287,22.713,92.747,7.253,11.69,224,0.875,bilinear,+7.527,+3.677,+1
mixer_l16_224.goog_in21k_ft_in1k,77.279,22.721,90.584,9.416,208.20,224,0.875,bicubic,+5.225,+2.910,-14
vgg13.tv_in1k,77.227,22.773,92.692,7.308,133.05,224,0.875,bilinear,+7.295,+3.442,-2
mobilevit_xxs.cvnets_in1k,76.604,23.396,92.675,7.325,1.27,256,0.900,bicubic,+7.672,+3.733,0
resnet18.a3_in1k,76.454,23.546,92.233,7.767,11.69,224,0.950,bicubic,+8.200,+4.057,+3
vgg11.tv_in1k,76.384,23.616,92.156,7.844,132.86,224,0.875,bilinear,+7.362,+3.532,-3
resnet10t.c3_in1k,76.173,23.827,92.222,7.778,5.44,224,0.950,bicubic,+7.815,+4.182,0
regnetx_002.pycls_in1k,76.124,23.876,92.194,7.806,2.68,224,0.875,bicubic,+7.378,+3.658,-2
lcnet_075.ra2_in1k,76.036,23.964,92.060,7.940,2.36,224,0.875,bicubic,+7.254,+3.700,-4
dla60x_c.in1k,75.622,24.378,92.169,7.831,1.32,224,0.875,bilinear,+7.724,+3.747,0
mobilenetv3_small_100.lamb_in1k,74.913,25.087,91.492,8.508,2.54,224,0.875,bicubic,+7.257,+3.856,0
tf_mobilenetv3_small_100.in1k,74.721,25.279,91.261,8.739,2.54,224,0.875,bilinear,+6.803,+3.587,-3
tinynet_d.in1k,74.290,25.710,90.917,9.083,2.34,152,0.875,bicubic,+7.316,+3.855,-1
mnasnet_small.lamb_in1k,73.801,26.199,90.732,9.268,2.03,224,0.875,bicubic,+7.605,+4.228,-1
dla46x_c.in1k,73.649,26.351,91.090,8.910,1.07,224,0.875,bilinear,+7.661,+4.110,-1
mobilenetv2_050.lamb_in1k,73.463,26.537,90.322,9.678,1.97,224,0.875,bicubic,+7.513,+4.234,-1
tf_mobilenetv3_small_075.in1k,72.804,27.196,90.046,9.954,2.04,224,0.875,bilinear,+7.076,+3.920,-1
dla46_c.in1k,72.622,27.378,90.503,9.497,1.30,224,0.875,bilinear,+7.750,+4.205,0
mobilenetv3_small_075.lamb_in1k,72.312,27.688,89.668,10.332,2.04,224,0.875,bicubic,+7.079,+4.222,-2
lcnet_050.ra2_in1k,70.423,29.577,88.838,11.162,1.88,224,0.875,bicubic,+7.303,+4.452,-1
tf_mobilenetv3_small_minimal_100.in1k,70.098,29.902,88.516,11.485,2.04,224,0.875,bilinear,+7.208,+4.276,-1
tinynet_e.in1k,66.806,33.194,86.280,13.720,2.04,106,0.875,bicubic,+6.940,+4.516,-1
mobilenetv3_small_050.lamb_in1k,64.695,35.305,84.854,15.146,1.59,224,0.875,bicubic,+6.785,+4.674,-1
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,90.052,9.948,99.048,0.952,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,89.966,10.034,99.012,0.988,305.08,448,1.000,bicubic
eva_giant_patch14_560.m30m_ft_in22k_in1k,89.786,10.214,98.992,1.008,"1,014.45",560,1.000,bicubic
eva02_large_patch14_448.mim_in22k_ft_in1k,89.624,10.376,98.950,1.050,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in1k,89.570,10.430,98.922,1.078,305.08,448,1.000,bicubic
eva_giant_patch14_336.m30m_ft_in22k_in1k,89.566,10.434,98.952,1.048,"1,013.01",336,1.000,bicubic
eva_giant_patch14_336.clip_ft_in1k,89.466,10.534,98.826,1.174,"1,013.01",336,1.000,bicubic
eva_large_patch14_336.in22k_ft_in22k_in1k,89.208,10.792,98.854,1.146,304.53,336,1.000,bicubic
eva_giant_patch14_224.clip_ft_in1k,88.880,11.120,98.680,1.320,"1,012.56",224,0.900,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_512,88.858,11.142,98.748,1.252,660.29,512,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,88.692,11.308,98.724,1.276,87.12,448,1.000,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_384,88.670,11.330,98.738,1.262,660.29,384,1.000,bicubic
eva_large_patch14_336.in22k_ft_in1k,88.670,11.330,98.722,1.278,304.53,336,1.000,bicubic
convnext_xxlarge.clip_laion2b_soup_ft_in1k,88.604,11.396,98.708,1.292,846.47,256,1.000,bicubic
beit_large_patch16_512.in22k_ft_in22k_in1k,88.596,11.404,98.656,1.344,305.67,512,1.000,bicubic
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,88.592,11.408,98.662,1.338,632.46,336,1.000,bicubic
eva_large_patch14_196.in22k_ft_in22k_in1k,88.576,11.424,98.658,1.342,304.14,196,1.000,bicubic
maxvit_xlarge_tf_512.in21k_ft_in1k,88.538,11.462,98.644,1.356,475.77,512,1.000,bicubic
beit_large_patch16_384.in22k_ft_in22k_in1k,88.402,11.598,98.608,1.392,305.00,384,1.000,bicubic
beitv2_large_patch16_224.in1k_ft_in22k_in1k,88.394,11.606,98.598,1.402,304.43,224,0.950,bicubic
tf_efficientnet_l2.ns_jft_in1k,88.352,11.648,98.648,1.352,480.31,800,0.960,bicubic
maxvit_xlarge_tf_384.in21k_ft_in1k,88.314,11.686,98.544,1.456,475.32,384,1.000,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,88.306,11.694,98.582,1.418,200.13,384,1.000,bicubic
vit_large_patch14_clip_336.openai_ft_in12k_in1k,88.268,11.732,98.526,1.474,304.53,336,1.000,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,88.256,11.744,98.552,1.448,632.05,224,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in1k,88.250,11.750,98.564,1.436,87.12,448,1.000,bicubic
tf_efficientnet_l2.ns_jft_in1k_475,88.234,11.766,98.546,1.454,480.31,475,0.936,bicubic
regnety_1280.swag_ft_in1k,88.230,11.770,98.686,1.314,644.81,384,1.000,bicubic
maxvit_large_tf_512.in21k_ft_in1k,88.226,11.774,98.598,1.402,212.33,512,1.000,bicubic
maxvit_base_tf_512.in21k_ft_in1k,88.218,11.782,98.528,1.472,119.88,512,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k_384,88.198,11.802,98.528,1.472,197.96,384,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,88.178,11.822,98.570,1.430,304.53,336,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in12k_in1k,88.176,11.824,98.544,1.456,304.20,224,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k_384,88.058,11.942,98.582,1.418,98.75,384,1.000,bicubic
maxvit_large_tf_384.in21k_ft_in1k,87.986,12.014,98.568,1.432,212.03,384,1.000,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,87.958,12.042,98.476,1.524,200.13,320,1.000,bicubic
eva_large_patch14_196.in22k_ft_in1k,87.934,12.066,98.498,1.502,304.14,196,1.000,bicubic
maxvit_base_tf_384.in21k_ft_in1k,87.922,12.078,98.544,1.456,119.65,384,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,87.892,12.108,98.410,1.590,304.20,224,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in1k,87.856,12.144,98.368,1.632,304.53,336,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in1k,87.852,12.148,98.426,1.574,304.20,224,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,87.848,12.152,98.446,1.554,200.13,384,1.000,bicubic
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,87.828,12.172,98.372,1.628,116.14,384,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k_384,87.752,12.248,98.556,1.444,350.20,384,1.000,bicubic
deit3_large_patch16_384.fb_in22k_ft_in1k,87.720,12.280,98.512,1.488,304.76,384,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k_384,87.644,12.356,98.416,1.584,88.72,384,1.000,bicubic
convformer_b36.sail_in22k_ft_in1k_384,87.602,12.398,98.434,1.566,99.88,384,1.000,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in1k,87.588,12.412,98.218,1.782,632.05,224,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k,87.484,12.516,98.356,1.644,197.96,288,1.000,bicubic
beit_large_patch16_224.in22k_ft_in22k_in1k,87.478,12.522,98.304,1.696,304.43,224,0.900,bicubic
convnext_large.fb_in22k_ft_in1k_384,87.472,12.528,98.386,1.614,197.77,384,1.000,bicubic
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,87.464,12.536,98.374,1.626,116.09,384,1.000,bicubic
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,87.464,12.536,98.250,1.750,196.74,384,1.000,bicubic
caformer_m36.sail_in22k_ft_in1k_384,87.450,12.550,98.308,1.692,56.20,384,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k,87.422,12.578,98.326,1.674,98.75,224,1.000,bicubic
beitv2_large_patch16_224.in1k_ft_in1k,87.412,12.588,98.234,1.766,304.43,224,0.950,bicubic
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,87.382,12.618,98.312,1.688,73.88,384,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,87.334,12.666,98.218,1.782,200.13,256,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k,87.330,12.670,98.328,1.672,350.20,288,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in1k,87.282,12.718,98.246,1.754,304.20,224,1.000,bicubic
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,87.208,12.792,98.034,1.966,86.86,384,1.000,bicubic
deit3_huge_patch14_224.fb_in22k_ft_in1k,87.186,12.814,98.260,1.740,632.13,224,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,87.134,12.866,98.222,1.778,88.59,384,1.000,bicubic
swin_large_patch4_window12_384.ms_in22k_ft_in1k,87.132,12.868,98.234,1.766,196.74,384,1.000,bicubic
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,87.096,12.904,98.234,1.766,87.92,384,1.000,bicubic
vit_large_patch16_384.augreg_in21k_ft_in1k,87.084,12.916,98.302,1.698,304.72,384,1.000,bicubic
volo_d5_512.sail_in1k,87.058,12.942,97.970,2.030,296.09,512,1.150,bicubic
convnext_large.fb_in22k_ft_in1k,87.026,12.974,98.204,1.796,197.77,288,1.000,bicubic
vit_base_patch16_clip_384.openai_ft_in12k_in1k,87.024,12.976,98.180,1.820,86.86,384,0.950,bicubic
convformer_b36.sail_in22k_ft_in1k,86.998,13.002,98.172,1.828,99.88,224,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k,86.998,13.002,98.168,1.832,88.72,288,1.000,bicubic
deit3_large_patch16_224.fb_in22k_ft_in1k,86.982,13.018,98.236,1.764,304.37,224,1.000,bicubic
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,86.952,13.048,98.106,1.894,196.74,256,0.900,bicubic
volo_d5_448.sail_in1k,86.952,13.048,97.938,2.062,295.91,448,1.150,bicubic
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,86.894,13.106,98.014,1.986,116.14,224,0.950,bicubic
convformer_m36.sail_in22k_ft_in1k_384,86.892,13.108,98.116,1.884,57.05,384,1.000,bicubic
caformer_s36.sail_in22k_ft_in1k_384,86.856,13.144,98.212,1.788,39.30,384,1.000,bicubic
tf_efficientnet_b7.ns_jft_in1k,86.840,13.160,98.094,1.906,66.35,600,0.949,bicubic
regnety_320.swag_ft_in1k,86.836,13.164,98.362,1.638,145.05,384,1.000,bicubic
tf_efficientnetv2_l.in21k_ft_in1k,86.802,13.198,98.136,1.864,118.52,480,1.000,bicubic
beit_base_patch16_384.in22k_ft_in22k_in1k,86.800,13.200,98.136,1.864,86.74,384,1.000,bicubic
convnext_base.fb_in22k_ft_in1k_384,86.796,13.204,98.264,1.736,88.59,384,1.000,bicubic
volo_d4_448.sail_in1k,86.792,13.208,97.884,2.116,193.41,448,1.150,bicubic
tf_efficientnetv2_xl.in21k_ft_in1k,86.748,13.252,98.014,1.986,208.12,512,1.000,bicubic
deit3_base_patch16_384.fb_in22k_ft_in1k,86.738,13.262,98.114,1.886,86.88,384,1.000,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,86.724,13.276,98.176,1.824,93.59,320,1.000,bicubic
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,86.642,13.358,98.020,1.980,116.09,224,0.950,bicubic
vit_base_patch16_clip_384.laion2b_ft_in1k,86.620,13.380,98.008,1.992,86.86,384,1.000,bicubic
maxvit_base_tf_512.in1k,86.602,13.398,97.918,2.082,119.88,512,1.000,bicubic
caformer_m36.sail_in22k_ft_in1k,86.594,13.406,98.024,1.976,56.20,224,1.000,bicubic
convnextv2_huge.fcmae_ft_in1k,86.580,13.420,97.972,2.028,660.29,288,1.000,bicubic
coatnet_2_rw_224.sw_in12k_ft_in1k,86.568,13.432,97.894,2.106,73.87,224,0.950,bicubic
maxvit_large_tf_512.in1k,86.526,13.474,97.880,2.120,212.33,512,1.000,bicubic
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,86.504,13.496,97.894,2.106,73.88,224,0.950,bicubic
convnext_base.clip_laiona_augreg_ft_in1k_384,86.502,13.498,97.968,2.032,88.59,384,1.000,bicubic
volo_d3_448.sail_in1k,86.500,13.500,97.710,2.290,86.63,448,1.000,bicubic
cait_m48_448.fb_dist_in1k,86.492,13.508,97.752,2.248,356.46,448,1.000,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k,86.484,13.516,98.030,1.970,93.59,288,1.000,bicubic
beitv2_base_patch16_224.in1k_ft_in22k_in1k,86.474,13.526,98.052,1.948,86.53,224,0.900,bicubic
tf_efficientnet_b6.ns_jft_in1k,86.456,13.544,97.884,2.116,43.04,528,0.942,bicubic
swin_base_patch4_window12_384.ms_in22k_ft_in1k,86.438,13.562,98.066,1.934,87.90,384,1.000,bicubic
caformer_b36.sail_in1k_384,86.408,13.592,97.814,2.186,98.75,384,1.000,bicubic
convformer_s36.sail_in22k_ft_in1k_384,86.378,13.622,97.984,2.016,40.01,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,86.370,13.630,97.984,2.016,88.59,256,1.000,bicubic
dm_nfnet_f6.dm_in1k,86.362,13.638,97.896,2.104,438.36,576,0.956,bicubic
swin_large_patch4_window7_224.ms_in22k_ft_in1k,86.312,13.688,97.902,2.098,196.53,224,0.900,bicubic
maxvit_base_tf_384.in1k,86.302,13.698,97.798,2.202,119.65,384,1.000,bicubic
convnext_base.fb_in22k_ft_in1k,86.274,13.726,98.092,1.908,88.59,288,1.000,bicubic
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,86.268,13.732,97.882,2.118,87.92,256,0.900,bicubic
maxvit_large_tf_384.in1k,86.228,13.772,97.690,2.310,212.03,384,1.000,bicubic
vit_base_patch8_224.augreg2_in21k_ft_in1k,86.218,13.782,97.832,2.168,86.58,224,0.900,bicubic
vit_base_patch16_clip_384.openai_ft_in1k,86.204,13.796,97.874,2.126,86.86,384,1.000,bicubic
convnext_small.in12k_ft_in1k_384,86.182,13.818,97.922,2.078,50.22,384,1.000,bicubic
vit_large_r50_s32_384.augreg_in21k_ft_in1k,86.182,13.818,97.922,2.078,329.09,384,1.000,bicubic
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,86.174,13.826,97.756,2.244,86.57,224,0.950,bicubic
caformer_m36.sail_in1k_384,86.166,13.834,97.820,2.180,56.20,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in1k,86.158,13.842,97.680,2.320,88.59,256,1.000,bicubic
convformer_m36.sail_in22k_ft_in1k,86.146,13.854,97.850,2.150,57.05,224,1.000,bicubic
convnextv2_large.fcmae_ft_in1k,86.118,13.882,97.822,2.178,197.96,288,1.000,bicubic
dm_nfnet_f5.dm_in1k,86.098,13.902,97.688,2.312,377.21,544,0.954,bicubic
tf_efficientnet_b5.ns_jft_in1k,86.088,13.912,97.754,2.246,30.39,456,0.934,bicubic
maxvit_small_tf_512.in1k,86.082,13.918,97.764,2.236,69.13,512,1.000,bicubic
volo_d5_224.sail_in1k,86.070,13.930,97.576,2.424,295.46,224,0.960,bicubic
cait_m36_384.fb_dist_in1k,86.060,13.940,97.730,2.270,271.22,384,1.000,bicubic
volo_d2_384.sail_in1k,86.042,13.958,97.574,2.426,58.87,384,1.000,bicubic
regnety_160.swag_ft_in1k,86.010,13.990,98.054,1.946,83.59,384,1.000,bicubic
tf_efficientnetv2_m.in21k_ft_in1k,86.000,14.000,97.946,2.054,54.14,480,1.000,bicubic
vit_base_patch16_384.augreg_in21k_ft_in1k,85.996,14.004,98.002,1.998,86.86,384,1.000,bicubic
xcit_large_24_p8_384.fb_dist_in1k,85.996,14.004,97.690,2.310,188.93,384,1.000,bicubic
regnety_160.lion_in12k_ft_in1k,85.986,14.014,97.836,2.164,83.59,288,1.000,bicubic
regnety_160.sw_in12k_ft_in1k,85.986,14.014,97.834,2.166,83.59,288,1.000,bicubic
regnety_1280.swag_lc_in1k,85.982,14.018,97.850,2.150,644.81,224,0.965,bicubic
vit_base_patch16_clip_224.openai_ft_in12k_in1k,85.940,14.060,97.728,2.272,86.57,224,0.950,bicubic
efficientnet_b5.sw_in12k_ft_in1k,85.892,14.108,97.736,2.264,30.39,448,1.000,bicubic
volo_d4_224.sail_in1k,85.872,14.128,97.472,2.528,192.96,224,0.960,bicubic
dm_nfnet_f4.dm_in1k,85.836,14.164,97.664,2.336,316.07,512,0.951,bicubic
vit_large_patch16_224.augreg_in21k_ft_in1k,85.834,14.166,97.818,2.182,304.33,224,0.900,bicubic
xcit_medium_24_p8_384.fb_dist_in1k,85.816,14.184,97.592,2.408,84.32,384,1.000,bicubic
deit3_large_patch16_384.fb_in1k,85.812,14.188,97.598,2.402,304.76,384,1.000,bicubic
vit_base_patch8_224.augreg_in21k_ft_in1k,85.798,14.202,97.790,2.210,86.58,224,0.900,bicubic
caformer_s36.sail_in22k_ft_in1k,85.792,14.208,97.824,2.176,39.30,224,1.000,bicubic
convnext_small.fb_in22k_ft_in1k_384,85.778,14.222,97.890,2.110,50.22,384,1.000,bicubic
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,85.778,14.222,97.638,2.362,88.34,448,1.000,bicubic
xcit_large_24_p16_384.fb_dist_in1k,85.754,14.246,97.538,2.462,189.10,384,1.000,bicubic
convformer_b36.sail_in1k_384,85.742,14.258,97.524,2.476,99.88,384,1.000,bicubic
caformer_s36.sail_in1k_384,85.740,14.260,97.672,2.328,39.30,384,1.000,bicubic
eva02_small_patch14_336.mim_in22k_ft_in1k,85.716,14.284,97.632,2.368,22.13,336,1.000,bicubic
deit3_base_patch16_224.fb_in22k_ft_in1k,85.700,14.300,97.746,2.254,86.59,224,1.000,bicubic
dm_nfnet_f3.dm_in1k,85.686,14.314,97.570,2.430,254.92,416,0.940,bicubic
maxvit_tiny_tf_512.in1k,85.664,14.336,97.584,2.416,31.05,512,1.000,bicubic
tf_efficientnetv2_l.in1k,85.664,14.336,97.474,2.526,118.52,480,1.000,bicubic
flexivit_large.1200ep_in1k,85.646,14.354,97.542,2.458,304.36,240,0.950,bicubic
beitv2_base_patch16_224.in1k_ft_in1k,85.594,14.406,97.506,2.494,86.53,224,0.900,bicubic
convformer_m36.sail_in1k_384,85.580,14.420,97.542,2.458,57.05,384,1.000,bicubic
xcit_small_24_p8_384.fb_dist_in1k,85.554,14.446,97.570,2.430,47.63,384,1.000,bicubic
maxvit_small_tf_384.in1k,85.540,14.460,97.462,2.538,69.02,384,1.000,bicubic
flexivit_large.600ep_in1k,85.538,14.462,97.488,2.512,304.36,240,0.950,bicubic
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,85.532,14.468,97.638,2.362,39.03,384,0.950,bicubic
caformer_b36.sail_in1k,85.506,14.494,97.310,2.690,98.75,224,1.000,bicubic
convnextv2_base.fcmae_ft_in1k,85.474,14.526,97.384,2.616,88.72,288,1.000,bicubic
vit_base_patch16_clip_224.laion2b_ft_in1k,85.466,14.534,97.574,2.426,86.57,224,1.000,bicubic
cait_s36_384.fb_dist_in1k,85.454,14.546,97.478,2.522,68.37,384,1.000,bicubic
deit_base_distilled_patch16_384.fb_in1k,85.426,14.574,97.330,2.670,87.63,384,1.000,bicubic
xcit_medium_24_p16_384.fb_dist_in1k,85.422,14.578,97.406,2.594,84.40,384,1.000,bicubic
caformer_s18.sail_in22k_ft_in1k_384,85.414,14.586,97.702,2.298,26.34,384,1.000,bicubic
convformer_s36.sail_in22k_ft_in1k,85.414,14.586,97.568,2.432,40.01,224,1.000,bicubic
volo_d3_224.sail_in1k,85.414,14.586,97.276,2.724,86.33,224,0.960,bicubic
xcit_large_24_p8_224.fb_dist_in1k,85.402,14.598,97.402,2.598,188.93,224,1.000,bicubic
regnety_120.sw_in12k_ft_in1k,85.400,14.600,97.582,2.418,51.82,288,1.000,bicubic
convformer_s36.sail_in1k_384,85.378,14.622,97.476,2.524,40.01,384,1.000,bicubic
tf_efficientnet_b8.ra_in1k,85.368,14.632,97.394,2.606,87.41,672,0.954,bicubic
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,85.366,14.634,97.656,2.344,88.30,384,1.000,bicubic
tf_efficientnet_b8.ap_in1k,85.364,14.636,97.292,2.708,87.41,672,0.954,bicubic
convnext_small.in12k_ft_in1k,85.330,14.670,97.546,2.454,50.22,288,1.000,bicubic
vit_base_patch16_clip_224.openai_ft_in1k,85.292,14.708,97.438,2.562,86.57,224,0.900,bicubic
flexivit_large.300ep_in1k,85.290,14.710,97.400,2.600,304.36,240,0.950,bicubic
swin_base_patch4_window7_224.ms_in22k_ft_in1k,85.272,14.728,97.564,2.436,87.77,224,0.900,bicubic
convnext_small.fb_in22k_ft_in1k,85.262,14.738,97.682,2.318,50.22,288,1.000,bicubic
volo_d1_384.sail_in1k,85.244,14.756,97.214,2.786,26.78,384,1.000,bicubic
mvitv2_large.fb_in1k,85.244,14.756,97.194,2.806,217.99,224,0.900,bicubic
caformer_m36.sail_in1k,85.232,14.768,97.200,2.800,56.20,224,1.000,bicubic
deit3_huge_patch14_224.fb_in1k,85.224,14.776,97.360,2.640,632.13,224,0.900,bicubic
vit_base_patch32_clip_384.openai_ft_in12k_in1k,85.216,14.784,97.406,2.594,88.30,384,0.950,bicubic
beit_base_patch16_224.in22k_ft_in22k_in1k,85.212,14.788,97.658,2.342,86.53,224,0.900,bicubic
tf_efficientnetv2_m.in1k,85.204,14.796,97.364,2.636,54.14,480,1.000,bicubic
volo_d2_224.sail_in1k,85.202,14.798,97.190,2.810,58.68,224,0.960,bicubic
dm_nfnet_f2.dm_in1k,85.194,14.806,97.346,2.654,193.78,352,0.920,bicubic
tf_efficientnet_b4.ns_jft_in1k,85.162,14.838,97.470,2.530,19.34,380,0.922,bicubic
regnety_2560.seer_ft_in1k,85.152,14.848,97.436,2.564,"1,282.60",384,1.000,bicubic
convnext_tiny.in12k_ft_in1k_384,85.122,14.878,97.606,2.394,28.59,384,1.000,bicubic
tf_efficientnet_b7.ap_in1k,85.122,14.878,97.252,2.748,66.35,600,0.949,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k_384,85.106,14.894,97.628,2.372,28.64,384,1.000,bicubic
maxvit_tiny_tf_384.in1k,85.100,14.900,97.378,2.622,30.98,384,1.000,bicubic
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,85.098,14.902,97.438,2.562,468.53,224,0.875,bilinear
vit_base_patch16_224.augreg2_in21k_ft_in1k,85.096,14.904,97.530,2.470,86.57,224,0.900,bicubic
xcit_small_24_p16_384.fb_dist_in1k,85.090,14.910,97.312,2.688,47.67,384,1.000,bicubic
xcit_small_12_p8_384.fb_dist_in1k,85.078,14.922,97.282,2.718,26.21,384,1.000,bicubic
xcit_medium_24_p8_224.fb_dist_in1k,85.074,14.926,97.274,2.726,84.32,224,1.000,bicubic
deit3_base_patch16_384.fb_in1k,85.072,14.928,97.252,2.748,86.88,384,1.000,bicubic
cait_s24_384.fb_dist_in1k,85.048,14.952,97.346,2.654,47.06,384,1.000,bicubic
regnetz_e8.ra3_in1k,85.036,14.964,97.272,2.728,57.70,320,1.000,bicubic
caformer_s18.sail_in1k_384,85.026,14.974,97.358,2.642,26.34,384,1.000,bicubic
resnetrs420.tf_in1k,85.006,14.994,97.124,2.876,191.89,416,1.000,bicubic
convformer_s18.sail_in22k_ft_in1k_384,84.998,15.002,97.570,2.430,26.77,384,1.000,bicubic
vit_base_r50_s16_384.orig_in21k_ft_in1k,84.976,15.024,97.290,2.710,98.95,384,1.000,bicubic
ecaresnet269d.ra2_in1k,84.966,15.034,97.222,2.778,102.09,352,1.000,bicubic
maxvit_large_tf_224.in1k,84.942,15.058,96.970,3.030,211.79,224,0.950,bicubic
tf_efficientnet_b7.ra_in1k,84.932,15.068,97.208,2.792,66.35,600,0.949,bicubic
resnetv2_152x4_bit.goog_in21k_ft_in1k,84.916,15.084,97.438,2.562,936.53,480,1.000,bilinear
xcit_large_24_p16_224.fb_dist_in1k,84.916,15.084,97.130,2.870,189.10,224,1.000,bicubic
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,84.910,15.090,96.958,3.042,41.72,224,0.950,bicubic
coat_lite_medium_384.in1k,84.878,15.122,97.372,2.628,44.57,384,1.000,bicubic
xcit_small_24_p8_224.fb_dist_in1k,84.868,15.132,97.190,2.810,47.63,224,1.000,bicubic
maxvit_base_tf_224.in1k,84.860,15.140,96.988,3.012,119.47,224,0.950,bicubic
convnext_large.fb_in1k,84.846,15.154,97.214,2.786,197.77,288,1.000,bicubic
deit3_small_patch16_384.fb_in22k_ft_in1k,84.824,15.176,97.488,2.512,22.21,384,1.000,bicubic
convformer_b36.sail_in1k,84.818,15.182,96.946,3.054,99.88,224,1.000,bicubic
efficientnetv2_rw_m.agc_in1k,84.812,15.188,97.150,2.850,53.24,416,1.000,bicubic
tf_efficientnet_b6.ap_in1k,84.786,15.214,97.136,2.864,43.04,528,0.942,bicubic
deit3_large_patch16_224.fb_in1k,84.776,15.224,97.038,2.962,304.37,224,0.900,bicubic
resnetrs350.tf_in1k,84.714,15.286,96.992,3.008,163.96,384,1.000,bicubic
xcit_small_12_p16_384.fb_dist_in1k,84.712,15.288,97.118,2.882,26.25,384,1.000,bicubic
eca_nfnet_l2.ra3_in1k,84.702,15.298,97.266,2.734,56.72,384,1.000,bicubic
dm_nfnet_f1.dm_in1k,84.702,15.298,97.182,2.818,132.63,320,0.910,bicubic
flexivit_base.1200ep_in1k,84.678,15.322,96.996,3.004,86.59,240,0.950,bicubic
davit_base.msft_in1k,84.642,15.358,97.020,2.980,87.95,224,0.950,bicubic
maxxvit_rmlp_small_rw_256.sw_in1k,84.624,15.376,97.068,2.932,66.01,256,0.950,bicubic
coatnet_rmlp_2_rw_224.sw_in1k,84.606,15.394,96.740,3.260,73.88,224,0.950,bicubic
swinv2_base_window16_256.ms_in1k,84.600,15.400,97.090,2.910,87.92,256,0.900,bicubic
seresnextaa101d_32x8d.ah_in1k,84.566,15.434,97.076,2.924,93.59,288,1.000,bicubic
deit3_medium_patch16_224.fb_in22k_ft_in1k,84.552,15.448,97.188,2.812,38.85,224,1.000,bicubic
regnety_320.swag_lc_in1k,84.546,15.454,97.442,2.558,145.05,224,0.965,bicubic
rexnetr_300.sw_in12k_ft_in1k,84.544,15.456,97.256,2.744,34.81,288,1.000,bicubic
vit_base_patch16_224.augreg_in21k_ft_in1k,84.532,15.468,97.292,2.708,86.57,224,0.900,bicubic
flexivit_base.600ep_in1k,84.522,15.478,96.936,3.064,86.59,240,0.950,bicubic
resnetv2_152x2_bit.goog_in21k_ft_in1k,84.510,15.490,97.434,2.566,236.34,448,1.000,bilinear
resnest269e.in1k,84.510,15.490,96.992,3.008,110.93,416,0.928,bicubic
caformer_s36.sail_in1k,84.508,15.492,96.996,3.004,39.30,224,1.000,bicubic
convformer_m36.sail_in1k,84.494,15.506,96.866,3.134,57.05,224,1.000,bicubic
regnetz_040_h.ra3_in1k,84.492,15.508,97.010,2.990,28.94,320,1.000,bicubic
maxvit_rmlp_small_rw_224.sw_in1k,84.492,15.508,96.758,3.242,64.90,224,0.900,bicubic
hrnet_w48_ssld.paddle_in1k,84.478,15.522,97.234,2.766,77.47,288,1.000,bilinear
swin_base_patch4_window12_384.ms_in1k,84.476,15.524,96.892,3.108,87.90,384,1.000,bicubic
convnext_tiny.in12k_ft_in1k,84.450,15.550,97.340,2.660,28.59,288,1.000,bicubic
mvitv2_base.fb_in1k,84.450,15.550,96.858,3.142,51.47,224,0.900,bicubic
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,84.446,15.554,97.208,2.792,38.86,256,0.950,bicubic
gcvit_base.in1k,84.446,15.554,96.844,3.156,90.32,224,0.875,bicubic
resnetrs200.tf_in1k,84.444,15.556,97.082,2.918,93.21,320,1.000,bicubic
resnetv2_101x3_bit.goog_in21k_ft_in1k,84.438,15.562,97.382,2.618,387.93,448,1.000,bilinear
regnety_1280.seer_ft_in1k,84.432,15.568,97.092,2.908,644.81,384,1.000,bicubic
convnext_base.fb_in1k,84.428,15.572,96.968,3.032,88.59,288,1.000,bicubic
resnetrs270.tf_in1k,84.428,15.572,96.968,3.032,129.86,352,1.000,bicubic
maxvit_small_tf_224.in1k,84.426,15.574,96.824,3.176,68.93,224,0.950,bicubic
vit_large_r50_s32_224.augreg_in21k_ft_in1k,84.422,15.578,97.170,2.830,328.99,224,0.900,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k,84.416,15.584,97.260,2.740,28.64,288,1.000,bicubic
tf_efficientnet_b7.aa_in1k,84.414,15.586,96.906,3.094,66.35,600,0.949,bicubic
flexivit_base.300ep_in1k,84.406,15.594,96.886,3.114,86.59,240,0.950,bicubic
convformer_s18.sail_in1k_384,84.402,15.598,97.112,2.888,26.77,384,1.000,bicubic
resmlp_big_24_224.fb_in22k_ft_in1k,84.398,15.602,97.112,2.888,129.14,224,0.875,bicubic
xcit_large_24_p8_224.fb_in1k,84.394,15.606,96.664,3.336,188.93,224,1.000,bicubic
seresnet152d.ra2_in1k,84.360,15.640,97.040,2.960,66.84,320,1.000,bicubic
seresnext101d_32x8d.ah_in1k,84.358,15.642,96.920,3.080,93.59,288,1.000,bicubic
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,84.302,15.698,97.176,2.824,88.79,224,0.875,bilinear
tf_efficientnetv2_s.in21k_ft_in1k,84.288,15.712,97.252,2.748,21.46,384,1.000,bicubic
xcit_medium_24_p16_224.fb_dist_in1k,84.286,15.714,96.932,3.068,84.40,224,1.000,bicubic
vit_base_patch16_224_miil.in21k_ft_in1k,84.266,15.734,96.804,3.196,86.54,224,0.875,bilinear
tf_efficientnet_b5.ap_in1k,84.258,15.742,96.974,3.026,30.39,456,0.934,bicubic
davit_small.msft_in1k,84.252,15.748,96.940,3.060,49.75,224,0.950,bicubic
swinv2_base_window8_256.ms_in1k,84.250,15.750,96.924,3.076,87.92,256,0.900,bicubic
regnetz_040.ra3_in1k,84.240,15.760,96.932,3.068,27.12,320,1.000,bicubic
xcit_small_12_p8_224.fb_dist_in1k,84.236,15.764,96.872,3.128,26.21,224,1.000,bicubic
swinv2_small_window16_256.ms_in1k,84.224,15.776,96.868,3.132,49.73,256,0.900,bicubic
maxvit_rmlp_tiny_rw_256.sw_in1k,84.224,15.776,96.778,3.222,29.15,256,0.950,bicubic
vit_base_patch16_384.orig_in21k_ft_in1k,84.200,15.800,97.220,2.780,86.86,384,1.000,bicubic
crossvit_18_dagger_408.in1k,84.198,15.802,96.816,3.184,44.61,408,1.000,bicubic
seresnext101_32x8d.ah_in1k,84.186,15.814,96.874,3.126,93.57,288,1.000,bicubic
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,84.166,15.834,97.198,2.802,194.03,224,0.875,bilinear
volo_d1_224.sail_in1k,84.162,15.838,96.776,3.224,26.63,224,0.960,bicubic
regnetz_d8_evos.ch_in1k,84.126,15.874,97.012,2.988,23.46,320,1.000,bicubic
resnetaa101d.sw_in12k_ft_in1k,84.124,15.876,97.106,2.894,44.57,288,1.000,bicubic
tf_efficientnet_b6.aa_in1k,84.116,15.884,96.880,3.120,43.04,528,0.942,bicubic
convnext_tiny.fb_in22k_ft_in1k_384,84.088,15.912,97.144,2.856,28.59,384,1.000,bicubic
caformer_s18.sail_in22k_ft_in1k,84.076,15.924,97.198,2.802,26.34,224,1.000,bicubic
cait_xs24_384.fb_dist_in1k,84.062,15.938,96.884,3.116,26.67,384,1.000,bicubic
convformer_s36.sail_in1k,84.060,15.940,96.746,3.254,40.01,224,1.000,bicubic
tf_efficientnet_b3.ns_jft_in1k,84.056,15.944,96.918,3.082,12.23,300,0.904,bicubic
edgenext_base.in21k_ft_in1k,84.054,15.946,97.196,2.804,18.51,320,1.000,bicubic
regnetz_d8.ra3_in1k,84.054,15.946,96.996,3.004,23.37,320,1.000,bicubic
vit_small_r26_s32_384.augreg_in21k_ft_in1k,84.048,15.952,97.328,2.672,36.47,384,1.000,bicubic
regnetz_d32.ra3_in1k,84.022,15.978,96.868,3.132,27.58,320,0.950,bicubic
resnetv2_50x3_bit.goog_in21k_ft_in1k,84.020,15.980,97.126,2.874,217.32,448,1.000,bilinear
eca_nfnet_l1.ra2_in1k,84.010,15.990,97.026,2.974,41.41,320,1.000,bicubic
resnet200d.ra2_in1k,83.964,16.036,96.824,3.176,64.69,320,1.000,bicubic
edgenext_base.usi_in1k,83.958,16.042,96.770,3.230,18.51,320,1.000,bicubic
regnety_080.ra3_in1k,83.926,16.074,96.890,3.110,39.18,288,1.000,bicubic
swin_s3_base_224.ms_in1k,83.920,16.080,96.672,3.328,71.13,224,0.900,bicubic
regnety_640.seer_ft_in1k,83.908,16.092,96.922,3.078,281.38,384,1.000,bicubic
tf_efficientnetv2_s.in1k,83.900,16.100,96.698,3.302,21.46,384,1.000,bicubic
tresnet_v2_l.miil_in21k_ft_in1k,83.900,16.100,96.494,3.506,46.17,224,0.875,bilinear
gcvit_small.in1k,83.892,16.108,96.658,3.342,51.09,224,0.875,bicubic
xcit_small_24_p16_224.fb_dist_in1k,83.874,16.126,96.736,3.264,47.67,224,1.000,bicubic
swinv2_small_window8_256.ms_in1k,83.854,16.146,96.644,3.356,49.73,256,0.900,bicubic
resnest200e.in1k,83.846,16.154,96.884,3.116,70.20,320,0.909,bicubic
crossvit_15_dagger_408.in1k,83.842,16.158,96.778,3.222,28.50,408,1.000,bicubic
focalnet_base_lrf.ms_in1k,83.838,16.162,96.608,3.392,88.75,224,0.900,bicubic
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,83.834,16.166,97.126,2.874,236.34,384,1.000,bicubic
xcit_small_24_p8_224.fb_in1k,83.834,16.166,96.632,3.368,47.63,224,1.000,bicubic
focalnet_base_srf.ms_in1k,83.818,16.182,96.682,3.318,88.15,224,0.900,bicubic
tf_efficientnet_b5.ra_in1k,83.810,16.190,96.754,3.246,30.39,456,0.934,bicubic
efficientnetv2_rw_s.ra2_in1k,83.808,16.192,96.732,3.268,23.94,384,1.000,bicubic
vit_small_patch16_384.augreg_in21k_ft_in1k,83.802,16.198,97.098,2.902,22.20,384,1.000,bicubic
deit3_base_patch16_224.fb_in1k,83.786,16.214,96.588,3.412,86.59,224,0.900,bicubic
regnety_160.swag_lc_in1k,83.782,16.218,97.280,2.720,83.59,224,0.965,bicubic
mvitv2_small.fb_in1k,83.770,16.230,96.576,3.424,34.87,224,0.900,bicubic
pit_b_distilled_224.in1k,83.766,16.234,96.466,3.534,74.79,224,0.900,bicubic
swin_s3_small_224.ms_in1k,83.756,16.244,96.452,3.548,49.74,224,0.900,bicubic
xcit_medium_24_p8_224.fb_in1k,83.748,16.252,96.400,3.600,84.32,224,1.000,bicubic
xcit_tiny_24_p8_384.fb_dist_in1k,83.746,16.254,96.710,3.290,12.11,384,1.000,bicubic
pvt_v2_b5.in1k,83.742,16.258,96.634,3.366,81.96,224,0.900,bicubic
convformer_s18.sail_in22k_ft_in1k,83.738,16.262,97.048,2.952,26.77,224,1.000,bicubic
regnety_064.ra3_in1k,83.720,16.280,96.722,3.278,30.58,288,1.000,bicubic
regnetv_064.ra3_in1k,83.716,16.284,96.744,3.256,30.58,288,1.000,bicubic
pvt_v2_b4.in1k,83.710,16.290,96.674,3.326,62.56,224,0.900,bicubic
resnetrs152.tf_in1k,83.702,16.298,96.612,3.388,86.62,320,1.000,bicubic
convnext_small.fb_in1k,83.700,16.300,96.808,3.192,50.22,288,1.000,bicubic
regnety_160.deit_in1k,83.690,16.310,96.782,3.218,83.59,288,1.000,bicubic
tf_efficientnet_b5.aa_in1k,83.688,16.312,96.712,3.288,30.39,456,0.934,bicubic
resnet152d.ra2_in1k,83.684,16.316,96.740,3.260,60.21,320,1.000,bicubic
twins_svt_large.in1k,83.678,16.322,96.590,3.410,99.27,224,0.900,bicubic
caformer_s18.sail_in1k,83.656,16.344,96.516,3.484,26.34,224,1.000,bicubic
efficientformerv2_l.snap_dist_in1k,83.632,16.368,96.560,3.440,26.32,224,0.950,bicubic
swin_base_patch4_window7_224.ms_in1k,83.606,16.394,96.452,3.548,87.77,224,0.900,bicubic
coat_lite_medium.in1k,83.600,16.400,96.728,3.272,44.57,224,0.900,bicubic
coatnet_1_rw_224.sw_in1k,83.596,16.404,96.382,3.618,41.72,224,0.950,bicubic
resmlp_big_24_224.fb_distilled_in1k,83.592,16.408,96.650,3.350,129.14,224,0.875,bicubic
cs3se_edgenet_x.c2ns_in1k,83.542,16.458,96.672,3.328,50.72,320,1.000,bicubic
nest_base_jx.goog_in1k,83.534,16.466,96.374,3.626,67.72,224,0.875,bicubic
maxvit_tiny_rw_224.sw_in1k,83.504,16.496,96.504,3.496,29.06,224,0.950,bicubic
swinv2_cr_small_ns_224.sw_in1k,83.498,16.502,96.484,3.516,49.70,224,0.900,bicubic
focalnet_small_lrf.ms_in1k,83.494,16.506,96.580,3.420,50.34,224,0.900,bicubic
dm_nfnet_f0.dm_in1k,83.486,16.514,96.568,3.432,71.49,256,0.900,bicubic
convnextv2_tiny.fcmae_ft_in1k,83.464,16.536,96.718,3.282,28.64,288,1.000,bicubic
resnet152.a1h_in1k,83.450,16.550,96.538,3.462,60.19,288,1.000,bicubic
cait_s24_224.fb_dist_in1k,83.442,16.558,96.574,3.426,46.92,224,1.000,bicubic
deit3_small_patch16_384.fb_in1k,83.430,16.570,96.674,3.326,22.21,384,1.000,bicubic
focalnet_small_srf.ms_in1k,83.416,16.584,96.438,3.562,49.89,224,0.900,bicubic
efficientnet_b4.ra2_in1k,83.414,16.586,96.598,3.402,19.34,384,1.000,bicubic
maxvit_tiny_tf_224.in1k,83.402,16.598,96.590,3.410,30.92,224,0.950,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k_384,83.400,16.600,96.582,3.418,18.45,384,1.000,bicubic
sequencer2d_l.in1k,83.394,16.606,96.496,3.504,54.30,224,0.875,bicubic
deit_base_distilled_patch16_224.fb_in1k,83.386,16.614,96.488,3.512,87.34,224,0.900,bicubic
gcvit_tiny.in1k,83.384,16.616,96.398,3.602,28.22,224,0.875,bicubic
efficientformer_l7.snap_dist_in1k,83.382,16.618,96.536,3.464,82.23,224,0.950,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k_384,83.374,16.626,96.744,3.256,15.62,384,1.000,bicubic
coatnet_rmlp_1_rw_224.sw_in1k,83.362,16.638,96.450,3.550,41.69,224,0.950,bicubic
vit_base_patch32_384.augreg_in21k_ft_in1k,83.350,16.650,96.838,3.162,88.30,384,1.000,bicubic
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,83.336,16.664,96.846,3.154,194.03,224,0.875,bilinear
regnety_320.seer_ft_in1k,83.334,16.666,96.706,3.294,145.05,384,1.000,bicubic
xcit_small_12_p8_224.fb_in1k,83.332,16.668,96.484,3.516,26.21,224,1.000,bicubic
xcit_small_12_p16_224.fb_dist_in1k,83.328,16.672,96.416,3.584,26.25,224,1.000,bicubic
swin_small_patch4_window7_224.ms_in22k_ft_in1k,83.298,16.702,96.964,3.036,49.61,224,0.900,bicubic
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,83.296,16.704,96.528,3.472,88.22,224,0.900,bicubic
tf_efficientnet_b4.ap_in1k,83.250,16.750,96.394,3.606,19.34,380,0.922,bicubic
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,83.232,16.768,96.758,3.242,44.18,224,0.875,bilinear
swin_small_patch4_window7_224.ms_in1k,83.208,16.792,96.316,3.684,49.61,224,0.900,bicubic
regnetv_040.ra3_in1k,83.198,16.802,96.658,3.342,20.64,288,1.000,bicubic
xception65.ra3_in1k,83.182,16.818,96.592,3.408,39.92,299,0.940,bicubic
tf_efficientnet_b5.in1k,83.176,16.824,96.536,3.464,30.39,456,0.934,bicubic
regnety_320.tv2_in1k,83.162,16.838,96.416,3.584,145.05,224,0.965,bicubic
resnext101_64x4d.c1_in1k,83.156,16.844,96.374,3.626,83.46,288,1.000,bicubic
rexnetr_200.sw_in12k_ft_in1k,83.136,16.864,96.636,3.364,16.52,288,1.000,bicubic
xception65p.ra3_in1k,83.136,16.864,96.484,3.516,39.82,299,0.940,bicubic
swinv2_cr_small_224.sw_in1k,83.136,16.864,96.108,3.892,49.70,224,0.900,bicubic
twins_pcpvt_large.in1k,83.130,16.870,96.602,3.398,60.99,224,0.900,bicubic
nest_small_jx.goog_in1k,83.124,16.876,96.320,3.680,38.35,224,0.875,bicubic
twins_svt_base.in1k,83.122,16.878,96.416,3.584,56.07,224,0.900,bicubic
pvt_v2_b3.in1k,83.120,16.880,96.554,3.446,45.24,224,0.900,bicubic
maxxvitv2_nano_rw_256.sw_in1k,83.110,16.890,96.324,3.676,23.70,256,0.950,bicubic
deit_base_patch16_384.fb_in1k,83.106,16.894,96.368,3.632,86.86,384,1.000,bicubic
deit3_medium_patch16_224.fb_in1k,83.086,16.914,96.296,3.704,38.85,224,0.900,bicubic
deit3_small_patch16_224.fb_in22k_ft_in1k,83.074,16.926,96.776,3.224,22.06,224,1.000,bicubic
tresnet_m.miil_in21k_ft_in1k,83.074,16.926,96.114,3.886,31.39,224,0.875,bilinear
tresnet_xl.miil_in1k_448,83.060,16.940,96.174,3.826,78.44,448,0.875,bilinear
regnety_040.ra3_in1k,83.044,16.956,96.498,3.502,20.65,288,1.000,bicubic
maxxvit_rmlp_nano_rw_256.sw_in1k,83.042,16.958,96.350,3.650,16.78,256,0.950,bicubic
tf_efficientnet_b4.aa_in1k,83.018,16.982,96.300,3.700,19.34,380,0.922,bicubic
resnet101d.ra2_in1k,83.016,16.984,96.452,3.548,44.57,320,1.000,bicubic
resnetv2_101.a1h_in1k,83.002,16.998,96.454,3.546,44.54,288,1.000,bicubic
resnext101_64x4d.tv_in1k,82.992,17.008,96.244,3.756,83.46,224,0.875,bilinear
convformer_s18.sail_in1k,82.986,17.014,96.250,3.750,26.77,224,1.000,bicubic
ecaresnet101d.miil_in1k,82.984,17.016,96.542,3.458,44.57,288,0.950,bicubic
maxvit_rmlp_nano_rw_256.sw_in1k,82.954,17.046,96.266,3.734,15.50,256,0.950,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k_384,82.938,17.062,96.426,3.574,14.25,384,1.000,bicubic
maxvit_nano_rw_256.sw_in1k,82.928,17.072,96.220,3.780,15.45,256,0.950,bicubic
xcit_large_24_p16_224.fb_in1k,82.900,17.100,95.886,4.114,189.10,224,1.000,bicubic
resnest101e.in1k,82.884,17.116,96.322,3.678,48.28,256,0.875,bilinear
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,82.876,17.124,96.582,3.418,236.34,224,0.875,bicubic
convnext_nano.in12k_ft_in1k,82.862,17.138,96.556,3.444,15.59,288,1.000,bicubic
resnext101_32x8d.tv2_in1k,82.832,17.168,96.232,3.768,88.79,224,0.965,bilinear
resnetv2_50x1_bit.goog_distilled_in1k,82.820,17.180,96.514,3.486,25.55,224,0.875,bicubic
sequencer2d_m.in1k,82.812,17.188,96.274,3.726,38.31,224,0.875,bicubic
regnetx_320.tv2_in1k,82.810,17.190,96.208,3.792,107.81,224,0.965,bicubic
swinv2_tiny_window16_256.ms_in1k,82.804,17.196,96.236,3.764,28.35,256,0.900,bicubic
pnasnet5large.tf_in1k,82.782,17.218,96.040,3.960,86.06,331,0.911,bicubic
resnet101.a1h_in1k,82.778,17.222,96.310,3.690,44.55,288,1.000,bicubic
rexnet_300.nav_in1k,82.774,17.226,96.236,3.764,34.71,224,0.875,bicubic
vit_relpos_base_patch16_clsgap_224.sw_in1k,82.760,17.240,96.172,3.828,86.43,224,0.900,bicubic
nfnet_l0.ra2_in1k,82.752,17.248,96.516,3.484,35.07,288,1.000,bicubic
resnet152.a1_in1k,82.732,17.268,95.720,4.280,60.19,288,1.000,bicubic
regnety_032.ra_in1k,82.728,17.272,96.416,3.584,19.44,288,1.000,bicubic
twins_pcpvt_base.in1k,82.710,17.290,96.346,3.654,43.83,224,0.900,bicubic
cs3edgenet_x.c2_in1k,82.708,17.292,96.370,3.630,47.82,288,1.000,bicubic
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,82.698,17.302,96.632,3.368,88.79,224,0.875,bilinear
convnext_tiny.fb_in1k,82.698,17.302,96.144,3.856,28.59,288,1.000,bicubic
davit_tiny.msft_in1k,82.696,17.304,96.274,3.726,28.36,224,0.950,bicubic
tf_efficientnetv2_b3.in21k_ft_in1k,82.670,17.330,96.626,3.374,14.36,300,0.900,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k,82.664,17.336,96.520,3.480,15.62,288,1.000,bicubic
cs3sedarknet_x.c2ns_in1k,82.658,17.342,96.350,3.650,35.40,288,1.000,bicubic
regnety_160.tv2_in1k,82.642,17.358,96.216,3.784,83.59,224,0.965,bicubic
xcit_medium_24_p16_224.fb_in1k,82.640,17.360,95.984,4.016,84.40,224,1.000,bicubic
regnetz_c16.ra3_in1k,82.634,17.366,96.322,3.678,13.46,320,1.000,bicubic
regnetz_c16_evos.ch_in1k,82.632,17.368,96.474,3.526,13.49,320,0.950,bicubic
nasnetalarge.tf_in1k,82.626,17.374,96.042,3.958,88.75,331,0.911,bicubic
poolformerv2_m48.sail_in1k,82.620,17.380,96.072,3.928,73.35,224,1.000,bicubic
tf_efficientnet_b4.in1k,82.608,17.392,96.128,3.872,19.34,380,0.922,bicubic
resnet152.a2_in1k,82.608,17.392,95.752,4.248,60.19,288,1.000,bicubic
resnetaa50d.sw_in12k_ft_in1k,82.600,17.400,96.498,3.502,25.58,288,1.000,bicubic
levit_384.fb_dist_in1k,82.596,17.404,96.018,3.982,39.13,224,0.900,bicubic
regnety_080_tv.tv2_in1k,82.590,17.410,96.248,3.752,39.38,224,0.965,bicubic
levit_conv_384.fb_dist_in1k,82.590,17.410,96.018,3.982,39.13,224,0.900,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k_384,82.586,17.414,96.314,3.686,10.59,384,1.000,bicubic
convnext_tiny_hnf.a2h_in1k,82.584,17.416,96.008,3.992,28.59,288,1.000,bicubic
vit_base_patch32_clip_224.laion2b_ft_in1k,82.582,17.418,96.200,3.800,88.22,224,0.900,bicubic
xcit_small_24_p16_224.fb_in1k,82.580,17.420,96.012,3.988,47.67,224,1.000,bicubic
eca_nfnet_l0.ra2_in1k,82.578,17.422,96.494,3.506,24.14,288,1.000,bicubic
vit_relpos_medium_patch16_cls_224.sw_in1k,82.572,17.428,96.068,3.932,38.76,224,0.900,bicubic
xcit_tiny_24_p16_384.fb_dist_in1k,82.570,17.430,96.276,3.724,12.12,384,1.000,bicubic
xcit_tiny_24_p8_224.fb_dist_in1k,82.566,17.434,96.172,3.828,12.11,224,1.000,bicubic
regnetx_160.tv2_in1k,82.566,17.434,96.058,3.942,54.28,224,0.965,bicubic
efficientformer_l3.snap_dist_in1k,82.550,17.450,96.248,3.752,31.41,224,0.950,bicubic
resnet61q.ra2_in1k,82.524,17.476,96.130,3.870,36.85,288,1.000,bicubic
flexivit_small.1200ep_in1k,82.520,17.480,96.124,3.876,22.06,240,0.950,bicubic
crossvit_18_dagger_240.in1k,82.516,17.484,96.066,3.934,44.27,240,0.875,bicubic
wide_resnet101_2.tv2_in1k,82.498,17.502,96.018,3.982,126.89,224,0.965,bilinear
vit_relpos_base_patch16_224.sw_in1k,82.496,17.504,96.138,3.862,86.43,224,0.900,bicubic
convnextv2_nano.fcmae_ft_in1k,82.486,17.514,96.226,3.774,15.62,288,1.000,bicubic
poolformer_m48.sail_in1k,82.482,17.518,95.966,4.034,73.47,224,0.950,bicubic
vit_relpos_medium_patch16_224.sw_in1k,82.462,17.538,96.082,3.918,38.75,224,0.900,bicubic
gc_efficientnetv2_rw_t.agc_in1k,82.452,17.548,96.292,3.708,13.68,288,1.000,bicubic
pit_b_224.in1k,82.438,17.562,95.716,4.284,73.76,224,0.900,bicubic
mvitv2_tiny.fb_in1k,82.410,17.590,96.152,3.848,24.17,224,0.900,bicubic
coatnet_bn_0_rw_224.sw_in1k,82.400,17.600,96.186,3.814,27.44,224,0.950,bicubic
crossvit_18_240.in1k,82.396,17.604,96.060,3.940,43.27,240,0.875,bicubic
coatnet_0_rw_224.sw_in1k,82.390,17.610,95.836,4.164,27.44,224,0.950,bicubic
xcit_tiny_12_p8_384.fb_dist_in1k,82.388,17.612,96.220,3.780,6.71,384,1.000,bicubic
tf_efficientnet_b2.ns_jft_in1k,82.378,17.622,96.254,3.746,9.11,260,0.890,bicubic
coat_small.in1k,82.362,17.638,96.208,3.792,21.69,224,0.900,bicubic
flexivit_small.600ep_in1k,82.362,17.638,96.082,3.918,22.06,240,0.950,bicubic
resnet51q.ra2_in1k,82.360,17.640,96.182,3.818,35.70,288,1.000,bilinear
ecaresnet50t.ra2_in1k,82.352,17.648,96.140,3.860,25.57,320,0.950,bicubic
efficientnetv2_rw_t.ra2_in1k,82.350,17.650,96.192,3.808,13.65,288,1.000,bicubic
resnetv2_101x1_bit.goog_in21k_ft_in1k,82.342,17.658,96.520,3.480,44.54,448,1.000,bilinear
sequencer2d_s.in1k,82.340,17.660,96.030,3.970,27.65,224,0.875,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k,82.332,17.668,95.942,4.058,18.45,256,0.888,bicubic
crossvit_15_dagger_240.in1k,82.330,17.670,95.960,4.040,28.21,240,0.875,bicubic
resnet101.a1_in1k,82.322,17.678,95.632,4.368,44.55,288,1.000,bicubic
coat_lite_small.in1k,82.314,17.686,95.848,4.152,19.84,224,0.900,bicubic
vit_relpos_medium_patch16_rpn_224.sw_in1k,82.310,17.690,95.972,4.028,38.73,224,0.900,bicubic
mixer_b16_224.miil_in21k_ft_in1k,82.306,17.694,95.720,4.280,59.88,224,0.875,bilinear
convit_base.fb_in1k,82.290,17.710,95.936,4.064,86.54,224,0.875,bicubic
resnet152.tv2_in1k,82.288,17.712,96.004,3.996,60.19,224,0.965,bilinear
resnetrs101.tf_in1k,82.284,17.716,96.014,3.986,63.62,288,0.940,bicubic
wide_resnet50_2.racm_in1k,82.280,17.720,96.064,3.936,68.88,288,0.950,bicubic
tresnet_l.miil_in1k_448,82.276,17.724,95.978,4.022,55.99,448,0.875,bilinear
efficientnet_b3.ra2_in1k,82.242,17.758,96.118,3.882,12.23,320,1.000,bicubic
vit_srelpos_medium_patch16_224.sw_in1k,82.240,17.760,95.942,4.058,38.74,224,0.900,bicubic
resnet101.a2_in1k,82.236,17.764,95.730,4.270,44.55,288,1.000,bicubic
cs3darknet_x.c2ns_in1k,82.222,17.778,96.230,3.770,35.05,288,1.000,bicubic
poolformerv2_m36.sail_in1k,82.216,17.784,95.924,4.076,56.08,224,1.000,bicubic
crossvit_base_240.in1k,82.216,17.784,95.832,4.168,105.03,240,0.875,bicubic
vit_base_patch16_rpn_224.sw_in1k,82.206,17.794,95.998,4.002,86.54,224,0.900,bicubic
cait_xxs36_384.fb_dist_in1k,82.204,17.796,96.144,3.856,17.37,384,1.000,bicubic
seresnext50_32x4d.racm_in1k,82.196,17.804,96.148,3.852,27.56,288,0.950,bicubic
pvt_v2_b2_li.in1k,82.194,17.806,96.092,3.908,22.55,224,0.900,bicubic
flexivit_small.300ep_in1k,82.178,17.822,96.036,3.964,22.06,240,0.950,bicubic
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,82.174,17.826,96.222,3.778,25.03,224,0.875,bilinear
efficientformerv2_s2.snap_dist_in1k,82.166,17.834,95.910,4.090,12.71,224,0.950,bicubic
focalnet_tiny_lrf.ms_in1k,82.154,17.846,95.948,4.052,28.65,224,0.900,bicubic
swin_s3_tiny_224.ms_in1k,82.144,17.856,95.954,4.046,28.33,224,0.900,bicubic
focalnet_tiny_srf.ms_in1k,82.134,17.866,95.968,4.032,28.43,224,0.900,bicubic
ecaresnet50t.a1_in1k,82.128,17.872,95.642,4.358,25.57,288,1.000,bicubic
visformer_small.in1k,82.102,17.898,95.878,4.122,40.22,224,0.900,bicubic
poolformer_m36.sail_in1k,82.102,17.898,95.698,4.302,56.17,224,0.950,bicubic
pvt_v2_b2.in1k,82.086,17.914,95.956,4.044,25.36,224,0.900,bicubic
tresnet_xl.miil_in1k,82.074,17.926,95.926,4.074,78.44,224,0.875,bilinear
halo2botnet50ts_256.a1h_in1k,82.062,17.938,95.634,4.366,22.64,256,0.950,bicubic
coatnet_rmlp_nano_rw_224.sw_in1k,82.050,17.950,95.880,4.120,15.15,224,0.900,bicubic
hrnet_w18_ssld.paddle_in1k,82.046,17.954,96.250,3.750,21.30,288,1.000,bilinear
fbnetv3_g.ra2_in1k,82.036,17.964,96.058,3.942,16.62,288,0.950,bilinear
resnext50_32x4d.a1h_in1k,82.014,17.986,95.934,4.066,25.03,288,1.000,bicubic
resnetv2_50d_evos.ah_in1k,82.000,18.000,95.900,4.100,25.59,288,1.000,bicubic
ecaresnet101d_pruned.miil_in1k,81.998,18.002,96.160,3.840,24.88,288,0.950,bicubic
deit_base_patch16_224.fb_in1k,81.992,18.008,95.736,4.264,86.57,224,0.900,bicubic
xception41p.ra3_in1k,81.972,18.028,95.796,4.204,26.91,299,0.940,bicubic
xcit_small_12_p16_224.fb_in1k,81.970,18.030,95.812,4.188,26.25,224,1.000,bicubic
tf_efficientnetv2_b3.in1k,81.970,18.030,95.788,4.212,14.36,300,0.904,bicubic
resnetv2_50d_gn.ah_in1k,81.958,18.042,95.928,4.072,25.57,288,1.000,bicubic
gcvit_xtiny.in1k,81.954,18.046,95.968,4.032,19.98,224,0.875,bicubic
coatnext_nano_rw_224.sw_in1k,81.942,18.058,95.916,4.084,14.70,224,0.900,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k,81.938,18.062,95.790,4.210,14.25,256,0.888,bicubic
vit_base_patch32_clip_224.openai_ft_in1k,81.930,18.070,95.966,4.034,88.22,224,0.900,bicubic
xcit_tiny_24_p8_224.fb_in1k,81.892,18.108,95.970,4.030,12.11,224,1.000,bicubic
resnet101.tv2_in1k,81.890,18.110,95.768,4.232,44.55,224,0.965,bilinear
vit_small_r26_s32_224.augreg_in21k_ft_in1k,81.864,18.136,96.022,3.978,36.43,224,0.900,bicubic
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,81.842,18.158,96.092,3.908,194.03,224,0.875,bilinear
tf_efficientnet_b3.ap_in1k,81.826,18.174,95.626,4.374,12.23,300,0.904,bicubic
swinv2_tiny_window8_256.ms_in1k,81.820,18.180,95.994,4.006,28.35,256,0.900,bicubic
pit_s_distilled_224.in1k,81.808,18.192,95.728,4.272,24.04,224,0.900,bicubic
swinv2_cr_tiny_ns_224.sw_in1k,81.802,18.198,95.818,4.182,28.33,224,0.900,bicubic
vit_base_patch16_224.orig_in21k_ft_in1k,81.786,18.214,96.124,3.876,86.57,224,0.900,bicubic
cs3sedarknet_l.c2ns_in1k,81.784,18.216,95.958,4.042,21.91,288,0.950,bicubic
regnety_032.tv2_in1k,81.756,18.244,95.840,4.160,19.44,224,0.965,bicubic
tresnet_m.miil_in1k_448,81.708,18.292,95.574,4.426,31.39,448,0.875,bilinear
coatnet_nano_rw_224.sw_in1k,81.698,18.302,95.646,4.354,15.14,224,0.900,bicubic
twins_svt_small.in1k,81.678,18.322,95.660,4.340,24.06,224,0.900,bicubic
halonet50ts.a1h_in1k,81.660,18.340,95.610,4.390,22.73,256,0.940,bicubic
ecaresnet50t.a2_in1k,81.658,18.342,95.552,4.448,25.57,288,1.000,bicubic
ecaresnet50d.miil_in1k,81.650,18.350,95.882,4.118,25.58,288,0.950,bicubic
tf_efficientnet_b3.aa_in1k,81.644,18.356,95.722,4.278,12.23,300,0.904,bicubic
rexnet_200.nav_in1k,81.636,18.364,95.664,4.336,16.37,224,0.875,bicubic
resnetaa50.a1h_in1k,81.614,18.386,95.802,4.198,25.56,288,1.000,bicubic
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,81.606,18.394,96.040,3.960,88.79,224,0.875,bilinear
wide_resnet50_2.tv2_in1k,81.606,18.394,95.762,4.238,68.88,224,0.965,bilinear
convnext_nano_ols.d1h_in1k,81.600,18.400,95.636,4.364,15.65,288,1.000,bicubic
poolformerv2_s36.sail_in1k,81.568,18.432,95.690,4.310,30.79,224,1.000,bicubic
edgenext_small.usi_in1k,81.564,18.436,95.712,4.288,5.59,320,1.000,bicubic
lamhalobotnet50ts_256.a1h_in1k,81.554,18.446,95.490,4.510,22.57,256,0.950,bicubic
regnetx_080.tv2_in1k,81.538,18.462,95.542,4.458,39.57,224,0.965,bicubic
tnt_s_patch16_224,81.536,18.464,95.736,4.264,23.76,224,0.900,bicubic
crossvit_15_240.in1k,81.534,18.466,95.692,4.308,27.53,240,0.875,bicubic
tf_efficientnet_lite4.in1k,81.532,18.468,95.664,4.336,13.01,380,0.920,bilinear
levit_conv_256.fb_dist_in1k,81.520,18.480,95.490,4.510,18.89,224,0.900,bicubic
levit_256.fb_dist_in1k,81.514,18.486,95.492,4.508,18.89,224,0.900,bicubic
vit_large_patch32_384.orig_in21k_ft_in1k,81.510,18.490,96.090,3.910,306.63,384,1.000,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k,81.488,18.512,95.668,4.332,10.59,256,0.888,bicubic
convnext_nano.d1h_in1k,81.482,18.518,95.658,4.342,15.59,288,1.000,bicubic
tresnet_l.miil_in1k,81.480,18.520,95.624,4.376,55.99,224,0.875,bilinear
resnext50_32x4d.a1_in1k,81.466,18.534,95.174,4.826,25.03,288,1.000,bicubic
vit_relpos_small_patch16_224.sw_in1k,81.462,18.538,95.820,4.180,21.98,224,0.900,bicubic
gcresnet50t.ra2_in1k,81.456,18.544,95.718,4.282,25.90,288,1.000,bicubic
resnet50d.a1_in1k,81.450,18.550,95.218,4.782,25.58,288,1.000,bicubic
poolformer_s36.sail_in1k,81.430,18.570,95.444,4.556,30.86,224,0.900,bicubic
nest_tiny_jx.goog_in1k,81.426,18.574,95.618,4.382,17.06,224,0.875,bicubic
convit_small.fb_in1k,81.420,18.580,95.744,4.256,27.78,224,0.875,bicubic
ecaresnetlight.miil_in1k,81.408,18.592,95.816,4.184,30.16,288,0.950,bicubic
resnetv2_50.a1h_in1k,81.398,18.602,95.726,4.274,25.55,288,1.000,bicubic
tf_efficientnet_b1.ns_jft_in1k,81.394,18.606,95.736,4.264,7.79,240,0.882,bicubic
vit_small_patch16_224.augreg_in21k_ft_in1k,81.384,18.616,96.136,3.864,22.05,224,0.900,bicubic
swin_tiny_patch4_window7_224.ms_in1k,81.376,18.624,95.544,4.456,28.29,224,0.900,bicubic
deit3_small_patch16_224.fb_in1k,81.370,18.630,95.456,4.544,22.06,224,0.900,bicubic
convmixer_1536_20.in1k,81.362,18.638,95.614,4.386,51.63,224,0.960,bicubic
resnet50d.ra2_in1k,81.356,18.644,95.738,4.262,25.58,288,0.950,bicubic
gernet_l.idstcv_in1k,81.354,18.646,95.530,4.470,31.08,256,0.875,bilinear
efficientnet_el.ra_in1k,81.312,18.688,95.536,4.464,10.59,300,0.904,bicubic
legacy_senet154.in1k,81.308,18.692,95.492,4.508,115.09,224,0.875,bilinear
resnext50_32x4d.a2_in1k,81.304,18.696,95.096,4.904,25.03,288,1.000,bicubic
seresnet50.ra2_in1k,81.284,18.716,95.652,4.348,28.09,288,0.950,bicubic
coat_mini.in1k,81.270,18.730,95.382,4.618,10.34,224,0.900,bicubic
gcresnext50ts.ch_in1k,81.232,18.768,95.544,4.456,15.67,288,1.000,bicubic
senet154.gluon_in1k,81.226,18.774,95.358,4.642,115.09,224,0.875,bicubic
res2net101d.in1k,81.224,18.776,95.352,4.648,45.23,224,0.875,bilinear
deit_small_distilled_patch16_224.fb_in1k,81.218,18.782,95.384,4.616,22.44,224,0.900,bicubic
resnet50_gn.a1h_in1k,81.216,18.784,95.628,4.372,25.56,288,0.950,bicubic
resnet50.a1_in1k,81.214,18.786,95.102,4.898,25.56,288,1.000,bicubic
xcit_tiny_12_p8_224.fb_dist_in1k,81.212,18.788,95.602,4.398,6.71,224,1.000,bicubic
resnet50.fb_swsl_ig1b_ft_in1k,81.182,18.818,95.992,4.008,25.56,224,0.875,bilinear
resnext50_32x4d.tv2_in1k,81.182,18.818,95.340,4.660,25.03,224,0.965,bilinear
sebotnet33ts_256.a1h_in1k,81.166,18.834,95.168,4.832,13.70,256,0.940,bicubic
resnet50d.a2_in1k,81.164,18.836,95.084,4.916,25.58,288,1.000,bicubic
lambda_resnet50ts.a1h_in1k,81.156,18.844,95.098,4.902,21.54,256,0.950,bicubic
resmlp_36_224.fb_distilled_in1k,81.148,18.852,95.478,4.522,44.69,224,0.875,bicubic
mobilevitv2_200.cvnets_in1k,81.134,18.866,95.362,4.638,18.45,256,0.888,bicubic
resnest50d_4s2x40d.in1k,81.120,18.880,95.560,4.440,30.42,224,0.875,bicubic
vit_small_patch16_384.augreg_in1k,81.116,18.884,95.574,4.426,22.20,384,1.000,bicubic
seresnet50.a2_in1k,81.106,18.894,95.222,4.778,28.09,288,1.000,bicubic
vit_base_patch16_384.augreg_in1k,81.102,18.898,95.330,4.670,86.86,384,1.000,bicubic
seresnet50.a1_in1k,81.102,18.898,95.120,4.880,28.09,288,1.000,bicubic
twins_pcpvt_small.in1k,81.096,18.904,95.648,4.352,24.11,224,0.900,bicubic
vit_srelpos_small_patch16_224.sw_in1k,81.092,18.908,95.570,4.430,21.97,224,0.900,bicubic
pit_s_224.in1k,81.092,18.908,95.332,4.668,23.46,224,0.900,bicubic
convnextv2_pico.fcmae_ft_in1k,81.086,18.914,95.480,4.520,9.07,288,0.950,bicubic
haloregnetz_b.ra3_in1k,81.048,18.952,95.200,4.800,11.68,224,0.940,bicubic
resmlp_big_24_224.fb_in1k,81.036,18.964,95.018,4.982,129.14,224,0.875,bicubic
crossvit_small_240.in1k,81.016,18.984,95.456,4.544,26.86,240,0.875,bicubic
resnet152s.gluon_in1k,81.002,18.998,95.410,4.590,60.32,224,0.875,bicubic
resnest50d_1s4x24d.in1k,80.988,19.012,95.326,4.674,25.68,224,0.875,bicubic
cait_xxs24_384.fb_dist_in1k,80.972,19.028,95.640,4.360,12.03,384,1.000,bicubic
resnet50.d_in1k,80.972,19.028,95.430,4.570,25.56,288,1.000,bicubic
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,80.968,19.032,96.014,3.986,28.29,224,0.900,bicubic
resnest50d.in1k,80.964,19.036,95.384,4.616,27.48,224,0.875,bilinear
sehalonet33ts.ra2_in1k,80.956,19.044,95.276,4.724,13.69,256,0.940,bicubic
xcit_tiny_12_p16_384.fb_dist_in1k,80.938,19.062,95.414,4.586,6.72,384,1.000,bicubic
regnetx_032.tv2_in1k,80.926,19.074,95.278,4.722,15.30,224,0.965,bicubic
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,80.924,19.076,95.734,4.266,44.18,224,0.875,bilinear
resnet50.c1_in1k,80.912,19.088,95.552,4.448,25.56,288,1.000,bicubic
cs3darknet_l.c2ns_in1k,80.896,19.104,95.662,4.338,21.16,288,0.950,bicubic
seresnext101_64x4d.gluon_in1k,80.896,19.104,95.296,4.704,88.23,224,0.875,bicubic
seresnext101_32x4d.gluon_in1k,80.892,19.108,95.296,4.704,48.96,224,0.875,bicubic
cs3darknet_focus_l.c2ns_in1k,80.880,19.120,95.682,4.318,21.15,288,0.950,bicubic
tf_efficientnet_b3.in1k,80.878,19.122,95.300,4.700,12.23,300,0.904,bicubic
resnet50.c2_in1k,80.870,19.130,95.534,4.466,25.56,288,1.000,bicubic
mobilevitv2_175.cvnets_in1k,80.860,19.140,95.256,4.744,14.25,256,0.888,bicubic
efficientnet_b3_pruned.in1k,80.852,19.148,95.244,4.756,9.86,300,0.904,bicubic
resnet50.tv2_in1k,80.850,19.150,95.434,4.566,25.56,224,0.965,bilinear
regnety_320.pycls_in1k,80.808,19.192,95.238,4.762,145.05,224,0.875,bicubic
tresnet_m.miil_in1k,80.798,19.202,94.856,5.144,31.39,224,0.875,bilinear
ecaresnet50d_pruned.miil_in1k,80.790,19.210,95.570,4.430,19.94,288,0.950,bicubic
seresnet33ts.ra2_in1k,80.784,19.216,95.362,4.638,19.78,288,1.000,bicubic
resnet50.a2_in1k,80.772,19.228,94.988,5.012,25.56,288,1.000,bicubic
resmlp_24_224.fb_distilled_in1k,80.756,19.244,95.224,4.776,30.02,224,0.875,bicubic
poolformerv2_s24.sail_in1k,80.748,19.252,95.310,4.690,21.34,224,1.000,bicubic
gernet_m.idstcv_in1k,80.734,19.266,95.186,4.814,21.14,224,0.875,bilinear
regnetz_b16.ra3_in1k,80.728,19.272,95.520,4.480,9.72,288,1.000,bicubic
vit_base_patch32_224.augreg_in21k_ft_in1k,80.716,19.284,95.566,4.434,88.22,224,0.900,bicubic
resnet50.b1k_in1k,80.706,19.294,95.432,4.568,25.56,288,1.000,bicubic
resnext50_32x4d.ra_in1k,80.698,19.302,95.390,4.610,25.03,288,0.950,bicubic
resnet50.a1h_in1k,80.676,19.324,95.306,4.694,25.56,224,1.000,bicubic
eca_resnet33ts.ra2_in1k,80.672,19.328,95.364,4.636,19.68,288,1.000,bicubic
regnety_016.tv2_in1k,80.668,19.332,95.330,4.670,11.20,224,0.965,bicubic
resnext50d_32x4d.bt_in1k,80.664,19.336,95.420,4.580,25.05,288,0.950,bicubic
nf_resnet50.ra2_in1k,80.640,19.360,95.332,4.668,25.56,288,0.940,bicubic
eva02_tiny_patch14_336.mim_in22k_ft_in1k,80.630,19.370,95.526,4.474,5.76,336,1.000,bicubic
efficientnet_b2.ra_in1k,80.610,19.390,95.314,4.686,9.11,288,1.000,bicubic
gcresnet33ts.ra2_in1k,80.600,19.400,95.320,4.680,19.88,288,1.000,bicubic
resnext101_64x4d.gluon_in1k,80.600,19.400,94.992,5.008,83.46,224,0.875,bicubic
cspresnext50.ra_in1k,80.556,19.444,95.322,4.678,20.57,256,0.887,bilinear
resnet152.a3_in1k,80.546,19.454,95.000,5.000,60.19,224,0.950,bicubic
darknet53.c2ns_in1k,80.536,19.464,95.434,4.566,41.61,288,1.000,bicubic
maxvit_rmlp_pico_rw_256.sw_in1k,80.514,19.486,95.214,4.786,7.52,256,0.950,bicubic
repvgg_b3.rvgg_in1k,80.506,19.494,95.254,4.746,123.09,224,0.875,bilinear
darknetaa53.c2ns_in1k,80.502,19.498,95.322,4.678,36.02,288,1.000,bilinear
efficientformer_l1.snap_dist_in1k,80.498,19.502,94.988,5.012,12.29,224,0.950,bicubic
vit_small_patch32_384.augreg_in21k_ft_in1k,80.486,19.514,95.596,4.404,22.92,384,1.000,bicubic
mixnet_xl.ra_in1k,80.482,19.518,94.936,5.064,11.90,224,0.875,bicubic
resnet152d.gluon_in1k,80.476,19.524,95.202,4.798,60.21,224,0.875,bicubic
convnext_pico_ols.d1_in1k,80.464,19.536,95.246,4.754,9.06,288,1.000,bicubic
inception_resnet_v2.tf_in1k,80.462,19.538,95.308,4.692,55.84,299,0.897,bicubic
edgenext_small_rw.sw_in1k,80.458,19.542,95.190,4.810,7.83,320,1.000,bicubic
resnet50.b2k_in1k,80.454,19.546,95.318,4.682,25.56,288,1.000,bicubic
xcit_tiny_24_p16_224.fb_dist_in1k,80.454,19.546,95.218,4.782,12.12,224,1.000,bicubic
resnet101d.gluon_in1k,80.426,19.574,95.024,4.976,44.57,224,0.875,bicubic
convnext_pico.d1_in1k,80.414,19.586,95.050,4.950,9.05,288,0.950,bicubic
regnety_120.pycls_in1k,80.386,19.614,95.128,4.872,51.82,224,0.875,bicubic
mobilevitv2_150.cvnets_in1k,80.370,19.630,95.074,4.926,10.59,256,0.888,bicubic
ese_vovnet39b.ra_in1k,80.350,19.650,95.366,4.634,24.57,288,0.950,bicubic
resnetv2_50x1_bit.goog_in21k_ft_in1k,80.340,19.660,95.680,4.320,25.55,448,1.000,bilinear
resnext101_32x4d.gluon_in1k,80.340,19.660,94.930,5.070,44.18,224,0.875,bicubic
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,80.338,19.662,95.398,4.602,25.03,224,0.875,bilinear
rexnet_150.nav_in1k,80.324,19.676,95.174,4.826,9.73,224,0.875,bicubic
tf_efficientnet_b2.ap_in1k,80.308,19.692,95.028,4.972,9.11,260,0.890,bicubic
resnet101s.gluon_in1k,80.302,19.698,95.150,4.850,44.67,224,0.875,bicubic
efficientnet_el_pruned.in1k,80.300,19.700,95.222,4.778,10.59,300,0.904,bicubic
regnety_160.pycls_in1k,80.298,19.702,94.962,5.038,83.59,224,0.875,bicubic
poolformer_s24.sail_in1k,80.296,19.704,95.072,4.928,21.39,224,0.900,bicubic
res2net50d.in1k,80.254,19.746,95.036,4.964,25.72,224,0.875,bilinear
tf_efficientnet_el.in1k,80.252,19.748,95.118,4.882,10.59,300,0.904,bicubic
regnetx_320.pycls_in1k,80.246,19.754,95.022,4.978,107.81,224,0.875,bicubic
vit_base_patch16_224.sam_in1k,80.244,19.756,94.756,5.244,86.57,224,0.900,bicubic
resnetblur50.bt_in1k,80.234,19.766,95.234,4.766,25.56,288,0.950,bicubic
legacy_seresnext101_32x4d.in1k,80.230,19.770,95.026,4.974,48.96,224,0.875,bilinear
repvgg_b3g4.rvgg_in1k,80.216,19.784,95.092,4.908,83.83,224,0.875,bilinear
tf_efficientnetv2_b2.in1k,80.194,19.806,95.042,4.958,10.10,260,0.890,bicubic
convmixer_768_32.in1k,80.168,19.832,95.074,4.926,21.11,224,0.960,bicubic
dpn107.mx_in1k,80.168,19.832,94.942,5.058,86.92,224,0.875,bicubic
skresnext50_32x4d.ra_in1k,80.162,19.838,94.640,5.360,27.48,224,0.875,bicubic
inception_v4.tf_in1k,80.156,19.844,94.968,5.032,42.68,299,0.875,bicubic
tf_efficientnet_b2.aa_in1k,80.084,19.916,94.906,5.094,9.11,260,0.890,bicubic
cspdarknet53.ra_in1k,80.062,19.938,95.080,4.920,27.64,256,0.887,bilinear
dpn92.mx_in1k,80.038,19.962,94.860,5.140,37.67,224,0.875,bicubic
inception_resnet_v2.tf_ens_adv_in1k,79.978,20.022,94.946,5.054,55.84,299,0.897,bicubic
resnet50.ram_in1k,79.976,20.024,95.052,4.948,25.56,288,0.950,bicubic
efficientnet_b2_pruned.in1k,79.920,20.080,94.852,5.148,8.31,260,0.890,bicubic
resnet152c.gluon_in1k,79.910,20.090,94.844,5.156,60.21,224,0.875,bicubic
seresnext50_32x4d.gluon_in1k,79.910,20.090,94.824,5.176,27.56,224,0.875,bicubic
resnetrs50.tf_in1k,79.896,20.104,94.972,5.028,35.69,224,0.910,bicubic
xception71.tf_in1k,79.872,20.128,94.928,5.072,42.34,299,0.903,bicubic
regnety_080.pycls_in1k,79.868,20.132,94.832,5.168,39.18,224,0.875,bicubic
regnetx_160.pycls_in1k,79.866,20.134,94.828,5.172,54.28,224,0.875,bicubic
ecaresnet26t.ra2_in1k,79.850,20.150,95.090,4.910,16.01,320,0.950,bicubic
deit_small_patch16_224.fb_in1k,79.844,20.156,95.044,4.956,22.05,224,0.900,bicubic
levit_conv_192.fb_dist_in1k,79.838,20.162,94.784,5.216,10.95,224,0.900,bicubic
levit_192.fb_dist_in1k,79.838,20.162,94.778,5.222,10.95,224,0.900,bicubic
resnet50.ra_in1k,79.836,20.164,94.966,5.034,25.56,288,0.950,bicubic
dpn131.mx_in1k,79.814,20.186,94.700,5.300,79.25,224,0.875,bicubic
resnet101.a3_in1k,79.814,20.186,94.606,5.394,44.55,224,0.950,bicubic
tf_efficientnet_lite3.in1k,79.808,20.192,94.912,5.088,8.20,300,0.904,bilinear
resmlp_36_224.fb_in1k,79.772,20.228,94.884,5.116,44.69,224,0.875,bicubic
cait_xxs36_224.fb_dist_in1k,79.746,20.254,94.874,5.126,17.30,224,1.000,bicubic
gcvit_xxtiny.in1k,79.728,20.272,95.080,4.920,12.00,224,0.875,bicubic
resnet33ts.ra2_in1k,79.726,20.274,94.828,5.172,19.68,288,1.000,bicubic
regnety_064.pycls_in1k,79.714,20.286,94.766,5.234,30.58,224,0.875,bicubic
resnet152.gluon_in1k,79.694,20.306,94.736,5.264,60.19,224,0.875,bicubic
xcit_tiny_12_p8_224.fb_in1k,79.688,20.312,95.054,4.946,6.71,224,1.000,bicubic
efficientformerv2_s1.snap_dist_in1k,79.688,20.312,94.710,5.290,6.19,224,0.950,bicubic
fbnetv3_d.ra2_in1k,79.682,20.318,94.944,5.056,10.31,256,0.950,bilinear
mobilevitv2_125.cvnets_in1k,79.680,20.320,94.858,5.142,7.48,256,0.888,bicubic
dpn98.mx_in1k,79.670,20.330,94.654,5.346,61.57,224,0.875,bicubic
gmlp_s16_224.ra3_in1k,79.644,20.356,94.622,5.378,19.42,224,0.875,bicubic
resnet50.bt_in1k,79.640,20.360,94.892,5.108,25.56,288,0.950,bicubic
tf_efficientnet_b2.in1k,79.604,20.396,94.714,5.286,9.11,260,0.890,bicubic
cspresnet50.ra_in1k,79.590,20.410,94.712,5.288,21.62,256,0.887,bilinear
regnetx_120.pycls_in1k,79.588,20.412,94.742,5.258,46.11,224,0.875,bicubic
xception65.tf_in1k,79.556,20.444,94.654,5.346,39.92,299,0.903,bicubic
ecaresnet50t.a3_in1k,79.552,20.448,94.694,5.306,25.57,224,0.950,bicubic
resnet101c.gluon_in1k,79.542,20.458,94.586,5.414,44.57,224,0.875,bicubic
rexnet_130.nav_in1k,79.504,20.496,94.678,5.322,7.56,224,0.875,bicubic
eca_halonext26ts.c1_in1k,79.486,20.514,94.600,5.400,10.76,256,0.940,bicubic
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,79.484,20.516,94.138,5.862,119.42,256,0.900,bicubic
hrnet_w64.ms_in1k,79.474,20.526,94.646,5.354,128.06,224,0.875,bilinear
tf_efficientnetv2_b1.in1k,79.462,20.538,94.722,5.278,8.14,240,0.882,bicubic
xcit_tiny_24_p16_224.fb_in1k,79.448,20.552,94.878,5.122,12.12,224,1.000,bicubic
dla102x2.in1k,79.444,20.556,94.638,5.362,41.28,224,0.875,bilinear
regnetx_016.tv2_in1k,79.436,20.564,94.768,5.232,9.19,224,0.965,bicubic
resnet32ts.ra2_in1k,79.392,20.608,94.652,5.348,17.96,288,1.000,bicubic
repvgg_b2g4.rvgg_in1k,79.382,20.618,94.676,5.324,61.76,224,0.875,bilinear
resmlp_24_224.fb_in1k,79.374,20.626,94.546,5.454,30.02,224,0.875,bicubic
dpn68b.ra_in1k,79.362,20.638,94.438,5.562,12.61,288,1.000,bicubic
resnext50_32x4d.gluon_in1k,79.360,20.640,94.430,5.570,25.03,224,0.875,bicubic
convnextv2_femto.fcmae_ft_in1k,79.338,20.662,94.560,5.440,5.23,288,0.950,bicubic
resnet101.gluon_in1k,79.310,20.690,94.522,5.478,44.55,224,0.875,bicubic
resnext101_32x8d.tv_in1k,79.310,20.690,94.520,5.480,88.79,224,0.875,bilinear
nf_regnet_b1.ra2_in1k,79.306,20.694,94.740,5.260,10.22,288,0.900,bicubic
hrnet_w48.ms_in1k,79.302,20.698,94.516,5.484,77.47,224,0.875,bilinear
tf_efficientnet_cc_b1_8e.in1k,79.302,20.698,94.374,5.626,39.72,240,0.882,bicubic
tf_efficientnet_b1.ap_in1k,79.280,20.720,94.308,5.692,7.79,240,0.882,bicubic
eca_botnext26ts_256.c1_in1k,79.268,20.732,94.606,5.394,10.59,256,0.950,bicubic
resnext50_32x4d.a3_in1k,79.268,20.732,94.306,5.694,25.03,224,0.950,bicubic
botnet26t_256.c1_in1k,79.256,20.744,94.534,5.466,12.49,256,0.950,bicubic
efficientnet_em.ra2_in1k,79.240,20.760,94.794,5.206,6.90,240,0.882,bicubic
resnet50.fb_ssl_yfcc100m_ft_in1k,79.234,20.766,94.826,5.174,25.56,224,0.875,bilinear
regnety_040.pycls_in1k,79.232,20.768,94.656,5.344,20.65,224,0.875,bicubic
regnetx_080.pycls_in1k,79.202,20.798,94.552,5.448,39.57,224,0.875,bicubic
res2net101_26w_4s.in1k,79.202,20.798,94.436,5.564,45.21,224,0.875,bilinear
pit_xs_distilled_224.in1k,79.180,20.820,94.362,5.638,11.00,224,0.900,bicubic
vit_base_patch16_224.augreg_in1k,79.154,20.846,94.090,5.910,86.57,224,0.900,bicubic
fbnetv3_b.ra2_in1k,79.142,20.858,94.742,5.258,8.60,256,0.950,bilinear
halonet26t.a1h_in1k,79.106,20.894,94.306,5.694,12.48,256,0.950,bicubic
coat_lite_mini.in1k,79.102,20.898,94.608,5.392,11.01,224,0.900,bicubic
lambda_resnet26t.c1_in1k,79.088,20.912,94.590,5.410,10.96,256,0.940,bicubic
resnet50d.gluon_in1k,79.078,20.922,94.468,5.532,25.58,224,0.875,bicubic
legacy_seresnext50_32x4d.in1k,79.076,20.924,94.432,5.568,27.56,224,0.875,bilinear
regnetx_064.pycls_in1k,79.066,20.934,94.458,5.542,26.21,224,0.875,bicubic
legacy_xception.tf_in1k,79.040,20.960,94.382,5.618,22.86,299,0.897,bicubic
resnet50.am_in1k,78.998,21.002,94.396,5.604,25.56,224,0.875,bicubic
lambda_resnet26rpt_256.c1_in1k,78.968,21.032,94.438,5.562,10.99,256,0.940,bicubic
mixnet_l.ft_in1k,78.966,21.034,94.180,5.820,7.33,224,0.875,bicubic
res2net50_26w_8s.in1k,78.942,21.058,94.294,5.706,48.40,224,0.875,bilinear
hrnet_w40.ms_in1k,78.930,21.070,94.464,5.536,57.56,224,0.875,bilinear
convnext_femto_ols.d1_in1k,78.924,21.076,94.526,5.474,5.23,288,0.950,bicubic
convnext_tiny.fb_in22k_ft_in1k,78.898,21.102,94.674,5.326,28.59,288,1.000,bicubic
hrnet_w44.ms_in1k,78.892,21.108,94.364,5.636,67.06,224,0.875,bilinear
regnety_032.pycls_in1k,78.876,21.124,94.408,5.592,19.44,224,0.875,bicubic
vit_small_patch16_224.augreg_in1k,78.846,21.154,94.288,5.712,22.05,224,0.900,bicubic
wide_resnet101_2.tv_in1k,78.838,21.162,94.282,5.718,126.89,224,0.875,bilinear
tf_efficientnet_b1.aa_in1k,78.830,21.170,94.198,5.802,7.79,240,0.882,bicubic
seresnext26d_32x4d.bt_in1k,78.814,21.186,94.240,5.760,16.81,288,0.950,bicubic
inception_v3.gluon_in1k,78.806,21.194,94.374,5.626,23.83,299,0.875,bicubic
efficientnet_b1.ft_in1k,78.800,21.200,94.342,5.658,7.79,256,1.000,bicubic
repvgg_b2.rvgg_in1k,78.792,21.208,94.420,5.580,89.02,224,0.875,bilinear
tf_mixnet_l.in1k,78.776,21.224,94.002,5.998,7.33,224,0.875,bicubic
vit_base_patch32_384.augreg_in1k,78.752,21.248,94.224,5.776,88.30,384,1.000,bicubic
seresnext26t_32x4d.bt_in1k,78.744,21.256,94.312,5.688,16.81,288,0.950,bicubic
resnet50d.a3_in1k,78.722,21.278,94.236,5.764,25.58,224,0.950,bicubic
resnet50s.gluon_in1k,78.718,21.282,94.242,5.758,25.68,224,0.875,bicubic
convnext_femto.d1_in1k,78.716,21.284,94.430,5.570,5.22,288,0.950,bicubic
dla169.in1k,78.708,21.292,94.346,5.654,53.39,224,0.875,bilinear
pvt_v2_b1.in1k,78.702,21.298,94.502,5.498,14.01,224,0.900,bicubic
regnety_008_tv.tv2_in1k,78.674,21.326,94.386,5.614,6.43,224,0.965,bicubic
tf_efficientnet_b0.ns_jft_in1k,78.670,21.330,94.374,5.626,5.29,224,0.875,bicubic
legacy_seresnet152.in1k,78.660,21.340,94.370,5.630,66.82,224,0.875,bilinear
xcit_tiny_12_p16_224.fb_dist_in1k,78.574,21.426,94.198,5.802,6.72,224,1.000,bicubic
res2net50_26w_6s.in1k,78.568,21.432,94.122,5.878,37.05,224,0.875,bilinear
tf_efficientnet_b1.in1k,78.560,21.440,94.094,5.906,7.79,240,0.882,bicubic
xception41.tf_in1k,78.504,21.496,94.276,5.724,26.97,299,0.903,bicubic
dla102x.in1k,78.498,21.502,94.236,5.764,26.31,224,0.875,bilinear
levit_conv_128.fb_dist_in1k,78.494,21.506,94.008,5.992,9.21,224,0.900,bicubic
regnetx_040.pycls_in1k,78.492,21.508,94.242,5.758,22.12,224,0.875,bicubic
levit_128.fb_dist_in1k,78.490,21.510,94.012,5.988,9.21,224,0.900,bicubic
resnest26d.gluon_in1k,78.484,21.516,94.294,5.706,17.07,224,0.875,bilinear
wide_resnet50_2.tv_in1k,78.480,21.520,94.086,5.914,68.88,224,0.875,bilinear
dla60_res2net.in1k,78.462,21.538,94.202,5.798,20.85,224,0.875,bilinear
hrnet_w32.ms_in1k,78.444,21.556,94.188,5.812,41.23,224,0.875,bilinear
resnet34d.ra2_in1k,78.436,21.564,94.344,5.656,21.82,288,0.950,bicubic
dla60_res2next.in1k,78.434,21.566,94.144,5.856,17.03,224,0.875,bilinear
coat_tiny.in1k,78.426,21.574,94.048,5.952,5.50,224,0.900,bicubic
vit_tiny_patch16_384.augreg_in21k_ft_in1k,78.424,21.576,94.540,5.460,5.79,384,1.000,bicubic
SelecSls60b.in1k,78.414,21.586,94.170,5.830,32.77,224,0.875,bicubic
gcresnext26ts.ch_in1k,78.414,21.586,94.034,5.966,10.48,288,1.000,bicubic
legacy_seresnet101.in1k,78.386,21.614,94.262,5.738,49.33,224,0.875,bilinear
cait_xxs24_224.fb_dist_in1k,78.384,21.616,94.316,5.684,11.96,224,1.000,bicubic
repvgg_b1.rvgg_in1k,78.368,21.632,94.096,5.904,57.42,224,0.875,bilinear
tf_efficientnetv2_b0.in1k,78.358,21.642,94.014,5.986,7.14,224,0.875,bicubic
resnet26t.ra2_in1k,78.332,21.668,94.124,5.876,16.01,320,1.000,bicubic
resnet152.tv_in1k,78.322,21.678,94.048,5.952,60.19,224,0.875,bilinear
mobilevit_s.cvnets_in1k,78.312,21.688,94.144,5.856,5.58,256,0.900,bicubic
seresnext26ts.ch_in1k,78.272,21.728,94.094,5.906,10.39,288,1.000,bicubic
bat_resnext26ts.ch_in1k,78.252,21.748,94.098,5.902,10.73,256,0.900,bicubic
res2next50.in1k,78.242,21.758,93.892,6.108,24.67,224,0.875,bilinear
efficientnet_b1_pruned.in1k,78.242,21.758,93.836,6.164,6.33,240,0.882,bicubic
dla60x.in1k,78.240,21.760,94.034,5.966,17.35,224,0.875,bilinear
hrnet_w30.ms_in1k,78.192,21.808,94.222,5.778,37.71,224,0.875,bilinear
pit_xs_224.in1k,78.180,21.820,94.164,5.836,10.62,224,0.900,bicubic
regnetx_032.pycls_in1k,78.168,21.832,94.082,5.918,15.30,224,0.875,bicubic
visformer_tiny.in1k,78.162,21.838,94.164,5.836,10.32,224,0.900,bicubic
res2net50_14w_8s.in1k,78.158,21.842,93.846,6.154,25.06,224,0.875,bilinear
hrnet_w18.ms_aug_in1k,78.126,21.874,94.052,5.948,21.30,224,0.950,bilinear
tf_efficientnet_em.in1k,78.122,21.878,94.046,5.954,6.90,240,0.882,bicubic
hardcorenas_f.miil_green_in1k,78.098,21.902,93.806,6.194,8.20,224,0.875,bilinear
mobilevitv2_100.cvnets_in1k,78.076,21.924,94.170,5.830,4.90,256,0.888,bicubic
efficientnet_es.ra_in1k,78.054,21.946,93.924,6.076,5.44,224,0.875,bicubic
resnet50.a3_in1k,78.048,21.952,93.780,6.220,25.56,224,0.950,bicubic
gmixer_24_224.ra3_in1k,78.026,21.974,93.668,6.332,24.72,224,0.875,bicubic
dla102.in1k,78.022,21.978,93.932,6.068,33.27,224,0.875,bilinear
resnet50c.gluon_in1k,78.012,21.988,93.988,6.012,25.58,224,0.875,bicubic
poolformerv2_s12.sail_in1k,78.004,21.996,93.864,6.136,11.89,224,1.000,bicubic
eca_resnext26ts.ch_in1k,78.000,22.000,93.926,6.074,10.30,288,1.000,bicubic
SelecSls60.in1k,77.984,22.016,93.832,6.168,30.67,224,0.875,bicubic
resmlp_12_224.fb_distilled_in1k,77.954,22.046,93.560,6.440,15.35,224,0.875,bicubic
res2net50_26w_4s.in1k,77.950,22.050,93.852,6.148,25.70,224,0.875,bilinear
resnet34.a1_in1k,77.918,22.082,93.766,6.234,21.80,288,1.000,bicubic
mobilenetv3_large_100.miil_in21k_ft_in1k,77.918,22.082,92.914,7.086,5.48,224,0.875,bilinear
tf_efficientnet_cc_b0_8e.in1k,77.906,22.094,93.660,6.340,24.01,224,0.875,bicubic
rexnet_100.nav_in1k,77.862,22.138,93.866,6.134,4.80,224,0.875,bicubic
regnety_016.pycls_in1k,77.862,22.138,93.718,6.282,11.20,224,0.875,bicubic
inception_v3.tf_in1k,77.862,22.138,93.644,6.356,23.83,299,0.875,bicubic
xcit_nano_12_p8_384.fb_dist_in1k,77.820,22.180,94.040,5.960,3.05,384,1.000,bicubic
hardcorenas_e.miil_green_in1k,77.784,22.216,93.698,6.302,8.07,224,0.875,bilinear
convnextv2_atto.fcmae_ft_in1k,77.760,22.240,93.726,6.274,3.71,288,0.950,bicubic
ese_vovnet19b_dw.ra_in1k,77.744,22.256,93.788,6.212,6.54,288,0.950,bicubic
efficientnet_b0.ra_in1k,77.694,22.306,93.532,6.468,5.29,224,0.875,bicubic
tinynet_a.in1k,77.658,22.342,93.540,6.460,6.19,192,0.875,bicubic
legacy_seresnet50.in1k,77.644,22.356,93.758,6.242,28.09,224,0.875,bilinear
cs3darknet_m.c2ns_in1k,77.634,22.366,94.016,5.984,9.31,288,0.950,bicubic
resnext50_32x4d.tv_in1k,77.618,22.382,93.696,6.304,25.03,224,0.875,bilinear
inception_v3.tf_adv_in1k,77.592,22.408,93.730,6.270,23.83,299,0.875,bicubic
repvgg_b1g4.rvgg_in1k,77.582,22.418,93.836,6.164,39.97,224,0.875,bilinear
resnet50.gluon_in1k,77.580,22.420,93.718,6.282,25.56,224,0.875,bicubic
coat_lite_tiny.in1k,77.520,22.480,93.922,6.078,5.72,224,0.900,bicubic
dpn68b.mx_in1k,77.518,22.482,93.852,6.148,12.61,224,0.875,bicubic
res2net50_48w_2s.in1k,77.514,22.486,93.552,6.448,25.29,224,0.875,bilinear
tf_efficientnet_lite2.in1k,77.464,22.536,93.750,6.250,6.09,260,0.890,bicubic
hardcorenas_d.miil_green_in1k,77.440,22.560,93.490,6.510,7.50,224,0.875,bilinear
inception_v3.tv_in1k,77.434,22.566,93.474,6.526,23.83,299,0.875,bicubic
resnet26d.bt_in1k,77.408,22.592,93.638,6.362,16.01,288,0.950,bicubic
resnet101.tv_in1k,77.376,22.624,93.542,6.458,44.55,224,0.875,bilinear
densenet161.tv_in1k,77.358,22.642,93.642,6.358,28.68,224,0.875,bicubic
densenetblur121d.ra_in1k,77.320,22.680,93.790,6.210,8.00,288,0.950,bicubic
regnetx_008.tv2_in1k,77.304,22.696,93.666,6.334,7.26,224,0.965,bicubic
mobilenetv2_120d.ra_in1k,77.302,22.698,93.504,6.496,5.83,224,0.875,bicubic
tf_efficientnet_cc_b0_4e.in1k,77.302,22.698,93.336,6.664,13.31,224,0.875,bicubic
densenet201.tv_in1k,77.286,22.714,93.480,6.520,20.01,224,0.875,bicubic
cs3darknet_focus_m.c2ns_in1k,77.284,22.716,93.966,6.034,9.30,288,0.950,bicubic
mixnet_m.ft_in1k,77.258,22.742,93.418,6.582,5.01,224,0.875,bicubic
poolformer_s12.sail_in1k,77.234,22.766,93.532,6.468,11.92,224,0.900,bicubic
convnext_atto_ols.a2_in1k,77.216,22.784,93.676,6.324,3.70,288,0.950,bicubic
resnext26ts.ra2_in1k,77.174,22.826,93.464,6.536,10.30,288,1.000,bicubic
SelecSls42b.in1k,77.170,22.830,93.392,6.608,32.46,224,0.875,bicubic
resnet34.a2_in1k,77.158,22.842,93.274,6.726,21.80,288,1.000,bicubic
xcit_tiny_12_p16_224.fb_in1k,77.140,22.860,93.716,6.284,6.72,224,1.000,bicubic
legacy_seresnext26_32x4d.in1k,77.108,22.892,93.314,6.686,16.79,224,0.875,bicubic
tf_efficientnet_b0.ap_in1k,77.090,22.910,93.262,6.738,5.29,224,0.875,bicubic
hardcorenas_c.miil_green_in1k,77.058,22.942,93.168,6.832,5.52,224,0.875,bilinear
dla60.in1k,77.046,22.954,93.318,6.682,22.04,224,0.875,bilinear
seresnet50.a3_in1k,77.028,22.972,93.074,6.926,28.09,224,0.950,bicubic
convnext_atto.d2_in1k,77.008,22.992,93.702,6.298,3.70,288,0.950,bicubic
crossvit_9_dagger_240.in1k,76.980,23.020,93.620,6.380,8.78,240,0.875,bicubic
tf_mixnet_m.in1k,76.954,23.046,93.154,6.846,5.01,224,0.875,bicubic
convmixer_1024_20_ks9_p14.in1k,76.936,23.064,93.350,6.650,24.38,224,0.960,bicubic
regnetx_016.pycls_in1k,76.924,23.076,93.416,6.584,9.19,224,0.875,bicubic
skresnet34.ra_in1k,76.910,23.090,93.316,6.684,22.28,224,0.875,bicubic
gernet_s.idstcv_in1k,76.910,23.090,93.144,6.856,8.17,224,0.875,bilinear
tf_efficientnet_b0.aa_in1k,76.834,23.166,93.220,6.780,5.29,224,0.875,bicubic
hrnet_w18.ms_in1k,76.752,23.248,93.444,6.556,21.30,224,0.875,bilinear
resmlp_12_224.fb_in1k,76.648,23.352,93.178,6.822,15.35,224,0.875,bicubic
tf_efficientnet_lite1.in1k,76.644,23.356,93.230,6.770,5.42,240,0.882,bicubic
mixer_b16_224.goog_in21k_ft_in1k,76.602,23.398,92.224,7.776,59.88,224,0.875,bicubic
tf_efficientnet_es.in1k,76.600,23.400,93.202,6.798,5.44,224,0.875,bicubic
hardcorenas_b.miil_green_in1k,76.536,23.464,92.760,7.240,5.18,224,0.875,bilinear
tf_efficientnet_b0.in1k,76.532,23.468,93.008,6.992,5.29,224,0.875,bicubic
levit_128s.fb_dist_in1k,76.526,23.474,92.872,7.128,7.78,224,0.900,bicubic
mobilenetv2_140.ra_in1k,76.520,23.480,92.986,7.014,6.11,224,0.875,bicubic
levit_conv_128s.fb_dist_in1k,76.520,23.480,92.866,7.134,7.78,224,0.900,bicubic
densenet121.ra_in1k,76.500,23.500,93.368,6.632,7.98,288,0.950,bicubic
resnet34.bt_in1k,76.480,23.520,93.354,6.646,21.80,288,0.950,bicubic
repvgg_a2.rvgg_in1k,76.458,23.542,93.002,6.998,28.21,224,0.875,bilinear
resnet26.bt_in1k,76.366,23.634,93.180,6.820,16.00,288,0.950,bicubic
dpn68.mx_in1k,76.346,23.654,93.008,6.992,12.61,224,0.875,bicubic
xcit_nano_12_p8_224.fb_dist_in1k,76.332,23.668,93.094,6.906,3.05,224,1.000,bicubic
regnety_008.pycls_in1k,76.302,23.698,93.060,6.940,6.26,224,0.875,bicubic
resnet50.tv_in1k,76.130,23.870,92.858,7.142,25.56,224,0.875,bilinear
efficientformerv2_s0.snap_dist_in1k,76.118,23.882,92.860,7.140,3.60,224,0.950,bicubic
vit_small_patch32_224.augreg_in21k_ft_in1k,75.994,24.006,93.270,6.730,22.88,224,0.900,bicubic
mixnet_s.ft_in1k,75.994,24.006,92.798,7.202,4.13,224,0.875,bicubic
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,75.962,24.038,93.264,6.736,6.36,384,1.000,bicubic
hardcorenas_a.miil_green_in1k,75.930,24.070,92.510,7.490,5.26,224,0.875,bilinear
densenet169.tv_in1k,75.900,24.100,93.028,6.972,14.15,224,0.875,bicubic
mobilenetv3_large_100.ra_in1k,75.766,24.234,92.538,7.462,5.48,224,0.875,bicubic
edgenext_x_small.in1k,75.688,24.312,92.766,7.234,2.34,288,1.000,bicubic
tf_mixnet_s.in1k,75.652,24.348,92.636,7.364,4.13,224,0.875,bicubic
mobilenetv3_rw.rmsp_in1k,75.620,24.380,92.704,7.296,5.48,224,0.875,bicubic
mobilevitv2_075.cvnets_in1k,75.608,24.392,92.744,7.256,2.87,256,0.888,bicubic
regnety_004.tv2_in1k,75.598,24.402,92.696,7.304,4.34,224,0.965,bicubic
tf_mobilenetv3_large_100.in1k,75.516,24.484,92.594,7.406,5.48,224,0.875,bilinear
resnest14d.gluon_in1k,75.510,24.490,92.504,7.496,10.61,224,0.875,bilinear
efficientnet_lite0.ra_in1k,75.480,24.520,92.522,7.478,4.65,224,0.875,bicubic
xcit_nano_12_p16_384.fb_dist_in1k,75.458,24.542,92.696,7.304,3.05,384,1.000,bicubic
vit_tiny_patch16_224.augreg_in21k_ft_in1k,75.454,24.546,92.844,7.156,5.72,224,0.900,bicubic
semnasnet_100.rmsp_in1k,75.450,24.550,92.594,7.406,3.89,224,0.875,bicubic
regnety_006.pycls_in1k,75.270,24.730,92.526,7.474,6.06,224,0.875,bicubic
repvgg_b0.rvgg_in1k,75.142,24.858,92.416,7.584,15.82,224,0.875,bilinear
fbnetc_100.rmsp_in1k,75.130,24.870,92.388,7.612,5.57,224,0.875,bilinear
hrnet_w18_small_v2.ms_in1k,75.114,24.886,92.414,7.586,15.60,224,0.875,bilinear
mobilenetv2_110d.ra_in1k,75.056,24.944,92.186,7.814,4.52,224,0.875,bicubic
regnetx_008.pycls_in1k,75.030,24.970,92.338,7.662,7.26,224,0.875,bicubic
efficientnet_es_pruned.in1k,75.014,24.986,92.446,7.554,5.44,224,0.875,bicubic
tinynet_b.in1k,74.978,25.022,92.186,7.814,3.73,188,0.875,bicubic
vit_base_patch32_224.augreg_in1k,74.894,25.106,91.778,8.222,88.22,224,0.900,bicubic
tf_efficientnet_lite0.in1k,74.830,25.170,92.172,7.828,4.65,224,0.875,bicubic
legacy_seresnet34.in1k,74.808,25.192,92.126,7.874,21.96,224,0.875,bilinear
densenet121.tv_in1k,74.764,25.236,92.154,7.846,7.98,224,0.875,bicubic
mnasnet_100.rmsp_in1k,74.650,25.350,92.124,7.876,4.38,224,0.875,bicubic
dla34.in1k,74.638,25.362,92.064,7.936,15.74,224,0.875,bilinear
mobilevit_xs.cvnets_in1k,74.636,25.364,92.346,7.654,2.32,256,0.900,bicubic
regnetx_004_tv.tv2_in1k,74.596,25.404,92.178,7.822,5.50,224,0.965,bicubic
resnet34.gluon_in1k,74.578,25.422,91.984,8.016,21.80,224,0.875,bicubic
deit_tiny_distilled_patch16_224.fb_in1k,74.500,25.500,91.892,8.108,5.91,224,0.900,bicubic
pit_ti_distilled_224.in1k,74.254,25.746,91.954,8.046,5.10,224,0.900,bicubic
vgg19_bn.tv_in1k,74.216,25.784,91.844,8.156,143.68,224,0.875,bilinear
spnasnet_100.rmsp_in1k,74.086,25.914,91.820,8.180,4.42,224,0.875,bilinear
regnety_004.pycls_in1k,74.028,25.972,91.750,8.250,4.34,224,0.875,bicubic
ghostnet_100.in1k,73.982,26.018,91.466,8.534,5.18,224,0.875,bilinear
crossvit_9_240.in1k,73.958,26.042,91.968,8.032,8.55,240,0.875,bicubic
xcit_nano_12_p8_224.fb_in1k,73.916,26.084,92.170,7.830,3.05,224,1.000,bicubic
regnetx_006.pycls_in1k,73.868,26.132,91.678,8.322,6.20,224,0.875,bicubic
resnet18d.ra2_in1k,73.794,26.206,91.838,8.162,11.71,288,0.950,bicubic
vit_base_patch32_224.sam_in1k,73.694,26.306,91.014,8.986,88.22,224,0.900,bicubic
tf_mobilenetv3_large_075.in1k,73.432,26.568,91.352,8.648,3.99,224,0.875,bilinear
vgg16_bn.tv_in1k,73.370,26.630,91.514,8.486,138.37,224,0.875,bilinear
crossvit_tiny_240.in1k,73.336,26.664,91.908,8.092,7.01,240,0.875,bicubic
resnet18.fb_swsl_ig1b_ft_in1k,73.282,26.718,91.732,8.268,11.69,224,0.875,bilinear
resnet18.a1_in1k,73.158,26.842,91.026,8.974,11.69,288,1.000,bicubic
convit_tiny.fb_in1k,73.112,26.888,91.712,8.288,5.71,224,0.875,bicubic
skresnet18.ra_in1k,73.038,26.962,91.172,8.828,11.96,224,0.875,bicubic
semnasnet_075.rmsp_in1k,72.992,27.008,91.138,8.862,2.91,224,0.875,bicubic
mobilenetv2_100.ra_in1k,72.976,27.024,91.010,8.990,3.50,224,0.875,bicubic
resnet34.a3_in1k,72.970,27.030,91.108,8.892,21.80,224,0.950,bicubic
pit_ti_224.in1k,72.910,27.090,91.400,8.600,4.85,224,0.900,bicubic
resnet18.fb_ssl_yfcc100m_ft_in1k,72.594,27.406,91.418,8.582,11.69,224,0.875,bilinear
regnetx_004.pycls_in1k,72.398,27.602,90.828,9.172,5.16,224,0.875,bicubic
vgg19.tv_in1k,72.378,27.622,90.874,9.126,143.67,224,0.875,bilinear
resnet18.a2_in1k,72.372,27.628,90.596,9.404,11.69,288,1.000,bicubic
hrnet_w18_small.ms_in1k,72.338,27.662,90.678,9.322,13.19,224,0.875,bilinear
xcit_nano_12_p16_224.fb_dist_in1k,72.310,27.690,90.860,9.140,3.05,224,1.000,bicubic
tf_mobilenetv3_large_minimal_100.in1k,72.258,27.742,90.638,9.362,3.92,224,0.875,bilinear
resnet14t.c3_in1k,72.254,27.746,90.302,9.698,10.08,224,0.950,bicubic
deit_tiny_patch16_224.fb_in1k,72.172,27.828,91.112,8.888,5.72,224,0.900,bicubic
lcnet_100.ra2_in1k,72.096,27.904,90.362,9.638,2.95,224,0.875,bicubic
mixer_l16_224.goog_in21k_ft_in1k,72.054,27.946,87.674,12.326,208.20,224,0.875,bicubic
edgenext_xx_small.in1k,71.878,28.122,90.552,9.448,1.33,288,1.000,bicubic
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,71.798,28.202,90.824,9.176,6.34,224,0.900,bicubic
legacy_seresnet18.in1k,71.766,28.234,90.332,9.668,11.78,224,0.875,bicubic
vgg16.tv_in1k,71.592,28.408,90.384,9.616,138.36,224,0.875,bilinear
vgg13_bn.tv_in1k,71.588,28.412,90.378,9.622,133.05,224,0.875,bilinear
tinynet_c.in1k,71.240,28.760,89.734,10.266,2.46,184,0.875,bicubic
resnet18.gluon_in1k,70.838,29.162,89.754,10.246,11.69,224,0.875,bicubic
pvt_v2_b0.in1k,70.660,29.340,90.196,9.804,3.67,224,0.900,bicubic
vgg11_bn.tv_in1k,70.382,29.618,89.808,10.192,132.87,224,0.875,bilinear
regnety_002.pycls_in1k,70.278,29.722,89.528,10.472,3.16,224,0.875,bicubic
mobilevitv2_050.cvnets_in1k,70.138,29.862,89.920,10.080,1.37,256,0.888,bicubic
xcit_nano_12_p16_224.fb_in1k,69.962,30.038,89.762,10.238,3.05,224,1.000,bicubic
vgg13.tv_in1k,69.932,30.068,89.250,10.750,133.05,224,0.875,bilinear
resnet18.tv_in1k,69.760,30.240,89.070,10.930,11.69,224,0.875,bilinear
vgg11.tv_in1k,69.022,30.978,88.624,11.376,132.86,224,0.875,bilinear
mobilevit_xxs.cvnets_in1k,68.932,31.068,88.942,11.058,1.27,256,0.900,bicubic
lcnet_075.ra2_in1k,68.782,31.218,88.360,11.640,2.36,224,0.875,bicubic
regnetx_002.pycls_in1k,68.746,31.254,88.536,11.464,2.68,224,0.875,bicubic
resnet10t.c3_in1k,68.358,31.642,88.040,11.960,5.44,224,0.950,bicubic
resnet18.a3_in1k,68.254,31.746,88.176,11.824,11.69,224,0.950,bicubic
tf_mobilenetv3_small_100.in1k,67.918,32.082,87.674,12.326,2.54,224,0.875,bilinear
dla60x_c.in1k,67.898,32.102,88.422,11.578,1.32,224,0.875,bilinear
mobilenetv3_small_100.lamb_in1k,67.656,32.344,87.636,12.364,2.54,224,0.875,bicubic
tinynet_d.in1k,66.974,33.026,87.062,12.938,2.34,152,0.875,bicubic
mnasnet_small.lamb_in1k,66.196,33.804,86.504,13.496,2.03,224,0.875,bicubic
dla46x_c.in1k,65.988,34.012,86.980,13.020,1.07,224,0.875,bilinear
mobilenetv2_050.lamb_in1k,65.950,34.050,86.088,13.912,1.97,224,0.875,bicubic
tf_mobilenetv3_small_075.in1k,65.728,34.272,86.126,13.874,2.04,224,0.875,bilinear
mobilenetv3_small_075.lamb_in1k,65.234,34.766,85.446,14.554,2.04,224,0.875,bicubic
dla46_c.in1k,64.872,35.128,86.298,13.702,1.30,224,0.875,bilinear
lcnet_050.ra2_in1k,63.120,36.880,84.386,15.614,1.88,224,0.875,bicubic
tf_mobilenetv3_small_minimal_100.in1k,62.890,37.110,84.240,15.760,2.04,224,0.875,bilinear
tinynet_e.in1k,59.866,40.134,81.764,18.236,2.04,106,0.875,bicubic
mobilenetv3_small_050.lamb_in1k,57.910,42.090,80.180,19.820,1.59,224,0.875,bicubic
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenetv2-matched-frequency.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva_giant_patch14_336.clip_ft_in1k,82.200,17.800,96.290,3.710,"1,013.01",336,1.000,bicubic,-7.266,-2.536,+6
eva02_large_patch14_448.mim_in22k_ft_in1k,82.130,17.870,96.260,3.740,305.08,448,1.000,bicubic,-7.494,-2.690,+2
eva02_large_patch14_448.mim_m38m_ft_in1k,82.130,17.870,96.160,3.840,305.08,448,1.000,bicubic,-7.440,-2.762,+2
eva_giant_patch14_560.m30m_ft_in22k_in1k,82.040,17.960,96.440,3.560,"1,014.45",560,1.000,bicubic,-7.746,-2.552,-1
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,81.900,18.100,96.150,3.850,305.08,448,1.000,bicubic,-8.066,-2.862,-3
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,81.890,18.110,96.370,3.630,305.08,448,1.000,bicubic,-8.162,-2.678,-5
eva_giant_patch14_336.m30m_ft_in22k_in1k,81.820,18.180,96.290,3.710,"1,013.01",336,1.000,bicubic,-7.746,-2.662,-1
eva_giant_patch14_224.clip_ft_in1k,81.750,18.250,96.080,3.920,"1,012.56",224,0.900,bicubic,-7.130,-2.600,+1
eva_large_patch14_336.in22k_ft_in1k,81.190,18.810,95.880,4.120,304.53,336,1.000,bicubic,-7.480,-2.842,+4
eva_large_patch14_336.in22k_ft_in22k_in1k,80.930,19.070,96.010,3.990,304.53,336,1.000,bicubic,-8.278,-2.844,-2
vit_large_patch14_clip_336.openai_ft_in12k_in1k,80.530,19.470,95.500,4.500,304.53,336,1.000,bicubic,-7.738,-3.026,+13
regnety_1280.swag_ft_in1k,80.480,19.520,96.180,3.820,644.81,384,1.000,bicubic,-7.750,-2.506,+16
tf_efficientnet_l2.ns_jft_in1k_475,80.470,19.530,95.730,4.270,480.31,475,0.936,bicubic,-7.764,-2.816,+14
convnext_xxlarge.clip_laion2b_soup_ft_in1k,80.450,19.550,95.780,4.220,846.47,256,1.000,bicubic,-8.154,-2.928,0
beitv2_large_patch16_224.in1k_ft_in22k_in1k,80.270,19.730,95.150,4.850,304.43,224,0.950,bicubic,-8.124,-3.448,+5
tf_efficientnet_l2.ns_jft_in1k,80.250,19.750,95.860,4.140,480.31,800,0.960,bicubic,-8.102,-2.788,+5
eva_large_patch14_196.in22k_ft_in22k_in1k,80.180,19.820,95.380,4.620,304.14,196,1.000,bicubic,-8.396,-3.278,0
eva_large_patch14_196.in22k_ft_in1k,80.160,19.840,95.460,4.540,304.14,196,1.000,bicubic,-7.774,-3.038,+19
maxvit_base_tf_512.in21k_ft_in1k,80.150,19.850,95.480,4.520,119.88,512,1.000,bicubic,-8.068,-3.048,+11
maxvit_xlarge_tf_512.in21k_ft_in1k,80.100,19.900,95.480,4.520,475.77,512,1.000,bicubic,-8.438,-3.164,-2
convnextv2_huge.fcmae_ft_in22k_in1k_512,79.990,20.010,95.900,4.100,660.29,512,1.000,bicubic,-8.868,-2.848,-11
maxvit_large_tf_512.in21k_ft_in1k,79.970,20.030,95.160,4.840,212.33,512,1.000,bicubic,-8.256,-3.438,+7
beit_large_patch16_512.in22k_ft_in22k_in1k,79.950,20.050,95.350,4.650,305.67,512,1.000,bicubic,-8.646,-3.306,-8
convnextv2_huge.fcmae_ft_in22k_in1k_384,79.940,20.060,95.690,4.310,660.29,384,1.000,bicubic,-8.730,-3.048,-12
maxvit_xlarge_tf_384.in21k_ft_in1k,79.710,20.290,95.160,4.840,475.32,384,1.000,bicubic,-8.604,-3.384,-3
vit_large_patch14_clip_224.openai_ft_in1k,79.620,20.380,95.000,5.000,304.20,224,1.000,bicubic,-8.232,-3.426,+15
maxvit_large_tf_384.in21k_ft_in1k,79.580,20.420,95.070,4.930,212.03,384,1.000,bicubic,-8.406,-3.498,+8
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,79.520,20.480,95.200,4.800,87.12,448,1.000,bicubic,-9.172,-3.524,-17
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,79.520,20.480,95.000,5.000,632.46,336,1.000,bicubic,-9.072,-3.662,-13
beit_large_patch16_384.in22k_ft_in22k_in1k,79.500,20.500,95.180,4.820,305.00,384,1.000,bicubic,-8.902,-3.428,-11
vit_large_patch14_clip_224.openai_ft_in12k_in1k,79.400,20.600,95.070,4.930,304.20,224,1.000,bicubic,-8.776,-3.474,+2
vit_huge_patch14_clip_224.laion2b_ft_in1k,79.370,20.630,94.920,5.080,632.05,224,1.000,bicubic,-8.218,-3.298,+16
maxvit_base_tf_384.in21k_ft_in1k,79.340,20.660,95.080,4.920,119.65,384,1.000,bicubic,-8.582,-3.464,+5
vit_large_patch14_clip_336.laion2b_ft_in1k,79.240,20.760,95.000,5.000,304.53,336,1.000,bicubic,-8.616,-3.368,+6
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,79.200,20.800,95.120,4.880,632.05,224,1.000,bicubic,-9.056,-3.432,-10
deit3_huge_patch14_224.fb_in22k_ft_in1k,79.190,20.810,94.870,5.130,632.13,224,1.000,bicubic,-7.996,-3.390,+26
eva02_base_patch14_448.mim_in22k_ft_in1k,79.150,20.850,95.110,4.890,87.12,448,1.000,bicubic,-9.100,-3.454,-11
deit3_large_patch16_384.fb_in22k_ft_in1k,79.080,20.920,94.870,5.130,304.76,384,1.000,bicubic,-8.640,-3.642,+7
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,79.040,20.960,95.010,4.990,200.13,384,1.000,bicubic,-9.266,-3.572,-17
caformer_b36.sail_in22k_ft_in1k_384,79.040,20.960,94.920,5.080,98.75,384,1.000,bicubic,-9.018,-3.662,-5
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,78.980,21.020,94.890,5.110,304.53,336,1.000,bicubic,-9.198,-3.680,-9
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,78.900,21.100,94.580,5.420,116.14,384,1.000,bicubic,-8.928,-3.792,+1
beit_large_patch16_224.in22k_ft_in22k_in1k,78.830,21.170,94.610,5.390,304.43,224,0.900,bicubic,-8.648,-3.694,+7
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,78.750,21.250,94.950,5.050,200.13,384,1.000,bicubic,-9.098,-3.496,-2
beitv2_large_patch16_224.in1k_ft_in1k,78.730,21.270,94.230,5.770,304.43,224,0.950,bicubic,-8.682,-4.004,+11
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,78.650,21.350,94.660,5.340,200.13,320,1.000,bicubic,-9.308,-3.816,-10
deit3_large_patch16_224.fb_in22k_ft_in1k,78.630,21.370,94.720,5.280,304.37,224,1.000,bicubic,-8.352,-3.516,+25
convnextv2_large.fcmae_ft_in22k_in1k_384,78.570,21.430,94.840,5.160,197.96,384,1.000,bicubic,-9.628,-3.688,-17
tf_efficientnet_b7.ns_jft_in1k,78.530,21.470,94.370,5.630,66.35,600,0.949,bicubic,-8.310,-3.724,+29
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,78.490,21.510,94.660,5.340,304.20,224,1.000,bicubic,-9.402,-3.750,-11
vit_large_patch14_clip_224.laion2b_ft_in1k,78.450,21.550,94.580,5.420,304.20,224,1.000,bicubic,-8.832,-3.666,+9
regnety_320.swag_ft_in1k,78.380,21.620,95.210,4.790,145.05,384,1.000,bicubic,-8.456,-3.152,+27
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,78.240,21.760,94.350,5.650,73.88,384,1.000,bicubic,-9.142,-3.962,+4
caformer_m36.sail_in22k_ft_in1k_384,78.170,21.830,94.180,5.820,56.20,384,1.000,bicubic,-9.280,-4.128,0
caformer_b36.sail_in22k_ft_in1k,78.110,21.890,94.400,5.600,98.75,224,1.000,bicubic,-9.312,-3.926,0
convnextv2_huge.fcmae_ft_in1k,78.060,21.940,94.060,5.940,660.29,288,1.000,bicubic,-8.520,-3.912,+35
convformer_b36.sail_in22k_ft_in1k_384,78.040,21.960,94.170,5.830,99.88,384,1.000,bicubic,-9.562,-4.264,-10
convnext_xlarge.fb_in22k_ft_in1k_384,77.990,22.010,94.480,5.520,350.20,384,1.000,bicubic,-9.762,-4.076,-14
volo_d5_512.sail_in1k,77.970,22.030,94.160,5.840,296.09,512,1.150,bicubic,-9.088,-3.810,+8
vit_large_patch16_384.augreg_in21k_ft_in1k,77.940,22.060,94.450,5.550,304.72,384,1.000,bicubic,-9.144,-3.852,+5
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,77.940,22.060,94.390,5.610,200.13,256,1.000,bicubic,-9.394,-3.828,-2
convnextv2_large.fcmae_ft_in22k_in1k,77.900,22.100,94.390,5.610,197.96,288,1.000,bicubic,-9.584,-3.966,-13
deit3_base_patch16_384.fb_in22k_ft_in1k,77.860,22.140,94.030,5.970,86.88,384,1.000,bicubic,-8.878,-4.084,+22
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,77.780,22.220,94.160,5.840,86.86,384,1.000,bicubic,-9.428,-3.874,-3
volo_d5_448.sail_in1k,77.750,22.250,94.050,5.950,295.91,448,1.150,bicubic,-9.202,-3.888,+9
volo_d4_448.sail_in1k,77.740,22.260,93.940,6.060,193.41,448,1.150,bicubic,-9.052,-3.944,+17
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,77.710,22.290,94.330,5.670,116.09,384,1.000,bicubic,-9.754,-4.044,-15
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,77.680,22.320,94.070,5.930,116.14,224,0.950,bicubic,-9.214,-3.944,+7
tf_efficientnetv2_xl.in21k_ft_in1k,77.630,22.370,93.950,6.050,208.12,512,1.000,bicubic,-9.118,-4.064,+15
tf_efficientnetv2_l.in21k_ft_in1k,77.580,22.420,94.280,5.720,118.52,480,1.000,bicubic,-9.222,-3.856,+10
caformer_s36.sail_in22k_ft_in1k_384,77.540,22.460,94.070,5.930,39.30,384,1.000,bicubic,-9.316,-4.142,+6
convnextv2_base.fcmae_ft_in22k_in1k_384,77.490,22.510,94.410,5.590,88.72,384,1.000,bicubic,-10.154,-4.006,-27
caformer_b36.sail_in1k_384,77.490,22.510,93.530,6.470,98.75,384,1.000,bicubic,-8.918,-4.284,+30
convnext_base.clip_laiona_augreg_ft_in1k_384,77.480,22.520,93.960,6.040,88.59,384,1.000,bicubic,-9.022,-4.008,+21
maxvit_base_tf_512.in1k,77.440,22.560,93.980,6.020,119.88,512,1.000,bicubic,-9.162,-3.938,+14
caformer_m36.sail_in22k_ft_in1k,77.430,22.570,93.590,6.410,56.20,224,1.000,bicubic,-9.164,-4.434,+14
convnext_large.fb_in22k_ft_in1k_384,77.420,22.580,94.200,5.800,197.77,384,1.000,bicubic,-10.052,-4.186,-26
regnety_1280.swag_lc_in1k,77.380,22.620,94.500,5.500,644.81,224,0.965,bicubic,-8.602,-3.350,+54
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,77.340,22.660,93.910,6.090,196.74,384,1.000,bicubic,-10.124,-4.340,-26
vit_base_patch16_clip_384.laion2b_ft_in1k,77.320,22.680,93.860,6.140,86.86,384,1.000,bicubic,-9.300,-4.148,+8
maxvit_large_tf_512.in1k,77.310,22.690,93.800,6.200,212.33,512,1.000,bicubic,-9.216,-4.080,+12
convnextv2_base.fcmae_ft_in22k_in1k,77.290,22.710,94.110,5.890,88.72,288,1.000,bicubic,-9.708,-4.058,-11
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,77.290,22.710,94.050,5.950,93.59,320,1.000,bicubic,-9.434,-4.126,+3
tf_efficientnet_b6.ns_jft_in1k,77.290,22.710,93.890,6.110,43.04,528,0.942,bicubic,-9.166,-3.994,+16
convformer_b36.sail_in22k_ft_in1k,77.270,22.730,93.970,6.030,99.88,224,1.000,bicubic,-9.728,-4.202,-15
regnety_160.swag_ft_in1k,77.240,22.760,94.620,5.380,83.59,384,1.000,bicubic,-8.770,-3.434,+40
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,77.240,22.760,94.250,5.750,87.92,384,1.000,bicubic,-9.856,-3.984,-22
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,77.180,22.820,94.170,5.830,88.59,384,1.000,bicubic,-9.954,-4.052,-25
caformer_m36.sail_in1k_384,77.160,22.840,93.630,6.370,56.20,384,1.000,bicubic,-9.006,-4.190,+27
maxvit_large_tf_384.in1k,77.130,22.870,93.460,6.540,212.03,384,1.000,bicubic,-9.098,-4.230,+20
beitv2_base_patch16_224.in1k_ft_in22k_in1k,77.120,22.880,94.020,5.980,86.53,224,0.900,bicubic,-9.354,-4.032,+8
volo_d3_448.sail_in1k,77.090,22.910,94.110,5.890,86.63,448,1.000,bicubic,-9.410,-3.600,+4
swin_large_patch4_window12_384.ms_in22k_ft_in1k,77.070,22.930,93.760,6.240,196.74,384,1.000,bicubic,-10.062,-4.474,-29
vit_large_r50_s32_384.augreg_in21k_ft_in1k,77.070,22.930,93.710,6.290,329.09,384,1.000,bicubic,-9.112,-4.212,+20
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,77.000,23.000,93.320,6.680,73.88,224,0.950,bicubic,-9.504,-4.574,-1
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,76.970,23.030,93.520,6.480,196.74,256,0.900,bicubic,-9.982,-4.586,-23
vit_base_patch16_clip_384.openai_ft_in1k,76.960,23.040,93.750,6.250,86.86,384,1.000,bicubic,-9.244,-4.124,+14
convformer_m36.sail_in22k_ft_in1k_384,76.960,23.040,93.690,6.310,57.05,384,1.000,bicubic,-9.932,-4.426,-21
beit_base_patch16_384.in22k_ft_in22k_in1k,76.930,23.070,93.910,6.090,86.74,384,1.000,bicubic,-9.870,-4.226,-18
seresnextaa101d_32x8d.sw_in12k_ft_in1k,76.900,23.100,93.850,6.150,93.59,288,1.000,bicubic,-9.584,-4.180,-2
tf_efficientnetv2_m.in21k_ft_in1k,76.890,23.110,93.650,6.350,54.14,480,1.000,bicubic,-9.110,-4.296,+26
vit_base_patch16_clip_384.openai_ft_in12k_in1k,76.870,23.130,93.780,6.220,86.86,384,0.950,bicubic,-10.154,-4.400,-34
cait_m48_448.fb_dist_in1k,76.870,23.130,93.380,6.620,356.46,448,1.000,bicubic,-9.622,-4.372,-5
tf_efficientnet_b5.ns_jft_in1k,76.830,23.170,93.580,6.420,30.39,456,0.934,bicubic,-9.258,-4.174,+17
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,76.810,23.190,93.210,6.790,468.53,224,0.875,bilinear,-8.288,-4.228,+89
maxvit_base_tf_384.in1k,76.790,23.210,93.440,6.560,119.65,384,1.000,bicubic,-9.512,-4.358,+1
convnext_large.fb_in22k_ft_in1k,76.750,23.250,93.710,6.290,197.77,288,1.000,bicubic,-10.276,-4.494,-39
deit3_large_patch16_384.fb_in1k,76.690,23.310,93.350,6.650,304.76,384,1.000,bicubic,-9.122,-4.248,+31
convnextv2_large.fcmae_ft_in1k,76.660,23.340,93.570,6.430,197.96,288,1.000,bicubic,-9.458,-4.252,+10
convnext_base.fb_in22k_ft_in1k_384,76.650,23.350,93.700,6.300,88.59,384,1.000,bicubic,-10.146,-4.564,-28
convnext_xlarge.fb_in22k_ft_in1k,76.630,23.370,93.860,6.140,350.20,288,1.000,bicubic,-10.700,-4.468,-53
beitv2_base_patch16_224.in1k_ft_in1k,76.630,23.370,92.930,7.070,86.53,224,0.900,bicubic,-8.964,-4.576,+42
coatnet_2_rw_224.sw_in12k_ft_in1k,76.620,23.380,93.380,6.620,73.87,224,0.950,bicubic,-9.948,-4.514,-21
xcit_large_24_p8_384.fb_dist_in1k,76.620,23.380,93.090,6.910,188.93,384,1.000,bicubic,-9.376,-4.600,+15
regnety_160.sw_in12k_ft_in1k,76.610,23.390,93.560,6.440,83.59,288,1.000,bicubic,-9.376,-4.274,+16
caformer_s36.sail_in22k_ft_in1k,76.590,23.410,93.620,6.380,39.30,224,1.000,bicubic,-9.202,-4.204,+25
vit_base_patch8_224.augreg2_in21k_ft_in1k,76.590,23.410,93.340,6.660,86.58,224,0.900,bicubic,-9.628,-4.492,-6
caformer_s36.sail_in1k_384,76.550,23.450,93.450,6.550,39.30,384,1.000,bicubic,-9.190,-4.222,+28
volo_d5_224.sail_in1k,76.550,23.450,93.330,6.670,295.46,224,0.960,bicubic,-9.520,-4.246,+4
deit3_base_patch16_224.fb_in22k_ft_in1k,76.530,23.470,93.560,6.440,86.59,224,1.000,bicubic,-9.170,-4.186,+28
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,76.530,23.470,93.370,6.630,116.09,224,0.950,bicubic,-10.112,-4.650,-34
dm_nfnet_f6.dm_in1k,76.510,23.490,93.340,6.660,438.36,576,0.956,bicubic,-9.852,-4.556,-17
maxvit_small_tf_512.in1k,76.490,23.510,93.360,6.640,69.13,512,1.000,bicubic,-9.592,-4.404,-1
vit_base_patch16_384.augreg_in21k_ft_in1k,76.480,23.520,93.760,6.240,86.86,384,1.000,bicubic,-9.516,-4.242,+4
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,76.450,23.550,93.700,6.300,87.92,256,0.900,bicubic,-9.818,-4.182,-16
convformer_s36.sail_in22k_ft_in1k_384,76.370,23.630,93.530,6.470,40.01,384,1.000,bicubic,-10.008,-4.454,-24
convformer_m36.sail_in1k_384,76.370,23.630,93.100,6.900,57.05,384,1.000,bicubic,-9.210,-4.442,+28
convformer_m36.sail_in22k_ft_in1k,76.360,23.640,93.450,6.550,57.05,224,1.000,bicubic,-9.786,-4.400,-10
convnext_small.in12k_ft_in1k_384,76.340,23.660,93.420,6.580,50.22,384,1.000,bicubic,-9.842,-4.502,-16
cait_m36_384.fb_dist_in1k,76.330,23.670,93.060,6.940,271.22,384,1.000,bicubic,-9.730,-4.670,-6
swin_large_patch4_window7_224.ms_in22k_ft_in1k,76.310,23.690,93.390,6.610,196.53,224,0.900,bicubic,-10.002,-4.512,-25
vit_large_patch16_224.augreg_in21k_ft_in1k,76.300,23.700,93.610,6.390,304.33,224,0.900,bicubic,-9.534,-4.208,+5
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,76.290,23.710,93.810,6.190,88.59,256,1.000,bicubic,-10.080,-4.174,-29
swin_base_patch4_window12_384.ms_in22k_ft_in1k,76.250,23.750,93.300,6.700,87.90,384,1.000,bicubic,-10.188,-4.766,-33
cait_s36_384.fb_dist_in1k,76.220,23.780,92.960,7.040,68.37,384,1.000,bicubic,-9.234,-4.518,+27
regnety_160.lion_in12k_ft_in1k,76.200,23.800,93.670,6.330,83.59,288,1.000,bicubic,-9.786,-4.166,-6
dm_nfnet_f4.dm_in1k,76.160,23.840,93.050,6.950,316.07,512,0.951,bicubic,-9.676,-4.614,-1
xcit_medium_24_p8_384.fb_dist_in1k,76.130,23.870,92.980,7.020,84.32,384,1.000,bicubic,-9.686,-4.612,0
maxvit_small_tf_384.in1k,76.120,23.880,92.610,7.390,69.02,384,1.000,bicubic,-9.420,-4.852,+17
flexivit_large.1200ep_in1k,76.100,23.900,93.010,6.990,304.36,240,0.950,bicubic,-9.546,-4.532,+12
volo_d2_384.sail_in1k,76.090,23.910,93.130,6.870,58.87,384,1.000,bicubic,-9.952,-4.444,-17
tf_efficientnet_b8.ap_in1k,76.090,23.910,92.730,7.270,87.41,672,0.954,bicubic,-9.274,-4.562,+32
tf_efficientnet_b7.ap_in1k,76.080,23.920,92.970,7.030,66.35,600,0.949,bicubic,-9.042,-4.282,+48
convformer_b36.sail_in1k_384,76.070,23.930,92.720,7.280,99.88,384,1.000,bicubic,-9.672,-4.804,+1
maxvit_tiny_tf_512.in1k,76.060,23.940,93.160,6.840,31.05,512,1.000,bicubic,-9.604,-4.424,+5
flexivit_large.600ep_in1k,76.050,23.950,92.960,7.040,304.36,240,0.950,bicubic,-9.488,-4.528,+11
volo_d4_224.sail_in1k,76.020,23.980,92.990,7.010,192.96,224,0.960,bicubic,-9.852,-4.482,-12
tf_efficientnetv2_l.in1k,76.000,24.000,93.070,6.930,118.52,480,1.000,bicubic,-9.664,-4.404,+3
xcit_large_24_p8_224.fb_dist_in1k,75.970,24.030,92.710,7.290,188.93,224,1.000,bicubic,-9.432,-4.692,+19
vit_base_patch8_224.augreg_in21k_ft_in1k,75.960,24.040,93.370,6.630,86.58,224,0.900,bicubic,-9.838,-4.420,-10
flexivit_large.300ep_in1k,75.940,24.060,92.650,7.350,304.36,240,0.950,bicubic,-9.350,-4.750,+25
convnext_base.fb_in22k_ft_in1k,75.930,24.070,93.580,6.420,88.59,288,1.000,bicubic,-10.344,-4.512,-44
convnext_base.clip_laion2b_augreg_ft_in1k,75.840,24.160,93.210,6.790,88.59,256,1.000,bicubic,-10.318,-4.470,-36
xcit_large_24_p16_384.fb_dist_in1k,75.820,24.180,92.740,7.260,189.10,384,1.000,bicubic,-9.934,-4.798,-10
dm_nfnet_f3.dm_in1k,75.790,24.210,92.910,7.090,254.92,416,0.940,bicubic,-9.896,-4.660,-6
eva02_small_patch14_336.mim_in22k_ft_in1k,75.790,24.210,92.820,7.180,22.13,336,1.000,bicubic,-9.926,-4.812,-9
convformer_s36.sail_in1k_384,75.780,24.220,93.090,6.910,40.01,384,1.000,bicubic,-9.598,-4.386,+13
deit3_huge_patch14_224.fb_in1k,75.780,24.220,92.740,7.260,632.13,224,0.900,bicubic,-9.444,-4.620,+24
xcit_small_24_p8_384.fb_dist_in1k,75.770,24.230,92.970,7.030,47.63,384,1.000,bicubic,-9.784,-4.600,-4
caformer_b36.sail_in1k,75.730,24.270,92.690,7.310,98.75,224,1.000,bicubic,-9.776,-4.620,-1
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,75.720,24.280,92.910,7.090,194.03,224,0.875,bilinear,-8.446,-4.288,+116
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,75.710,24.290,92.770,7.230,86.57,224,0.950,bicubic,-10.464,-4.986,-47
tf_efficientnet_b4.ns_jft_in1k,75.690,24.310,93.050,6.950,19.34,380,0.922,bicubic,-9.472,-4.420,+25
caformer_s18.sail_in22k_ft_in1k_384,75.680,24.320,93.500,6.500,26.34,384,1.000,bicubic,-9.734,-4.202,+1
efficientnet_b5.sw_in12k_ft_in1k,75.680,24.320,93.040,6.960,30.39,448,1.000,bicubic,-10.212,-4.696,-31
hrnet_w48_ssld.paddle_in1k,75.670,24.330,92.830,7.170,77.47,288,1.000,bilinear,-8.808,-4.404,+76
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,75.660,24.340,92.980,7.020,39.03,384,0.950,bicubic,-9.872,-4.658,-9
volo_d1_384.sail_in1k,75.650,24.350,93.060,6.940,26.78,384,1.000,bicubic,-9.594,-4.134,+11
volo_d3_224.sail_in1k,75.620,24.380,92.980,7.020,86.33,224,0.960,bicubic,-9.794,-4.296,-3
convnextv2_base.fcmae_ft_in1k,75.620,24.380,92.850,7.150,88.72,288,1.000,bicubic,-9.854,-4.534,-9
dm_nfnet_f5.dm_in1k,75.610,24.390,92.780,7.220,377.21,544,0.954,bicubic,-10.488,-4.908,-52
caformer_m36.sail_in1k,75.610,24.390,92.400,7.600,56.20,224,1.000,bicubic,-9.622,-4.800,+10
vit_base_patch16_clip_224.openai_ft_in1k,75.580,24.420,92.960,7.040,86.57,224,0.900,bicubic,-9.712,-4.478,+2
vit_base_r50_s16_384.orig_in21k_ft_in1k,75.570,24.430,92.780,7.220,98.95,384,1.000,bicubic,-9.406,-4.510,+31
deit_base_distilled_patch16_384.fb_in1k,75.550,24.450,92.500,7.500,87.63,384,1.000,bicubic,-9.876,-4.830,-12
regnetz_e8.ra3_in1k,75.510,24.490,92.690,7.310,57.70,320,1.000,bicubic,-9.526,-4.582,+25
cait_s24_384.fb_dist_in1k,75.500,24.500,92.610,7.390,47.06,384,1.000,bicubic,-9.548,-4.736,+23
convformer_s36.sail_in22k_ft_in1k,75.480,24.520,93.190,6.810,40.01,224,1.000,bicubic,-9.934,-4.378,-12
xcit_medium_24_p8_224.fb_dist_in1k,75.470,24.530,92.890,7.110,84.32,224,1.000,bicubic,-9.604,-4.384,+18
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,75.470,24.530,92.730,7.270,88.79,224,0.875,bilinear,-8.832,-4.446,+84
vit_base_patch16_clip_224.openai_ft_in12k_in1k,75.460,24.540,92.750,7.250,86.57,224,0.950,bicubic,-10.480,-4.978,-48
regnety_2560.seer_ft_in1k,75.440,24.560,92.830,7.170,"1,282.60",384,1.000,bicubic,-9.712,-4.606,+7
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,75.420,24.580,92.710,7.290,88.34,448,1.000,bicubic,-10.358,-4.928,-40
regnety_320.swag_lc_in1k,75.400,24.600,93.680,6.320,145.05,224,0.965,bicubic,-9.146,-3.762,+48
beit_base_patch16_224.in22k_ft_in22k_in1k,75.390,24.610,93.020,6.980,86.53,224,0.900,bicubic,-9.822,-4.638,-1
tf_efficientnetv2_m.in1k,75.380,24.620,92.760,7.240,54.14,480,1.000,bicubic,-9.824,-4.604,-2
tf_efficientnet_b6.ap_in1k,75.380,24.620,92.440,7.560,43.04,528,0.942,bicubic,-9.406,-4.696,+33
vit_base_patch16_224.augreg2_in21k_ft_in1k,75.360,24.640,93.230,6.770,86.57,224,0.900,bicubic,-9.736,-4.300,+7
regnety_120.sw_in12k_ft_in1k,75.330,24.670,92.970,7.030,51.82,288,1.000,bicubic,-10.070,-4.612,-20
volo_d2_224.sail_in1k,75.310,24.690,92.520,7.480,58.68,224,0.960,bicubic,-9.892,-4.670,-4
dm_nfnet_f2.dm_in1k,75.270,24.730,92.450,7.550,193.78,352,0.920,bicubic,-9.924,-4.896,-5
mvitv2_large.fb_in1k,75.270,24.730,92.360,7.640,217.99,224,0.900,bicubic,-9.974,-4.854,-13
coat_lite_medium_384.in1k,75.270,24.730,92.230,7.770,44.57,384,1.000,bicubic,-9.608,-5.142,+21
caformer_s18.sail_in1k_384,75.210,24.790,92.700,7.300,26.34,384,1.000,bicubic,-9.816,-4.658,+8
convnext_small.fb_in22k_ft_in1k_384,75.160,24.840,93.060,6.940,50.22,384,1.000,bicubic,-10.618,-4.830,-53
efficientnetv2_rw_m.agc_in1k,75.150,24.850,92.570,7.430,53.24,416,1.000,bicubic,-9.662,-4.580,+22
ecaresnet269d.ra2_in1k,75.130,24.870,92.830,7.170,102.09,352,1.000,bicubic,-9.836,-4.392,+9
vit_base_patch16_clip_224.laion2b_ft_in1k,75.120,24.880,92.700,7.300,86.57,224,1.000,bicubic,-10.346,-4.874,-38
deit3_large_patch16_224.fb_in1k,75.120,24.880,92.280,7.720,304.37,224,0.900,bicubic,-9.656,-4.758,+22
deit3_small_patch16_384.fb_in22k_ft_in1k,75.090,24.910,92.810,7.190,22.21,384,1.000,bicubic,-9.734,-4.678,+16
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,75.090,24.910,92.580,7.420,88.30,384,1.000,bicubic,-10.276,-5.076,-29
xcit_medium_24_p16_384.fb_dist_in1k,75.090,24.910,92.440,7.560,84.40,384,1.000,bicubic,-10.332,-4.966,-38
maxvit_tiny_tf_384.in1k,75.020,24.980,92.450,7.550,30.98,384,1.000,bicubic,-10.080,-4.928,-10
convformer_b36.sail_in1k,74.980,25.020,91.600,8.400,99.88,224,1.000,bicubic,-9.838,-5.346,+13
xcit_small_24_p8_224.fb_dist_in1k,74.970,25.030,92.320,7.680,47.63,224,1.000,bicubic,-9.898,-4.870,+8
convnext_small.in12k_ft_in1k,74.960,25.040,92.520,7.480,50.22,288,1.000,bicubic,-10.370,-5.026,-32
tf_efficientnet_b8.ra_in1k,74.910,25.090,92.330,7.670,87.41,672,0.954,bicubic,-10.458,-5.064,-36
xcit_small_12_p8_384.fb_dist_in1k,74.850,25.150,92.450,7.550,26.21,384,1.000,bicubic,-10.228,-4.832,-11
eca_nfnet_l2.ra3_in1k,74.820,25.180,92.650,7.350,56.72,384,1.000,bicubic,-9.882,-4.532,+14
deit3_base_patch16_384.fb_in1k,74.790,25.210,92.240,7.760,86.88,384,1.000,bicubic,-10.282,-5.012,-11
convnext_tiny.in12k_ft_in1k_384,74.730,25.270,92.810,7.190,28.59,384,1.000,bicubic,-10.392,-4.796,-21
tf_efficientnet_b7.ra_in1k,74.730,25.270,92.220,7.780,66.35,600,0.949,bicubic,-10.202,-4.988,-4
deit3_medium_patch16_224.fb_in22k_ft_in1k,74.690,25.310,92.470,7.530,38.85,224,1.000,bicubic,-9.862,-4.718,+18
convnext_large.fb_in1k,74.650,25.350,91.970,8.030,197.77,288,1.000,bicubic,-10.196,-5.244,+1
xcit_large_24_p16_224.fb_dist_in1k,74.650,25.350,91.860,8.140,189.10,224,1.000,bicubic,-10.266,-5.270,-5
convformer_s18.sail_in1k_384,74.640,25.360,92.480,7.520,26.77,384,1.000,bicubic,-9.762,-4.632,+42
caformer_s36.sail_in1k,74.630,25.370,92.080,7.920,39.30,224,1.000,bicubic,-9.878,-4.916,+21
xcit_small_24_p16_384.fb_dist_in1k,74.590,25.410,92.460,7.540,47.67,384,1.000,bicubic,-10.500,-4.852,-23
tf_efficientnet_b5.ap_in1k,74.590,25.410,91.990,8.010,30.39,456,0.934,bicubic,-9.668,-4.984,+49
maxvit_large_tf_224.in1k,74.580,25.420,91.700,8.300,211.79,224,0.950,bicubic,-10.362,-5.270,-13
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,74.570,25.430,91.960,8.040,38.86,256,0.950,bicubic,-9.876,-4.884,+25
swin_base_patch4_window7_224.ms_in22k_ft_in1k,74.560,25.440,92.540,7.460,87.77,224,0.900,bicubic,-10.712,-5.024,-46
dm_nfnet_f1.dm_in1k,74.560,25.440,92.170,7.830,132.63,320,0.910,bicubic,-10.142,-5.096,+2
convformer_s18.sail_in22k_ft_in1k_384,74.550,25.450,92.770,7.230,26.77,384,1.000,bicubic,-10.448,-4.800,-20
maxxvit_rmlp_small_rw_256.sw_in1k,74.520,25.480,92.000,8.000,66.01,256,0.950,bicubic,-10.104,-5.068,+2
regnetz_040.ra3_in1k,74.510,25.490,91.880,8.120,27.12,320,1.000,bicubic,-9.730,-5.052,+44
seresnet152d.ra2_in1k,74.500,25.500,92.090,7.910,66.84,320,1.000,bicubic,-9.860,-4.950,+34
convnext_small.fb_in22k_ft_in1k,74.490,25.510,92.700,7.300,50.22,288,1.000,bicubic,-10.772,-4.982,-50
davit_base.msft_in1k,74.490,25.510,91.750,8.250,87.95,224,0.950,bicubic,-10.152,-5.270,-3
gcvit_base.in1k,74.480,25.520,91.770,8.230,90.32,224,0.875,bicubic,-9.966,-5.438,+17
resnest200e.in1k,74.470,25.530,91.880,8.120,70.20,320,0.909,bicubic,-9.376,-5.004,+72
regnetz_040_h.ra3_in1k,74.460,25.540,92.250,7.750,28.94,320,1.000,bicubic,-10.032,-4.508,+7
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,74.460,25.540,91.640,8.360,41.72,224,0.950,bicubic,-10.450,-5.318,-21
tf_efficientnetv2_s.in21k_ft_in1k,74.440,25.560,92.500,7.500,21.46,384,1.000,bicubic,-9.848,-4.752,+30
maxvit_rmlp_small_rw_224.sw_in1k,74.390,25.610,91.390,8.610,64.90,224,0.900,bicubic,-10.102,-5.620,+6
convformer_m36.sail_in1k,74.380,25.620,91.480,8.520,57.05,224,1.000,bicubic,-10.114,-5.386,+3
caformer_s18.sail_in22k_ft_in1k,74.370,25.630,92.500,7.500,26.34,224,1.000,bicubic,-9.706,-4.698,+46
flexivit_base.1200ep_in1k,74.350,25.650,91.800,8.200,86.59,240,0.950,bicubic,-10.328,-5.196,-13
resnetrs200.tf_in1k,74.340,25.660,91.940,8.060,93.21,320,1.000,bicubic,-10.104,-5.142,+9
seresnextaa101d_32x8d.ah_in1k,74.320,25.680,91.720,8.280,93.59,288,1.000,bicubic,-10.246,-5.356,-10
maxvit_base_tf_224.in1k,74.290,25.710,91.760,8.240,119.47,224,0.950,bicubic,-10.570,-5.228,-27
regnety_160.swag_lc_in1k,74.220,25.780,92.870,7.130,83.59,224,0.965,bicubic,-9.562,-4.410,+69
convnextv2_tiny.fcmae_ft_in22k_in1k_384,74.220,25.780,92.470,7.530,28.64,384,1.000,bicubic,-10.886,-5.158,-50
mvitv2_base.fb_in1k,74.220,25.780,91.650,8.350,51.47,224,0.900,bicubic,-10.230,-5.208,+2
convformer_s36.sail_in1k,74.210,25.790,91.230,8.770,40.01,224,1.000,bicubic,-9.850,-5.516,+40
convnext_tiny.in12k_ft_in1k,74.200,25.800,92.690,7.310,28.59,288,1.000,bicubic,-10.250,-4.650,-2
resnest269e.in1k,74.200,25.800,91.960,8.040,110.93,416,0.928,bicubic,-10.310,-5.474,-10
seresnext101d_32x8d.ah_in1k,74.180,25.820,91.850,8.150,93.59,288,1.000,bicubic,-10.178,-5.070,+13
coatnet_rmlp_2_rw_224.sw_in1k,74.180,25.820,91.300,8.700,73.88,224,0.950,bicubic,-10.426,-5.440,-20
cait_xs24_384.fb_dist_in1k,74.170,25.830,91.930,8.070,26.67,384,1.000,bicubic,-9.892,-4.954,+34
efficientnetv2_rw_s.ra2_in1k,74.160,25.840,91.710,8.290,23.94,384,1.000,bicubic,-9.648,-5.022,+59
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,74.130,25.870,91.990,8.010,44.18,224,0.875,bilinear,-9.102,-4.768,+112
vit_large_r50_s32_224.augreg_in21k_ft_in1k,74.120,25.880,92.380,7.620,328.99,224,0.900,bicubic,-10.302,-4.790,-2
vit_base_patch16_384.orig_in21k_ft_in1k,74.120,25.880,92.360,7.640,86.86,384,1.000,bicubic,-10.080,-4.860,+19
flexivit_base.600ep_in1k,74.120,25.880,91.750,8.250,86.59,240,0.950,bicubic,-10.402,-5.186,-19
flexivit_base.300ep_in1k,74.120,25.880,91.390,8.610,86.59,240,0.950,bicubic,-10.286,-5.496,+4
xcit_small_12_p16_384.fb_dist_in1k,74.110,25.890,92.090,7.910,26.25,384,1.000,bicubic,-10.602,-5.028,-37
eca_nfnet_l1.ra2_in1k,74.110,25.890,92.070,7.930,41.41,320,1.000,bicubic,-9.900,-4.956,+34
convnext_base.fb_in1k,74.110,25.890,91.710,8.290,88.59,288,1.000,bicubic,-10.318,-5.258,-6
volo_d1_224.sail_in1k,74.100,25.900,92.030,7.970,26.63,224,0.960,bicubic,-10.062,-4.746,+18
vit_base_patch32_clip_384.openai_ft_in12k_in1k,74.070,25.930,92.420,7.580,88.30,384,0.950,bicubic,-11.146,-4.986,-78
vit_base_patch16_224_miil.in21k_ft_in1k,74.040,25.960,91.700,8.300,86.54,224,0.875,bilinear,-10.226,-5.104,+4
xcit_large_24_p8_224.fb_in1k,74.040,25.960,90.890,9.110,188.93,224,1.000,bicubic,-10.354,-5.774,-3
tf_efficientnet_b7.aa_in1k,74.020,25.980,91.860,8.140,66.35,600,0.949,bicubic,-10.394,-5.046,-8
vit_base_patch16_224.augreg_in21k_ft_in1k,74.010,25.990,92.470,7.530,86.57,224,0.900,bicubic,-10.522,-4.822,-32
resnetv2_152x4_bit.goog_in21k_ft_in1k,74.010,25.990,92.340,7.660,936.53,480,1.000,bilinear,-10.906,-5.098,-56
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,73.990,26.010,92.180,7.820,194.03,224,0.875,bilinear,-9.346,-4.666,+90
swinv2_base_window16_256.ms_in1k,73.980,26.020,91.750,8.250,87.92,256,0.900,bicubic,-10.620,-5.340,-39
tf_efficientnetv2_s.in1k,73.980,26.020,91.540,8.460,21.46,384,1.000,bicubic,-9.920,-5.158,+29
regnetz_d32.ra3_in1k,73.960,26.040,91.950,8.050,27.58,320,0.950,bicubic,-10.062,-4.918,+20
seresnext101_32x8d.ah_in1k,73.960,26.040,91.450,8.550,93.57,288,1.000,bicubic,-10.226,-5.424,+5
resnetv2_152x2_bit.goog_in21k_ft_in1k,73.950,26.050,92.670,7.330,236.34,448,1.000,bilinear,-10.560,-4.322,-36
crossvit_18_dagger_408.in1k,73.930,26.070,91.410,8.590,44.61,408,1.000,bicubic,-10.268,-5.406,+2
rexnetr_300.sw_in12k_ft_in1k,73.920,26.080,92.210,7.790,34.81,288,1.000,bicubic,-10.624,-5.046,-41
xcit_small_12_p8_224.fb_dist_in1k,73.920,26.080,91.720,8.280,26.21,224,1.000,bicubic,-10.316,-5.152,-4
resnetrs420.tf_in1k,73.900,26.100,91.780,8.220,191.89,416,1.000,bicubic,-11.106,-5.344,-75
resmlp_big_24_224.fb_in22k_ft_in1k,73.900,26.100,91.760,8.240,129.14,224,0.875,bicubic,-10.498,-5.352,-18
pit_b_distilled_224.in1k,73.900,26.100,90.780,9.220,74.79,224,0.900,bicubic,-9.866,-5.686,+39
tf_efficientnet_b6.aa_in1k,73.890,26.110,91.750,8.250,43.04,528,0.942,bicubic,-10.226,-5.130,+2
edgenext_base.usi_in1k,73.880,26.120,91.760,8.240,18.51,320,1.000,bicubic,-10.078,-5.010,+14
regnety_1280.seer_ft_in1k,73.860,26.140,91.900,8.100,644.81,384,1.000,bicubic,-10.572,-5.192,-31
tf_efficientnet_b3.ns_jft_in1k,73.850,26.150,91.860,8.140,12.23,300,0.904,bicubic,-10.206,-5.058,+4
deit3_small_patch16_224.fb_in22k_ft_in1k,73.840,26.160,91.970,8.030,22.06,224,1.000,bicubic,-9.234,-4.806,+98
maxvit_rmlp_tiny_rw_256.sw_in1k,73.830,26.170,91.440,8.560,29.15,256,0.950,bicubic,-10.394,-5.428,-11
vit_small_r26_s32_384.augreg_in21k_ft_in1k,73.790,26.210,92.300,7.700,36.47,384,1.000,bicubic,-10.258,-5.028,+4
davit_small.msft_in1k,73.780,26.220,91.560,8.440,49.75,224,0.950,bicubic,-10.472,-5.380,-18
regnetz_d8.ra3_in1k,73.760,26.240,92.010,7.990,23.37,320,1.000,bicubic,-10.294,-4.986,0
maxvit_small_tf_224.in1k,73.760,26.240,91.420,8.580,68.93,224,0.950,bicubic,-10.666,-5.404,-34
regnety_080.ra3_in1k,73.750,26.250,91.800,8.200,39.18,288,1.000,bicubic,-10.176,-5.090,+6
focalnet_base_lrf.ms_in1k,73.740,26.260,90.990,9.010,88.75,224,0.900,bicubic,-10.098,-5.618,+15
resnetrs270.tf_in1k,73.730,26.270,91.580,8.420,129.86,352,1.000,bicubic,-10.698,-5.388,-39
gcvit_small.in1k,73.700,26.300,91.230,8.770,51.09,224,0.875,bicubic,-10.192,-5.428,+8
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,73.670,26.330,92.140,7.860,88.79,224,0.875,bilinear,-9.028,-4.004,+119
resnet200d.ra2_in1k,73.670,26.330,91.570,8.430,64.69,320,1.000,bicubic,-10.294,-5.254,0
resnetv2_101x3_bit.goog_in21k_ft_in1k,73.660,26.340,92.470,7.530,387.93,448,1.000,bilinear,-10.778,-4.912,-46
xcit_medium_24_p16_224.fb_dist_in1k,73.650,26.350,91.570,8.430,84.40,224,1.000,bicubic,-10.636,-5.362,-31
convnextv2_tiny.fcmae_ft_in22k_in1k,73.640,26.360,91.960,8.040,28.64,288,1.000,bicubic,-10.776,-5.300,-42
edgenext_base.in21k_ft_in1k,73.610,26.390,91.860,8.140,18.51,320,1.000,bicubic,-10.444,-5.336,-11
regnety_064.ra3_in1k,73.610,26.390,91.330,8.670,30.58,288,1.000,bicubic,-10.110,-5.392,+22
tresnet_v2_l.miil_in21k_ft_in1k,73.580,26.420,90.980,9.020,46.17,224,0.875,bilinear,-10.320,-5.514,-1
swinv2_base_window8_256.ms_in1k,73.550,26.450,91.510,8.490,87.92,256,0.900,bicubic,-10.700,-5.414,-32
tf_efficientnet_b5.ra_in1k,73.540,26.460,91.460,8.540,30.39,456,0.934,bicubic,-10.270,-5.294,+7
resnetaa101d.sw_in12k_ft_in1k,73.520,26.480,91.750,8.250,44.57,288,1.000,bicubic,-10.604,-5.356,-24
resnet152d.ra2_in1k,73.520,26.480,91.240,8.760,60.21,320,1.000,bicubic,-10.164,-5.500,+25
cs3se_edgenet_x.c2ns_in1k,73.510,26.490,91.490,8.510,50.72,320,1.000,bicubic,-10.032,-5.182,+31
regnetz_d8_evos.ch_in1k,73.490,26.510,91.710,8.290,23.46,320,1.000,bicubic,-10.636,-5.302,-28
deit3_base_patch16_224.fb_in1k,73.490,26.510,91.280,8.720,86.59,224,0.900,bicubic,-10.296,-5.308,+6
regnetv_064.ra3_in1k,73.460,26.540,91.600,8.400,30.58,288,1.000,bicubic,-10.256,-5.144,+14
convnext_small.fb_in1k,73.450,26.550,91.330,8.670,50.22,288,1.000,bicubic,-10.250,-5.478,+15
coat_lite_medium.in1k,73.450,26.550,91.240,8.760,44.57,224,0.900,bicubic,-10.150,-5.488,+24
sequencer2d_l.in1k,73.450,26.550,91.090,8.910,54.30,224,0.875,bicubic,-9.944,-5.406,+39
xcit_tiny_24_p8_384.fb_dist_in1k,73.430,26.570,91.560,8.440,12.11,384,1.000,bicubic,-10.316,-5.150,+6
twins_svt_large.in1k,73.420,26.580,90.880,9.120,99.27,224,0.900,bicubic,-10.258,-5.710,+16
resnetrs350.tf_in1k,73.400,26.600,91.300,8.700,163.96,384,1.000,bicubic,-11.314,-5.692,-94
pvt_v2_b4.in1k,73.400,26.600,91.070,8.930,62.56,224,0.900,bicubic,-10.310,-5.604,+9
regnety_160.deit_in1k,73.390,26.610,91.700,8.300,83.59,288,1.000,bicubic,-10.300,-5.082,+10
tf_efficientnet_b5.aa_in1k,73.380,26.620,91.210,8.790,30.39,456,0.934,bicubic,-10.308,-5.502,+10
swin_s3_base_224.ms_in1k,73.350,26.650,91.180,8.820,71.13,224,0.900,bicubic,-10.570,-5.492,-22
convformer_s18.sail_in22k_ft_in1k,73.340,26.660,91.910,8.090,26.77,224,1.000,bicubic,-10.398,-5.138,+1
efficientnet_b4.ra2_in1k,73.340,26.660,91.270,8.730,19.34,384,1.000,bicubic,-10.074,-5.328,+27
gcvit_tiny.in1k,73.330,26.670,90.970,9.030,28.22,224,0.875,bicubic,-10.054,-5.428,+31
mvitv2_small.fb_in1k,73.300,26.700,91.230,8.770,34.87,224,0.900,bicubic,-10.470,-5.346,-8
resnet152.a1h_in1k,73.300,26.700,91.180,8.820,60.19,288,1.000,bicubic,-10.150,-5.358,+19
resmlp_big_24_224.fb_distilled_in1k,73.300,26.700,91.170,8.830,129.14,224,0.875,bicubic,-10.292,-5.480,+12
swin_small_patch4_window7_224.ms_in22k_ft_in1k,73.290,26.710,92.030,7.970,49.61,224,0.900,bicubic,-10.008,-4.934,+36
vit_small_patch16_384.augreg_in21k_ft_in1k,73.280,26.720,91.990,8.010,22.20,384,1.000,bicubic,-10.522,-5.108,-15
xcit_small_24_p16_224.fb_dist_in1k,73.270,26.730,91.460,8.540,47.67,224,1.000,bicubic,-10.604,-5.276,-27
focalnet_base_srf.ms_in1k,73.270,26.730,91.260,8.740,88.15,224,0.900,bicubic,-10.548,-5.422,-19
swinv2_small_window16_256.ms_in1k,73.250,26.750,91.290,8.710,49.73,256,0.900,bicubic,-10.974,-5.488,-58
pvt_v2_b5.in1k,73.250,26.750,91.080,8.920,81.96,224,0.900,bicubic,-10.492,-5.554,-10
deit_base_distilled_patch16_224.fb_in1k,73.240,26.760,91.010,8.990,87.34,224,0.900,bicubic,-10.146,-5.478,+20
maxvit_tiny_rw_224.sw_in1k,73.230,26.770,90.770,9.230,29.06,224,0.950,bicubic,-10.274,-5.734,+6
pvt_v2_b3.in1k,73.210,26.790,91.010,8.990,45.24,224,0.900,bicubic,-9.910,-5.544,+44
resnetrs152.tf_in1k,73.200,26.800,91.260,8.740,86.62,320,1.000,bicubic,-10.502,-5.352,-10
swin_base_patch4_window12_384.ms_in1k,73.160,26.840,91.130,8.870,87.90,384,1.000,bicubic,-11.316,-5.762,-92
vit_base_patch32_384.augreg_in21k_ft_in1k,73.130,26.870,91.250,8.750,88.30,384,1.000,bicubic,-10.220,-5.588,+19
nest_base_jx.goog_in1k,73.130,26.870,91.070,8.930,67.72,224,0.875,bicubic,-10.404,-5.304,+1
xcit_medium_24_p8_224.fb_in1k,73.130,26.870,90.280,9.720,84.32,224,1.000,bicubic,-10.618,-6.120,-21
regnety_640.seer_ft_in1k,73.110,26.890,91.520,8.480,281.38,384,1.000,bicubic,-10.798,-5.402,-42
swinv2_small_window8_256.ms_in1k,73.100,26.900,90.950,9.050,49.73,256,0.900,bicubic,-10.754,-5.694,-38
deit3_small_patch16_384.fb_in1k,73.090,26.910,91.220,8.780,22.21,384,1.000,bicubic,-10.340,-5.454,+4
xcit_small_24_p8_224.fb_in1k,73.090,26.910,91.160,8.840,47.63,224,1.000,bicubic,-10.744,-5.472,-35
cait_s24_224.fb_dist_in1k,73.060,26.940,91.120,8.880,46.92,224,1.000,bicubic,-10.382,-5.454,+1
efficientformerv2_l.snap_dist_in1k,73.060,26.940,90.880,9.120,26.32,224,0.950,bicubic,-10.572,-5.680,-13
focalnet_small_srf.ms_in1k,73.060,26.940,90.730,9.270,49.89,224,0.900,bicubic,-10.356,-5.708,+1
coatnet_rmlp_1_rw_224.sw_in1k,73.040,26.960,90.880,9.120,41.69,224,0.950,bicubic,-10.322,-5.570,+9
crossvit_15_dagger_408.in1k,72.960,27.040,91.090,8.910,28.50,408,1.000,bicubic,-10.882,-5.688,-44
caformer_s18.sail_in1k,72.960,27.040,91.010,8.990,26.34,224,1.000,bicubic,-10.696,-5.506,-17
regnety_320.tv2_in1k,72.940,27.060,90.760,9.240,145.05,224,0.965,bicubic,-10.222,-5.656,+20
ecaresnet101d.miil_in1k,72.930,27.070,91.180,8.820,44.57,288,0.950,bicubic,-10.054,-5.362,+41
regnetv_040.ra3_in1k,72.920,27.080,91.130,8.870,20.64,288,1.000,bicubic,-10.278,-5.528,+15
tf_efficientnet_b4.ap_in1k,72.900,27.100,90.980,9.020,19.34,380,0.922,bicubic,-10.350,-5.414,+9
maxvit_tiny_tf_224.in1k,72.900,27.100,90.830,9.170,30.92,224,0.950,bicubic,-10.502,-5.760,-5
coatnet_1_rw_224.sw_in1k,72.900,27.100,90.790,9.210,41.72,224,0.950,bicubic,-10.696,-5.592,-18
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,72.880,27.120,91.550,8.450,236.34,384,1.000,bicubic,-10.954,-5.576,-49
swinv2_cr_small_ns_224.sw_in1k,72.850,27.150,90.820,9.180,49.70,224,0.900,bicubic,-10.648,-5.664,-17
regnety_032.ra_in1k,72.780,27.220,90.950,9.050,19.44,288,1.000,bicubic,-9.948,-5.466,+53
regnety_040.ra3_in1k,72.780,27.220,90.750,9.250,20.65,288,1.000,bicubic,-10.264,-5.748,+26
convnextv2_tiny.fcmae_ft_in1k,72.770,27.230,91.180,8.820,28.64,288,1.000,bicubic,-10.694,-5.538,-17
xception65p.ra3_in1k,72.760,27.240,90.910,9.090,39.82,299,0.940,bicubic,-10.376,-5.198,+12
swin_s3_small_224.ms_in1k,72.720,27.280,90.560,9.440,49.74,224,0.900,bicubic,-11.036,-5.892,-45
resnetv2_101.a1h_in1k,72.700,27.300,90.670,9.330,44.54,288,1.000,bicubic,-10.302,-5.784,+26
efficientformer_l7.snap_dist_in1k,72.660,27.340,90.800,9.200,82.23,224,0.950,bicubic,-10.722,-5.736,-10
xcit_small_12_p8_224.fb_in1k,72.630,27.370,90.670,9.330,26.21,224,1.000,bicubic,-10.702,-5.814,-5
nfnet_l0.ra2_in1k,72.610,27.390,90.990,9.010,35.07,288,1.000,bicubic,-10.142,-5.526,+42
resnext101_64x4d.c1_in1k,72.610,27.390,90.830,9.170,83.46,288,1.000,bicubic,-10.546,-5.544,+2
focalnet_small_lrf.ms_in1k,72.610,27.390,90.720,9.280,50.34,224,0.900,bicubic,-10.884,-5.860,-25
pnasnet5large.tf_in1k,72.610,27.390,90.500,9.500,86.06,331,0.911,bicubic,-10.172,-5.540,+37
xception65.ra3_in1k,72.590,27.410,90.840,9.160,39.92,299,0.940,bicubic,-10.592,-5.752,-2
cs3sedarknet_x.c2ns_in1k,72.580,27.420,91.060,8.940,35.40,288,1.000,bicubic,-10.078,-5.290,+48
resnest101e.in1k,72.580,27.420,90.810,9.190,48.28,256,0.875,bilinear,-10.304,-5.512,+25
twins_pcpvt_large.in1k,72.580,27.420,90.700,9.300,60.99,224,0.900,bicubic,-10.550,-5.902,+2
gc_efficientnetv2_rw_t.agc_in1k,72.570,27.430,90.830,9.170,13.68,288,1.000,bicubic,-9.882,-5.462,+76
tf_efficientnet_b5.in1k,72.560,27.440,91.110,8.890,30.39,456,0.934,bicubic,-10.616,-5.426,-7
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,72.560,27.440,90.840,9.160,25.03,224,0.875,bilinear,-9.614,-5.382,+112
tresnet_xl.miil_in1k_448,72.560,27.440,90.310,9.690,78.44,448,0.875,bilinear,-10.500,-5.864,+7
twins_svt_base.in1k,72.550,27.450,90.450,9.550,56.07,224,0.900,bicubic,-10.572,-5.966,-1
deit_base_patch16_384.fb_in1k,72.540,27.460,90.260,9.740,86.86,384,1.000,bicubic,-10.566,-6.108,+1
resnetv2_50x3_bit.goog_in21k_ft_in1k,72.520,27.480,91.760,8.240,217.32,448,1.000,bilinear,-11.500,-5.366,-88
xcit_small_12_p16_224.fb_dist_in1k,72.520,27.480,91.130,8.870,26.25,224,1.000,bicubic,-10.808,-5.286,-21
rexnet_300.nav_in1k,72.520,27.480,90.630,9.370,34.71,224,0.875,bicubic,-10.254,-5.606,+26
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,72.510,27.490,90.880,9.120,88.22,224,0.900,bicubic,-10.786,-5.648,-22
deit3_medium_patch16_224.fb_in1k,72.510,27.490,90.810,9.190,38.85,224,0.900,bicubic,-10.576,-5.486,-3
convformer_s18.sail_in1k,72.510,27.490,90.510,9.490,26.77,224,1.000,bicubic,-10.476,-5.740,+8
convnext_tiny.fb_in22k_ft_in1k_384,72.470,27.530,91.540,8.460,28.59,384,1.000,bicubic,-11.618,-5.604,-103
regnety_080_tv.tv2_in1k,72.460,27.540,90.540,9.460,39.38,224,0.965,bicubic,-10.130,-5.478,+43
xcit_tiny_24_p8_224.fb_dist_in1k,72.440,27.560,90.920,9.080,12.11,224,1.000,bicubic,-10.126,-5.138,+51
sequencer2d_m.in1k,72.430,27.570,90.710,9.290,38.31,224,0.875,bicubic,-10.382,-5.564,+12
resnet101d.ra2_in1k,72.430,27.570,90.650,9.350,44.57,320,1.000,bicubic,-10.586,-5.802,-1
regnetx_320.tv2_in1k,72.420,27.580,90.300,9.700,107.81,224,0.965,bicubic,-10.390,-5.908,+12
maxxvit_rmlp_nano_rw_256.sw_in1k,72.370,27.630,90.750,9.250,16.78,256,0.950,bicubic,-10.672,-5.600,-6
nest_small_jx.goog_in1k,72.350,27.650,90.690,9.310,38.35,224,0.875,bicubic,-10.774,-5.630,-18
maxvit_rmlp_nano_rw_256.sw_in1k,72.350,27.650,90.430,9.570,15.50,256,0.950,bicubic,-10.604,-5.836,0
resnext101_32x8d.tv2_in1k,72.330,27.670,90.270,9.730,88.79,224,0.965,bilinear,-10.502,-5.962,+5
tf_efficientnet_b4.aa_in1k,72.290,27.710,90.590,9.410,19.34,380,0.922,bicubic,-10.728,-5.710,-9
tf_efficientnet_b2.ns_jft_in1k,72.280,27.720,91.110,8.890,9.11,260,0.890,bicubic,-10.098,-5.144,+60
hrnet_w18_ssld.paddle_in1k,72.270,27.730,90.820,9.180,21.30,288,1.000,bilinear,-9.776,-5.430,+101
maxvit_nano_rw_256.sw_in1k,72.270,27.730,90.570,9.430,15.45,256,0.950,bicubic,-10.658,-5.650,-4
tresnet_m.miil_in21k_ft_in1k,72.270,27.730,90.230,9.770,31.39,224,0.875,bilinear,-10.804,-5.884,-17
swin_base_patch4_window7_224.ms_in1k,72.260,27.740,90.810,9.190,87.77,224,0.900,bicubic,-11.346,-5.642,-69
resnext101_64x4d.tv_in1k,72.260,27.740,90.550,9.450,83.46,224,0.875,bilinear,-10.732,-5.694,-11
resnetv2_50x1_bit.goog_distilled_in1k,72.240,27.760,91.000,9.000,25.55,224,0.875,bicubic,-10.580,-5.514,-4
nasnetalarge.tf_in1k,72.240,27.760,90.460,9.540,88.75,331,0.911,bicubic,-10.386,-5.582,+20
crossvit_18_240.in1k,72.240,27.760,90.270,9.730,43.27,240,0.875,bicubic,-10.156,-5.790,+51
efficientnetv2_rw_t.ra2_in1k,72.230,27.770,90.420,9.580,13.65,288,1.000,bicubic,-10.120,-5.772,+56
regnetz_c16_evos.ch_in1k,72.220,27.780,91.210,8.790,13.49,320,0.950,bicubic,-10.412,-5.264,+16
cait_xxs36_384.fb_dist_in1k,72.190,27.810,90.840,9.160,17.37,384,1.000,bicubic,-10.014,-5.304,+75
twins_pcpvt_base.in1k,72.190,27.810,90.500,9.500,43.83,224,0.900,bicubic,-10.520,-5.846,+3
crossvit_18_dagger_240.in1k,72.180,27.820,90.080,9.920,44.27,240,0.875,bicubic,-10.336,-5.986,+34
regnetz_c16.ra3_in1k,72.170,27.830,91.110,8.890,13.46,320,1.000,bicubic,-10.464,-5.212,+10
maxxvitv2_nano_rw_256.sw_in1k,72.170,27.830,90.470,9.530,23.70,256,0.950,bicubic,-10.940,-5.854,-32
regnety_320.seer_ft_in1k,72.160,27.840,90.880,9.120,145.05,384,1.000,bicubic,-11.174,-5.826,-54
resnet101.a1h_in1k,72.100,27.900,90.820,9.180,44.55,288,1.000,bicubic,-10.678,-5.490,-8
regnety_160.tv2_in1k,72.090,27.910,90.230,9.770,83.59,224,0.965,bicubic,-10.552,-5.986,+5
xcit_tiny_24_p16_384.fb_dist_in1k,72.070,27.930,90.580,9.420,12.12,384,1.000,bicubic,-10.500,-5.696,+22
cs3edgenet_x.c2_in1k,72.050,27.950,90.370,9.630,47.82,288,1.000,bicubic,-10.658,-6.000,-4
vit_relpos_medium_patch16_cls_224.sw_in1k,72.020,27.980,90.300,9.700,38.76,224,0.900,bicubic,-10.552,-5.768,+19
davit_tiny.msft_in1k,72.010,27.990,90.070,9.930,28.36,224,0.950,bicubic,-10.686,-6.204,-3
convnext_tiny_hnf.a2h_in1k,72.000,28.000,89.770,10.230,28.59,288,1.000,bicubic,-10.584,-6.238,+13
mobilevitv2_200.cvnets_in22k_ft_in1k_384,71.990,28.010,90.650,9.350,18.45,384,1.000,bicubic,-11.410,-5.932,-71
efficientformer_l3.snap_dist_in1k,71.980,28.020,90.270,9.730,31.41,224,0.950,bicubic,-10.570,-5.978,+18
convnext_tiny.fb_in1k,71.980,28.020,90.220,9.780,28.59,288,1.000,bicubic,-10.718,-6.412,-7
vit_relpos_base_patch16_clsgap_224.sw_in1k,71.970,28.030,90.250,9.750,86.43,224,0.900,bicubic,-10.790,-5.922,-16
sequencer2d_s.in1k,71.940,28.060,90.500,9.500,27.65,224,0.875,bicubic,-10.400,-5.530,+38
resnet152.a2_in1k,71.940,28.060,89.420,10.580,60.19,288,1.000,bicubic,-10.668,-6.708,+2
rexnetr_200.sw_in12k_ft_in1k,71.900,28.100,91.270,8.730,16.52,288,1.000,bicubic,-11.236,-5.366,-57
convnext_nano.in12k_ft_in1k,71.900,28.100,91.000,9.000,15.59,288,1.000,bicubic,-10.962,-5.556,-28
dm_nfnet_f0.dm_in1k,71.900,28.100,90.740,9.260,71.49,256,0.900,bicubic,-11.586,-5.828,-86
swinv2_cr_small_224.sw_in1k,71.900,28.100,90.270,9.730,49.70,224,0.900,bicubic,-11.236,-6.214,-56
convnextv2_nano.fcmae_ft_in22k_in1k,71.890,28.110,90.890,9.110,15.62,288,1.000,bicubic,-10.774,-5.630,-13
resnet101.a1_in1k,71.850,28.150,89.150,10.850,44.55,288,1.000,bicubic,-10.472,-6.482,+35
eca_nfnet_l0.ra2_in1k,71.840,28.160,91.110,8.890,24.14,288,1.000,bicubic,-10.738,-5.384,+3
mobilevitv2_175.cvnets_in22k_ft_in1k_384,71.840,28.160,90.770,9.230,14.25,384,1.000,bicubic,-11.098,-5.656,-40
vit_relpos_base_patch16_224.sw_in1k,71.820,28.180,90.250,9.750,86.43,224,0.900,bicubic,-10.676,-5.888,+11
regnetx_160.tv2_in1k,71.800,28.200,90.050,9.950,54.28,224,0.965,bicubic,-10.766,-6.122,+4
seresnext50_32x4d.racm_in1k,71.800,28.200,90.030,9.970,27.56,288,0.950,bicubic,-10.396,-6.118,+47
swin_small_patch4_window7_224.ms_in1k,71.770,28.230,90.260,9.740,49.61,224,0.900,bicubic,-11.438,-6.056,-72
coat_small.in1k,71.750,28.250,90.410,9.590,21.69,224,0.900,bicubic,-10.612,-5.798,+19
mvitv2_tiny.fb_in1k,71.740,28.260,90.310,9.690,24.17,224,0.900,bicubic,-10.670,-5.842,+12
flexivit_small.1200ep_in1k,71.720,28.280,90.260,9.740,22.06,240,0.950,bicubic,-10.800,-5.864,+2
flexivit_small.600ep_in1k,71.710,28.290,90.150,9.850,22.06,240,0.950,bicubic,-10.652,-5.932,+16
flexivit_small.300ep_in1k,71.710,28.290,89.980,10.020,22.06,240,0.950,bicubic,-10.468,-6.056,+44
pit_b_224.in1k,71.710,28.290,89.240,10.760,73.76,224,0.900,bicubic,-10.728,-6.476,+7
xcit_large_24_p16_224.fb_in1k,71.710,28.290,89.170,10.830,189.10,224,1.000,bicubic,-11.190,-6.716,-49
resnet50.fb_swsl_ig1b_ft_in1k,71.700,28.300,90.490,9.510,25.56,224,0.875,bilinear,-9.482,-5.502,+130
pvt_v2_b2_li.in1k,71.700,28.300,90.010,9.990,22.55,224,0.900,bicubic,-10.494,-6.082,+39
ecaresnet101d_pruned.miil_in1k,71.680,28.320,90.430,9.570,24.88,288,0.950,bicubic,-10.318,-5.730,+55
coatnet_bn_0_rw_224.sw_in1k,71.670,28.330,90.380,9.620,27.44,224,0.950,bicubic,-10.730,-5.806,+4
gcvit_xtiny.in1k,71.670,28.330,90.240,9.760,19.98,224,0.875,bicubic,-10.284,-5.728,+59
tresnet_xl.miil_in1k,71.650,28.350,89.630,10.370,78.44,224,0.875,bilinear,-10.424,-6.296,+45
resnet61q.ra2_in1k,71.630,28.370,90.280,9.720,36.85,288,1.000,bicubic,-10.894,-5.850,-10
tresnet_l.miil_in1k_448,71.630,28.370,90.030,9.970,55.99,448,0.875,bilinear,-10.646,-5.948,+22
poolformerv2_m48.sail_in1k,71.620,28.380,89.800,10.200,73.35,224,1.000,bicubic,-11.000,-6.272,-29
convnextv2_nano.fcmae_ft_in22k_in1k_384,71.590,28.410,90.760,9.240,15.62,384,1.000,bicubic,-11.784,-5.984,-100
xcit_tiny_12_p8_384.fb_dist_in1k,71.590,28.410,90.700,9.300,6.71,384,1.000,bicubic,-10.798,-5.520,-1
swinv2_tiny_window16_256.ms_in1k,71.590,28.410,90.330,9.670,28.35,256,0.900,bicubic,-11.214,-5.906,-52
convit_base.fb_in1k,71.580,28.420,90.120,9.880,86.54,224,0.875,bicubic,-10.710,-5.816,+13
coatnet_0_rw_224.sw_in1k,71.570,28.430,89.400,10.600,27.44,224,0.950,bicubic,-10.820,-6.436,-4
resnetv2_50d_evos.ah_in1k,71.560,28.440,90.090,9.910,25.59,288,1.000,bicubic,-10.440,-5.810,+42
resnet152.tv2_in1k,71.530,28.470,89.970,10.030,60.19,224,0.965,bilinear,-10.758,-6.034,+11
crossvit_15_dagger_240.in1k,71.520,28.480,89.850,10.150,28.21,240,0.875,bicubic,-10.810,-6.110,+4
poolformer_m48.sail_in1k,71.520,28.480,89.770,10.230,73.47,224,0.950,bicubic,-10.962,-6.196,-15
fbnetv3_g.ra2_in1k,71.510,28.490,90.380,9.620,16.62,288,0.950,bilinear,-10.526,-5.678,+36
resnetaa50d.sw_in12k_ft_in1k,71.500,28.500,90.320,9.680,25.58,288,1.000,bicubic,-11.100,-6.178,-37
mobilevitv2_150.cvnets_in22k_ft_in1k_384,71.490,28.510,90.420,9.580,10.59,384,1.000,bicubic,-11.096,-5.894,-34
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,71.480,28.520,90.470,9.530,88.79,224,0.875,bilinear,-10.126,-5.570,+66
wide_resnet50_2.racm_in1k,71.480,28.520,90.220,9.780,68.88,288,0.950,bicubic,-10.800,-5.844,+6
efficientnet_b3.ra2_in1k,71.470,28.530,90.060,9.940,12.23,320,1.000,bicubic,-10.772,-6.058,+7
pvt_v2_b2.in1k,71.450,28.550,90.060,9.940,25.36,224,0.900,bicubic,-10.636,-5.896,+25
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,71.420,28.580,90.530,9.470,194.03,224,0.875,bilinear,-10.422,-5.562,+45
resnet51q.ra2_in1k,71.410,28.590,90.180,9.820,35.70,288,1.000,bilinear,-10.950,-6.002,-12
wide_resnet101_2.tv2_in1k,71.370,28.630,89.790,10.210,126.89,224,0.965,bilinear,-11.128,-6.228,-28
xcit_tiny_24_p8_224.fb_in1k,71.350,28.650,90.230,9.770,12.11,224,1.000,bicubic,-10.542,-5.740,+37
vit_relpos_medium_patch16_224.sw_in1k,71.350,28.650,89.960,10.040,38.75,224,0.900,bicubic,-11.112,-6.122,-26
resnet152.a1_in1k,71.350,28.650,89.310,10.690,60.19,288,1.000,bicubic,-11.382,-6.410,-64
tf_efficientnetv2_b3.in21k_ft_in1k,71.340,28.660,90.750,9.250,14.36,300,0.900,bicubic,-11.330,-5.876,-60
tf_efficientnet_b4.in1k,71.330,28.670,90.110,9.890,19.34,380,0.922,bicubic,-11.278,-5.642,-52
vit_base_patch16_224.orig_in21k_ft_in1k,71.320,28.680,90.480,9.520,86.57,224,0.900,bicubic,-10.466,-5.644,+42
mixer_b16_224.miil_in21k_ft_in1k,71.300,28.700,89.650,10.350,59.88,224,0.875,bilinear,-11.006,-6.070,-10
pit_s_distilled_224.in1k,71.260,28.740,89.640,10.360,24.04,224,0.900,bicubic,-10.548,-6.088,+38
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,71.250,28.750,90.470,9.530,236.34,224,0.875,bicubic,-11.626,-6.112,-84
ecaresnet50t.ra2_in1k,71.240,28.760,90.450,9.550,25.57,320,0.950,bicubic,-11.112,-5.690,-22
convmixer_1536_20.in1k,71.230,28.770,89.450,10.550,51.63,224,0.960,bicubic,-10.132,-6.164,+78
deit_base_patch16_224.fb_in1k,71.210,28.790,89.180,10.820,86.57,224,0.900,bicubic,-10.782,-6.556,+19
xcit_small_12_p16_224.fb_in1k,71.200,28.800,89.750,10.250,26.25,224,1.000,bicubic,-10.770,-6.038,+20
ecaresnet50t.a1_in1k,71.190,28.810,89.580,10.420,25.57,288,1.000,bicubic,-10.938,-6.062,+5
vit_relpos_medium_patch16_rpn_224.sw_in1k,71.170,28.830,90.080,9.920,38.73,224,0.900,bicubic,-11.140,-5.892,-21
crossvit_base_240.in1k,71.170,28.830,89.830,10.170,105.03,240,0.875,bicubic,-11.046,-6.094,-7
resnext50_32x4d.a1h_in1k,71.170,28.830,89.700,10.300,25.03,288,1.000,bicubic,-10.844,-6.234,+12
vit_base_patch32_clip_224.laion2b_ft_in1k,71.160,28.840,90.210,9.790,88.22,224,0.900,bicubic,-11.422,-5.990,-57
swin_s3_tiny_224.ms_in1k,71.150,28.850,89.710,10.290,28.33,224,0.900,bicubic,-10.994,-6.244,-2
cs3sedarknet_l.c2ns_in1k,71.110,28.890,90.350,9.650,21.91,288,0.950,bicubic,-10.674,-5.608,+29
halo2botnet50ts_256.a1h_in1k,71.110,28.890,89.600,10.400,22.64,256,0.950,bicubic,-10.952,-6.034,+3
efficientformerv2_s2.snap_dist_in1k,71.100,28.900,90.140,9.860,12.71,224,0.950,bicubic,-11.066,-5.770,-7
cs3darknet_x.c2ns_in1k,71.090,28.910,90.150,9.850,35.05,288,1.000,bicubic,-11.132,-6.080,-17
mobilevitv2_200.cvnets_in22k_ft_in1k,71.090,28.910,89.700,10.300,18.45,256,0.888,bicubic,-11.242,-6.242,-32
ecaresnet50d.miil_in1k,71.080,28.920,90.240,9.760,25.58,288,0.950,bicubic,-10.570,-5.642,+31
focalnet_tiny_lrf.ms_in1k,71.060,28.940,89.570,10.430,28.65,224,0.900,bicubic,-11.094,-6.378,-10
convnextv2_nano.fcmae_ft_in1k,71.050,28.950,90.100,9.900,15.62,288,1.000,bicubic,-11.436,-6.126,-53
focalnet_tiny_srf.ms_in1k,71.050,28.950,89.600,10.400,28.43,224,0.900,bicubic,-11.084,-6.368,-10
xcit_tiny_12_p8_224.fb_dist_in1k,71.040,28.960,89.890,10.110,6.71,224,1.000,bicubic,-10.172,-5.712,+73
xcit_small_24_p16_224.fb_in1k,71.040,28.960,89.680,10.320,47.67,224,1.000,bicubic,-11.540,-6.332,-67
tresnet_m.miil_in1k_448,71.020,28.980,88.680,11.320,31.39,448,0.875,bilinear,-10.688,-6.894,+20
resnetv2_101x1_bit.goog_in21k_ft_in1k,71.010,28.990,91.080,8.920,44.54,448,1.000,bilinear,-11.332,-5.440,-42
visformer_small.in1k,71.010,28.990,89.440,10.560,40.22,224,0.900,bicubic,-11.092,-6.258,-13
xcit_medium_24_p16_224.fb_in1k,70.990,29.010,89.530,10.470,84.40,224,1.000,bicubic,-11.650,-6.454,-88
resnet101.a2_in1k,70.990,29.010,89.160,10.840,44.55,288,1.000,bicubic,-11.246,-6.570,-30
lamhalobotnet50ts_256.a1h_in1k,70.990,29.010,89.040,10.960,22.57,256,0.950,bicubic,-10.564,-6.450,+31
edgenext_small.usi_in1k,70.980,29.020,89.880,10.120,5.59,320,1.000,bicubic,-10.584,-5.832,+27
resnetv2_50d_gn.ah_in1k,70.960,29.040,89.830,10.170,25.57,288,1.000,bicubic,-10.998,-6.098,-4
tnt_s_patch16_224,70.960,29.040,89.610,10.390,23.76,224,0.900,bicubic,-10.576,-6.126,+27
convnext_nano.d1h_in1k,70.960,29.040,89.430,10.570,15.59,288,1.000,bicubic,-10.522,-6.228,+36
resnest50d_4s2x40d.in1k,70.940,29.060,89.720,10.280,30.42,224,0.875,bicubic,-10.180,-5.840,+69
coatnet_nano_rw_224.sw_in1k,70.940,29.060,89.700,10.300,15.14,224,0.900,bicubic,-10.758,-5.946,+11
vit_srelpos_medium_patch16_224.sw_in1k,70.920,29.080,89.940,10.060,38.74,224,0.900,bicubic,-11.320,-6.002,-40
tf_efficientnet_b3.ap_in1k,70.920,29.080,89.430,10.570,12.23,300,0.904,bicubic,-10.906,-6.196,+1
vit_small_patch16_224.augreg_in21k_ft_in1k,70.910,29.090,90.150,9.850,22.05,224,0.900,bicubic,-10.474,-5.986,+41
coatnext_nano_rw_224.sw_in1k,70.890,29.110,90.250,9.750,14.70,224,0.900,bicubic,-11.052,-5.666,-10
coatnet_rmlp_nano_rw_224.sw_in1k,70.890,29.110,89.920,10.080,15.15,224,0.900,bicubic,-11.160,-5.960,-21
vit_base_patch16_rpn_224.sw_in1k,70.880,29.120,89.770,10.230,86.54,224,0.900,bicubic,-11.326,-6.228,-39
vit_large_patch32_384.orig_in21k_ft_in1k,70.870,29.130,90.570,9.430,306.63,384,1.000,bicubic,-10.640,-5.520,+23
tf_efficientnet_b1.ns_jft_in1k,70.860,29.140,90.120,9.880,7.79,240,0.882,bicubic,-10.534,-5.616,+33
nest_tiny_jx.goog_in1k,70.860,29.140,89.940,10.060,17.06,224,0.875,bicubic,-10.566,-5.678,+31
rexnet_200.nav_in1k,70.860,29.140,89.710,10.290,16.37,224,0.875,bicubic,-10.776,-5.954,+7
resnetrs101.tf_in1k,70.850,29.150,89.830,10.170,63.62,288,0.940,bicubic,-11.434,-6.184,-54
poolformerv2_m36.sail_in1k,70.850,29.150,89.330,10.670,56.08,224,1.000,bicubic,-11.366,-6.502,-46
regnety_032.tv2_in1k,70.840,29.160,89.850,10.150,19.44,224,0.965,bicubic,-10.916,-5.990,-5
wide_resnet50_2.tv2_in1k,70.840,29.160,89.270,10.730,68.88,224,0.965,bilinear,-10.766,-6.492,+5
poolformer_m36.sail_in1k,70.830,29.170,89.510,10.490,56.17,224,0.950,bicubic,-11.272,-6.368,-36
tf_efficientnetv2_b3.in1k,70.830,29.170,89.510,10.490,14.36,300,0.904,bicubic,-11.140,-6.302,-24
tresnet_l.miil_in1k,70.820,29.180,89.600,10.400,55.99,224,0.875,bilinear,-10.660,-6.024,+16
levit_384.fb_dist_in1k,70.810,29.190,89.320,10.680,39.13,224,0.900,bicubic,-11.786,-6.698,-104
coat_lite_small.in1k,70.790,29.210,89.590,10.410,19.84,224,0.900,bicubic,-11.524,-6.258,-66
levit_conv_384.fb_dist_in1k,70.790,29.210,89.310,10.690,39.13,224,0.900,bicubic,-11.800,-6.938,-104
ecaresnetlight.miil_in1k,70.750,29.250,89.880,10.120,30.16,288,0.950,bicubic,-10.658,-5.936,+19
deit3_small_patch16_224.fb_in1k,70.750,29.250,89.440,10.560,22.06,224,0.900,bicubic,-10.620,-6.016,+25
regnetx_080.tv2_in1k,70.730,29.270,89.330,10.670,39.57,224,0.965,bicubic,-10.808,-6.212,+1
swinv2_cr_tiny_ns_224.sw_in1k,70.700,29.300,89.380,10.620,28.33,224,0.900,bicubic,-11.102,-6.438,-19
seresnet50.ra2_in1k,70.690,29.310,89.870,10.130,28.09,288,0.950,bicubic,-10.594,-5.782,+28
vit_base_patch32_clip_224.openai_ft_in1k,70.690,29.310,89.830,10.170,88.22,224,0.900,bicubic,-11.240,-6.136,-29
vit_relpos_small_patch16_224.sw_in1k,70.680,29.320,90.000,10.000,21.98,224,0.900,bicubic,-10.782,-5.820,+7
resnet50d.ra2_in1k,70.680,29.320,89.310,10.690,25.58,288,0.950,bicubic,-10.676,-6.428,+21
mobilevitv2_175.cvnets_in22k_ft_in1k,70.670,29.330,89.700,10.300,14.25,256,0.888,bicubic,-11.268,-6.090,-33
resnet50_gn.a1h_in1k,70.630,29.370,89.370,10.630,25.56,288,0.950,bicubic,-10.586,-6.258,+29
tf_efficientnet_b3.aa_in1k,70.620,29.380,89.440,10.560,12.23,300,0.904,bicubic,-11.024,-6.282,-17
resnet101.tv2_in1k,70.620,29.380,89.400,10.600,44.55,224,0.965,bilinear,-11.270,-6.368,-32
crossvit_small_240.in1k,70.610,29.390,89.360,10.640,26.86,240,0.875,bicubic,-10.406,-6.096,+47
cait_xxs24_384.fb_dist_in1k,70.600,29.400,89.730,10.270,12.03,384,1.000,bicubic,-10.372,-5.910,+49
senet154.gluon_in1k,70.600,29.400,88.920,11.080,115.09,224,0.875,bicubic,-10.626,-6.438,+21
convit_small.fb_in1k,70.580,29.420,89.590,10.410,27.78,224,0.875,bicubic,-10.840,-6.154,+4
convnext_nano_ols.d1h_in1k,70.570,29.430,89.100,10.900,15.65,288,1.000,bicubic,-11.030,-6.536,-17
twins_pcpvt_small.in1k,70.560,29.440,89.070,10.930,24.11,224,0.900,bicubic,-10.536,-6.578,+36
regnetz_b16.ra3_in1k,70.550,29.450,89.400,10.600,9.72,288,1.000,bicubic,-10.178,-6.120,+70
resnet50.c2_in1k,70.540,29.460,89.170,10.830,25.56,288,1.000,bicubic,-10.330,-6.364,+57
vit_small_r26_s32_224.augreg_in21k_ft_in1k,70.530,29.470,90.110,9.890,36.43,224,0.900,bicubic,-11.334,-5.912,-42
resnetv2_50.a1h_in1k,70.530,29.470,89.120,10.880,25.55,288,1.000,bicubic,-10.868,-6.606,+1
swinv2_tiny_window8_256.ms_in1k,70.510,29.490,89.500,10.500,28.35,256,0.900,bicubic,-11.310,-6.494,-41
deit_small_distilled_patch16_224.fb_in1k,70.510,29.490,89.470,10.530,22.44,224,0.900,bicubic,-10.708,-5.914,+15
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,70.500,29.500,89.750,10.250,44.18,224,0.875,bilinear,-10.424,-5.984,+44
legacy_senet154.in1k,70.500,29.500,88.980,11.020,115.09,224,0.875,bilinear,-10.808,-6.512,+6
halonet50ts.a1h_in1k,70.480,29.520,89.340,10.660,22.73,256,0.940,bicubic,-11.180,-6.270,-35
resnetaa50.a1h_in1k,70.440,29.560,89.990,10.010,25.56,288,1.000,bicubic,-11.174,-5.812,-31
tf_efficientnet_lite4.in1k,70.440,29.560,89.120,10.880,13.01,380,0.920,bilinear,-11.092,-6.544,-22
poolformerv2_s36.sail_in1k,70.430,29.570,89.630,10.370,30.79,224,1.000,bicubic,-11.138,-6.060,-31
crossvit_15_240.in1k,70.430,29.570,89.540,10.460,27.53,240,0.875,bicubic,-11.104,-6.152,-24
twins_svt_small.in1k,70.430,29.570,89.360,10.640,24.06,224,0.900,bicubic,-11.248,-6.300,-43
ecaresnet50t.a2_in1k,70.430,29.570,89.020,10.980,25.57,288,1.000,bicubic,-11.228,-6.532,-38
resnest50d.in1k,70.430,29.570,88.760,11.240,27.48,224,0.875,bilinear,-10.534,-6.624,+33
resnest50d_1s4x24d.in1k,70.420,29.580,89.220,10.780,25.68,224,0.875,bicubic,-10.568,-6.106,+27
gcresnext50ts.ch_in1k,70.410,29.590,89.420,10.580,15.67,288,1.000,bicubic,-10.822,-6.124,-1
seresnext101_64x4d.gluon_in1k,70.410,29.590,89.360,10.640,88.23,224,0.875,bicubic,-10.486,-5.936,+35
gernet_l.idstcv_in1k,70.410,29.590,88.970,11.030,31.08,256,0.875,bilinear,-10.944,-6.560,-8
cs3darknet_l.c2ns_in1k,70.350,29.650,89.750,10.250,21.16,288,0.950,bicubic,-10.546,-5.912,+33
resnet152s.gluon_in1k,70.320,29.680,88.870,11.130,60.32,224,0.875,bicubic,-10.682,-6.540,+21
vit_srelpos_small_patch16_224.sw_in1k,70.290,29.710,89.540,10.460,21.97,224,0.900,bicubic,-10.802,-5.792,+14
repvgg_b3.rvgg_in1k,70.230,29.770,88.730,11.270,123.09,224,0.875,bilinear,-10.276,-6.524,+64
coat_mini.in1k,70.200,29.800,89.460,10.540,10.34,224,0.900,bicubic,-11.070,-5.922,-9
xception41p.ra3_in1k,70.200,29.800,89.100,10.900,26.91,299,0.940,bicubic,-11.772,-6.696,-74
efficientnet_el.ra_in1k,70.150,29.850,89.300,10.700,10.59,300,0.904,bicubic,-11.162,-6.236,-15
sebotnet33ts_256.a1h_in1k,70.150,29.850,88.820,11.180,13.70,256,0.940,bicubic,-11.016,-6.348,-2
inception_resnet_v2.tf_in1k,70.130,29.870,88.690,11.310,55.84,299,0.897,bicubic,-10.332,-6.618,+66
resnet50.d_in1k,70.120,29.880,88.740,11.260,25.56,288,1.000,bicubic,-10.852,-6.690,+16
resnet50d.a1_in1k,70.120,29.880,88.350,11.650,25.58,288,1.000,bicubic,-11.330,-6.868,-32
haloregnetz_b.ra3_in1k,70.110,29.890,88.900,11.100,11.68,224,0.940,bicubic,-10.938,-6.300,+8
poolformer_s36.sail_in1k,70.100,29.900,89.140,10.860,30.86,224,0.900,bicubic,-11.330,-6.304,-33
resmlp_36_224.fb_distilled_in1k,70.100,29.900,89.090,10.910,44.69,224,0.875,bicubic,-11.048,-6.388,-5
ecaresnet50d_pruned.miil_in1k,70.090,29.910,89.510,10.490,19.94,288,0.950,bicubic,-10.700,-6.060,+30
resnet50.c1_in1k,70.070,29.930,89.000,11.000,25.56,288,1.000,bicubic,-10.842,-6.552,+17
gcresnet50t.ra2_in1k,70.050,29.950,89.520,10.480,25.90,288,1.000,bicubic,-11.406,-6.198,-39
sehalonet33ts.ra2_in1k,70.050,29.950,88.730,11.270,13.69,256,0.940,bicubic,-10.906,-6.546,+11
seresnext101_32x4d.gluon_in1k,70.020,29.980,88.940,11.060,48.96,224,0.875,bicubic,-10.872,-6.356,+16
regnety_320.pycls_in1k,70.020,29.980,88.900,11.100,145.05,224,0.875,bicubic,-10.788,-6.338,+24
seresnet50.a2_in1k,70.000,30.000,88.710,11.290,28.09,288,1.000,bicubic,-11.106,-6.512,-8
levit_256.fb_dist_in1k,69.970,30.030,89.240,10.760,18.89,224,0.900,bicubic,-11.544,-6.252,-51
levit_conv_256.fb_dist_in1k,69.970,30.030,89.240,10.760,18.89,224,0.900,bicubic,-11.550,-6.250,-53
resnet152d.gluon_in1k,69.940,30.060,88.490,11.510,60.21,224,0.875,bicubic,-10.536,-6.712,+49
maxvit_rmlp_pico_rw_256.sw_in1k,69.890,30.110,89.250,10.750,7.52,256,0.950,bicubic,-10.624,-5.964,+42
pit_s_224.in1k,69.890,30.110,88.940,11.060,23.46,224,0.900,bicubic,-11.202,-6.630,-8
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,69.850,30.150,90.410,9.590,28.29,224,0.900,bicubic,-11.118,-5.604,0
resnext50_32x4d.ra_in1k,69.830,30.170,88.220,11.780,25.03,288,0.950,bicubic,-10.868,-7.170,+26
regnety_016.tv2_in1k,69.810,30.190,89.360,10.640,11.20,224,0.965,bicubic,-10.858,-5.970,+28
seresnet50.a1_in1k,69.810,30.190,88.550,11.450,28.09,288,1.000,bicubic,-11.292,-6.780,-15
mobilevitv2_150.cvnets_in22k_ft_in1k,69.800,30.200,89.180,10.820,10.59,256,0.888,bicubic,-11.688,-6.488,-58
resnet50.a1_in1k,69.770,30.230,88.350,11.650,25.56,288,1.000,bicubic,-11.444,-6.752,-30
mobilevitv2_200.cvnets_in1k,69.750,30.250,88.620,11.380,18.45,256,0.888,bicubic,-11.384,-6.742,-23
resnet50.tv2_in1k,69.750,30.250,88.590,11.410,25.56,224,0.965,bilinear,-11.100,-6.844,+8
resnext50_32x4d.a2_in1k,69.750,30.250,88.200,11.800,25.03,288,1.000,bicubic,-11.554,-6.896,-41
ese_vovnet39b.ra_in1k,69.730,30.270,89.550,10.450,24.57,288,0.950,bicubic,-10.620,-5.816,+47
lambda_resnet50ts.a1h_in1k,69.730,30.270,88.830,11.170,21.54,256,0.950,bicubic,-11.426,-6.268,-29
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,69.710,30.290,89.420,10.580,25.03,224,0.875,bilinear,-10.628,-5.978,+48
xcit_tiny_24_p16_224.fb_dist_in1k,69.710,30.290,88.720,11.280,12.12,224,1.000,bicubic,-10.744,-6.498,+39
xcit_tiny_12_p16_384.fb_dist_in1k,69.690,30.310,89.020,10.980,6.72,384,1.000,bicubic,-11.248,-6.394,-10
tresnet_m.miil_in1k,69.670,30.330,88.000,12.000,31.39,224,0.875,bilinear,-11.128,-6.856,+3
resmlp_24_224.fb_distilled_in1k,69.660,30.340,89.070,10.930,30.02,224,0.875,bicubic,-11.096,-6.154,+6
resnext101_64x4d.gluon_in1k,69.660,30.340,88.260,11.740,83.46,224,0.875,bicubic,-10.940,-6.732,+20
resnext50d_32x4d.bt_in1k,69.640,30.360,89.250,10.750,25.05,288,0.950,bicubic,-11.024,-6.170,+14
regnetx_032.tv2_in1k,69.620,30.380,89.360,10.640,15.30,224,0.965,bicubic,-11.306,-5.918,-14
efficientnet_b3_pruned.in1k,69.590,30.410,88.980,11.020,9.86,300,0.904,bicubic,-11.262,-6.264,-5
convnextv2_pico.fcmae_ft_in1k,69.580,30.420,89.230,10.770,9.07,288,0.950,bicubic,-11.506,-6.250,-28
eva02_tiny_patch14_336.mim_in22k_ft_in1k,69.560,30.440,89.320,10.680,5.76,336,1.000,bicubic,-11.070,-6.206,+12
inception_resnet_v2.tf_ens_adv_in1k,69.560,30.440,88.490,11.510,55.84,299,0.897,bicubic,-10.418,-6.456,+59
nf_resnet50.ra2_in1k,69.550,30.450,88.730,11.270,25.56,288,0.940,bicubic,-11.090,-6.602,+9
gernet_m.idstcv_in1k,69.540,30.460,88.690,11.310,21.14,224,0.875,bilinear,-11.194,-6.496,-1
repvgg_b3g4.rvgg_in1k,69.530,30.470,88.460,11.540,83.83,224,0.875,bilinear,-10.686,-6.632,+47
gcresnet33ts.ra2_in1k,69.510,30.490,89.110,10.890,19.88,288,1.000,bicubic,-11.090,-6.210,+8
efficientnet_el_pruned.in1k,69.510,30.490,88.930,11.070,10.59,300,0.904,bicubic,-10.790,-6.292,+37
efficientnet_b2.ra_in1k,69.490,30.510,88.690,11.310,9.11,288,1.000,bicubic,-11.120,-6.624,+6
resnext50_32x4d.a1_in1k,69.490,30.510,88.340,11.660,25.03,288,1.000,bicubic,-11.976,-6.834,-80
swin_tiny_patch4_window7_224.ms_in1k,69.470,30.530,89.060,10.940,28.29,224,0.900,bicubic,-11.906,-6.484,-71
regnetx_320.pycls_in1k,69.470,30.530,88.270,11.730,107.81,224,0.875,bicubic,-10.776,-6.752,+38
vit_base_patch32_224.augreg_in21k_ft_in1k,69.450,30.550,89.440,10.560,88.22,224,0.900,bicubic,-11.266,-6.126,-8
res2net101d.in1k,69.450,30.550,88.710,11.290,45.23,224,0.875,bilinear,-11.774,-6.642,-60
gcvit_xxtiny.in1k,69.440,30.560,88.850,11.150,12.00,224,0.875,bicubic,-10.288,-6.230,+65
cspresnext50.ra_in1k,69.440,30.560,88.590,11.410,20.57,256,0.887,bilinear,-11.116,-6.732,+3
rexnet_150.nav_in1k,69.410,30.590,88.980,11.020,9.73,224,0.875,bicubic,-10.914,-6.194,+24
resnext50_32x4d.tv2_in1k,69.400,30.600,88.440,11.560,25.03,224,0.965,bilinear,-11.782,-6.900,-59
seresnet33ts.ra2_in1k,69.380,30.620,89.190,10.810,19.78,288,1.000,bicubic,-11.404,-6.172,-21
convmixer_768_32.in1k,69.380,30.620,88.880,11.120,21.11,224,0.960,bicubic,-10.788,-6.194,+36
inception_v4.tf_in1k,69.380,30.620,88.780,11.220,42.68,299,0.875,bicubic,-10.776,-6.188,+38
eca_resnet33ts.ra2_in1k,69.370,30.630,89.210,10.790,19.68,288,1.000,bicubic,-11.302,-6.154,-12
xception71.tf_in1k,69.360,30.640,88.260,11.740,42.34,299,0.903,bicubic,-10.512,-6.668,+45
darknet53.c2ns_in1k,69.350,30.650,88.780,11.220,41.61,288,1.000,bicubic,-11.186,-6.654,-4
cs3darknet_focus_l.c2ns_in1k,69.340,30.660,89.420,10.580,21.15,288,0.950,bicubic,-11.540,-6.262,-34
legacy_seresnext101_32x4d.in1k,69.340,30.660,88.050,11.950,48.96,224,0.875,bilinear,-10.890,-6.976,+26
vit_small_patch16_384.augreg_in1k,69.300,30.700,89.000,11.000,22.20,384,1.000,bicubic,-11.816,-6.574,-61
mobilevitv2_175.cvnets_in1k,69.290,30.710,88.960,11.040,14.25,256,0.888,bicubic,-11.570,-6.296,-34
efficientformer_l1.snap_dist_in1k,69.280,30.720,88.560,11.440,12.29,224,0.950,bicubic,-11.218,-6.428,-5
vit_small_patch32_384.augreg_in21k_ft_in1k,69.270,30.730,89.820,10.180,22.92,384,1.000,bicubic,-11.216,-5.776,-5
convnext_pico_ols.d1_in1k,69.230,30.770,88.830,11.170,9.06,288,1.000,bicubic,-11.234,-6.416,-3
edgenext_small_rw.sw_in1k,69.210,30.790,88.740,11.260,7.83,320,1.000,bicubic,-11.248,-6.450,-2
vit_base_patch16_384.augreg_in1k,69.190,30.810,88.380,11.620,86.86,384,1.000,bicubic,-11.912,-6.740,-65
resnet50.b2k_in1k,69.180,30.820,88.660,11.340,25.56,288,1.000,bicubic,-11.274,-6.658,-3
resnet50.a1h_in1k,69.130,30.870,88.510,11.490,25.56,224,1.000,bicubic,-11.546,-6.796,-26
resnet152c.gluon_in1k,69.130,30.870,87.890,12.110,60.21,224,0.875,bicubic,-10.780,-6.954,+29
resnet50.b1k_in1k,69.100,30.900,88.740,11.260,25.56,288,1.000,bicubic,-11.606,-6.692,-30
tf_efficientnetv2_b2.in1k,69.100,30.900,88.220,11.780,10.10,260,0.890,bicubic,-11.094,-6.822,+16
mixnet_xl.ra_in1k,69.090,30.910,88.310,11.690,11.90,224,0.875,bicubic,-11.392,-6.626,-13
resnetblur50.bt_in1k,69.070,30.930,88.450,11.550,25.56,288,0.950,bicubic,-11.164,-6.784,+11
repvgg_b2g4.rvgg_in1k,69.010,30.990,88.360,11.640,61.76,224,0.875,bilinear,-10.372,-6.316,+65
regnety_160.pycls_in1k,69.010,30.990,88.270,11.730,83.59,224,0.875,bicubic,-11.288,-6.692,+4
resnet101d.gluon_in1k,69.010,30.990,88.080,11.920,44.57,224,0.875,bicubic,-11.416,-6.944,-10
xception65.tf_in1k,68.950,31.050,88.470,11.530,39.92,299,0.903,bicubic,-10.606,-6.184,+50
resnext101_32x4d.gluon_in1k,68.950,31.050,88.340,11.660,44.18,224,0.875,bicubic,-11.390,-6.590,-5
poolformerv2_s24.sail_in1k,68.940,31.060,88.670,11.330,21.34,224,1.000,bicubic,-11.808,-6.640,-43
tf_efficientnet_b2.ap_in1k,68.930,31.070,88.330,11.670,9.11,260,0.890,bicubic,-11.378,-6.698,-5
cspdarknet53.ra_in1k,68.920,31.080,88.600,11.400,27.64,256,0.887,bilinear,-11.142,-6.480,+12
convnext_pico.d1_in1k,68.900,31.100,88.470,11.530,9.05,288,0.950,bicubic,-11.514,-6.580,-15
mobilevitv2_150.cvnets_in1k,68.890,31.110,88.080,11.920,10.59,256,0.888,bicubic,-11.480,-6.994,-14
resnet50d.a2_in1k,68.890,31.110,87.990,12.010,25.58,288,1.000,bicubic,-12.274,-7.094,-90
regnety_120.pycls_in1k,68.860,31.140,88.330,11.670,51.82,224,0.875,bicubic,-11.526,-6.798,-17
resnet152.a3_in1k,68.820,31.180,87.760,12.240,60.19,224,0.950,bicubic,-11.726,-7.240,-34
resnet152.gluon_in1k,68.820,31.180,87.710,12.290,60.19,224,0.875,bicubic,-10.874,-7.026,+30
poolformer_s24.sail_in1k,68.780,31.220,88.170,11.830,21.39,224,0.900,bicubic,-11.516,-6.902,-11
dpn107.mx_in1k,68.780,31.220,88.130,11.870,86.92,224,0.875,bicubic,-11.388,-6.812,+1
gmlp_s16_224.ra3_in1k,68.780,31.220,88.070,11.930,19.42,224,0.875,bicubic,-10.864,-6.552,+34
dpn131.mx_in1k,68.770,31.230,87.570,12.430,79.25,224,0.875,bicubic,-11.044,-7.130,+18
darknetaa53.c2ns_in1k,68.760,31.240,88.720,11.280,36.02,288,1.000,bilinear,-11.742,-6.602,-36
tf_efficientnet_b2.aa_in1k,68.760,31.240,87.950,12.050,9.11,260,0.890,bicubic,-11.324,-6.956,-1
resnet101s.gluon_in1k,68.720,31.280,87.900,12.100,44.67,224,0.875,bicubic,-11.582,-7.250,-18
deit_small_patch16_224.fb_in1k,68.710,31.290,88.200,11.800,22.05,224,0.900,bicubic,-11.134,-6.844,+10
regnety_080.pycls_in1k,68.710,31.290,87.970,12.030,39.18,224,0.875,bicubic,-11.158,-6.862,+6
seresnext50_32x4d.gluon_in1k,68.660,31.340,88.360,11.640,27.56,224,0.875,bicubic,-11.250,-6.464,+2
resnet50.ram_in1k,68.630,31.370,88.320,11.680,25.56,288,0.950,bicubic,-11.346,-6.732,-3
hrnet_w64.ms_in1k,68.630,31.370,88.070,11.930,128.06,224,0.875,bilinear,-10.844,-6.576,+36
xcit_tiny_12_p8_224.fb_in1k,68.580,31.420,88.720,11.280,6.71,224,1.000,bicubic,-11.108,-5.990,+18
resnet50.a2_in1k,68.570,31.430,87.990,12.010,25.56,288,1.000,bicubic,-12.202,-6.998,-67
tf_efficientnet_b3.in1k,68.530,31.470,88.700,11.300,12.23,300,0.904,bicubic,-12.348,-6.600,-77
dpn98.mx_in1k,68.520,31.480,87.610,12.390,61.57,224,0.875,bicubic,-11.150,-7.044,+19
regnetx_160.pycls_in1k,68.510,31.490,88.460,11.540,54.28,224,0.875,bicubic,-11.356,-6.368,-1
rexnet_130.nav_in1k,68.460,31.540,88.040,11.960,7.56,224,0.875,bicubic,-11.044,-6.638,+25
cspresnet50.ra_in1k,68.460,31.540,87.950,12.050,21.62,256,0.887,bilinear,-11.130,-6.762,+21
regnety_064.pycls_in1k,68.450,31.550,88.060,11.940,30.58,224,0.875,bicubic,-11.264,-6.706,+9
tf_efficientnet_el.in1k,68.440,31.560,88.210,11.790,10.59,300,0.904,bicubic,-11.812,-6.908,-27
xcit_tiny_24_p16_224.fb_in1k,68.420,31.580,88.290,11.710,12.12,224,1.000,bicubic,-11.028,-6.588,+27
cait_xxs36_224.fb_dist_in1k,68.410,31.590,88.650,11.350,17.30,224,1.000,bicubic,-11.336,-6.224,+3
skresnext50_32x4d.ra_in1k,68.400,31.600,87.560,12.440,27.48,224,0.875,bicubic,-11.762,-7.080,-21
resnet50.fb_ssl_yfcc100m_ft_in1k,68.380,31.620,88.550,11.450,25.56,224,0.875,bilinear,-10.854,-6.276,+43
fbnetv3_d.ra2_in1k,68.350,31.650,88.430,11.570,10.31,256,0.950,bilinear,-11.332,-6.514,+6
dla102x2.in1k,68.350,31.650,87.890,12.110,41.28,224,0.875,bilinear,-11.094,-6.748,+24
res2net50d.in1k,68.300,31.700,88.110,11.890,25.72,224,0.875,bilinear,-11.954,-6.926,-36
efficientnet_b2_pruned.in1k,68.300,31.700,88.100,11.900,8.31,260,0.890,bicubic,-11.620,-6.752,-18
resmlp_big_24_224.fb_in1k,68.300,31.700,87.540,12.460,129.14,224,0.875,bicubic,-12.736,-7.478,-109
vit_base_patch16_224.sam_in1k,68.290,31.710,87.730,12.270,86.57,224,0.900,bicubic,-11.954,-7.026,-36
resnext50_32x4d.gluon_in1k,68.290,31.710,87.340,12.660,25.03,224,0.875,bicubic,-11.070,-7.090,+25
ecaresnet26t.ra2_in1k,68.260,31.740,88.810,11.190,16.01,320,0.950,bicubic,-11.590,-6.280,-16
efficientformerv2_s1.snap_dist_in1k,68.230,31.770,88.320,11.680,6.19,224,0.950,bicubic,-11.458,-6.734,-2
tf_efficientnet_lite3.in1k,68.230,31.770,87.720,12.280,8.20,300,0.904,bilinear,-11.578,-7.192,-11
resnet101.a3_in1k,68.220,31.780,87.630,12.370,44.55,224,0.950,bicubic,-11.594,-6.976,-13
pit_xs_distilled_224.in1k,68.190,31.810,87.730,12.270,11.00,224,0.900,bicubic,-10.990,-6.632,+35
resnet50.ra_in1k,68.150,31.850,87.900,12.100,25.56,288,0.950,bicubic,-11.686,-7.066,-17
fbnetv3_b.ra2_in1k,68.140,31.860,87.920,12.080,8.60,256,0.950,bilinear,-11.002,-6.822,+35
regnetx_120.pycls_in1k,68.130,31.870,87.610,12.390,46.11,224,0.875,bicubic,-11.458,-7.132,0
resmlp_36_224.fb_in1k,68.090,31.910,88.180,11.820,44.69,224,0.875,bicubic,-11.682,-6.704,-16
resnet50.bt_in1k,68.080,31.920,87.810,12.190,25.56,288,0.950,bicubic,-11.560,-7.082,-5
dpn68b.ra_in1k,68.070,31.930,87.380,12.620,12.61,288,1.000,bicubic,-11.292,-7.058,+12
regnetx_016.tv2_in1k,68.050,31.950,88.230,11.770,9.19,224,0.965,bicubic,-11.386,-6.538,+7
resnetrs50.tf_in1k,68.020,31.980,87.730,12.270,35.69,224,0.910,bicubic,-11.876,-7.242,-32
dpn92.mx_in1k,67.980,32.020,87.600,12.400,37.67,224,0.875,bicubic,-12.058,-7.260,-39
nf_regnet_b1.ra2_in1k,67.970,32.030,88.220,11.780,10.22,288,0.900,bicubic,-11.336,-6.520,+13
resnet50d.gluon_in1k,67.920,32.080,87.120,12.880,25.58,224,0.875,bicubic,-11.158,-7.348,+30
resnetv2_50x1_bit.goog_in21k_ft_in1k,67.900,32.100,89.270,10.730,25.55,448,1.000,bilinear,-12.440,-6.410,-67
levit_192.fb_dist_in1k,67.900,32.100,87.900,12.100,10.95,224,0.900,bicubic,-11.938,-6.884,-29
levit_conv_192.fb_dist_in1k,67.900,32.100,87.890,12.110,10.95,224,0.900,bicubic,-11.938,-6.888,-31
regnetx_080.pycls_in1k,67.890,32.110,87.000,13.000,39.57,224,0.875,bicubic,-11.312,-7.552,+18
tf_efficientnetv2_b1.in1k,67.870,32.130,87.830,12.170,8.14,240,0.882,bicubic,-11.592,-6.892,-5
efficientnet_em.ra2_in1k,67.860,32.140,88.140,11.860,6.90,240,0.882,bicubic,-11.380,-6.654,+13
legacy_seresnext50_32x4d.in1k,67.860,32.140,87.620,12.380,27.56,224,0.875,bilinear,-11.216,-6.812,+24
resnext101_32x8d.tv_in1k,67.840,32.160,87.490,12.510,88.79,224,0.875,bilinear,-11.470,-7.030,+3
resmlp_24_224.fb_in1k,67.820,32.180,87.610,12.390,30.02,224,0.875,bicubic,-11.554,-6.936,-3
lambda_resnet26t.c1_in1k,67.800,32.200,87.790,12.210,10.96,256,0.940,bicubic,-11.288,-6.800,+19
ecaresnet50t.a3_in1k,67.790,32.210,87.560,12.440,25.57,224,0.950,bicubic,-11.762,-7.134,-17
hrnet_w44.ms_in1k,67.770,32.230,87.530,12.470,67.06,224,0.875,bilinear,-11.122,-6.834,+29
hrnet_w48.ms_in1k,67.760,32.240,87.420,12.580,77.47,224,0.875,bilinear,-11.542,-7.096,0
resnet33ts.ra2_in1k,67.730,32.270,88.100,11.900,19.68,288,1.000,bicubic,-11.996,-6.728,-34
coat_lite_mini.in1k,67.710,32.290,87.710,12.290,11.01,224,0.900,bicubic,-11.392,-6.898,+13
tf_efficientnet_b0.ns_jft_in1k,67.700,32.300,88.070,11.930,5.29,224,0.875,bicubic,-10.970,-6.304,+42
resnext50_32x4d.a3_in1k,67.700,32.300,86.930,13.070,25.03,224,0.950,bicubic,-11.568,-7.376,+1
legacy_xception.tf_in1k,67.690,32.310,87.560,12.440,22.86,299,0.897,bicubic,-11.350,-6.822,+15
eca_botnext26ts_256.c1_in1k,67.680,32.320,87.080,12.920,10.59,256,0.950,bicubic,-11.588,-7.526,-3
regnetx_064.pycls_in1k,67.670,32.330,87.530,12.470,26.21,224,0.875,bicubic,-11.396,-6.928,+12
convnext_femto_ols.d1_in1k,67.650,32.350,87.390,12.610,5.23,288,0.950,bicubic,-11.274,-7.136,+18
resnet32ts.ra2_in1k,67.640,32.360,87.580,12.420,17.96,288,1.000,bicubic,-11.752,-7.072,-18
halonet26t.a1h_in1k,67.620,32.380,87.250,12.750,12.48,256,0.950,bicubic,-11.486,-7.056,+4
inception_v3.gluon_in1k,67.590,32.410,87.450,12.550,23.83,299,0.875,bicubic,-11.216,-6.924,+23
hrnet_w40.ms_in1k,67.580,32.420,87.140,12.860,57.56,224,0.875,bilinear,-11.350,-7.324,+13
regnety_040.pycls_in1k,67.570,32.430,87.490,12.510,20.65,224,0.875,bicubic,-11.662,-7.166,-5
resnet101c.gluon_in1k,67.560,32.440,87.160,12.840,44.57,224,0.875,bicubic,-11.982,-7.426,-32
dla169.in1k,67.550,32.450,87.570,12.430,53.39,224,0.875,bilinear,-11.158,-6.776,+28
legacy_seresnet152.in1k,67.550,32.450,87.390,12.610,66.82,224,0.875,bilinear,-11.110,-6.980,+31
tf_efficientnet_b1.ap_in1k,67.520,32.480,87.750,12.250,7.79,240,0.882,bicubic,-11.760,-6.558,-16
efficientnet_b1.ft_in1k,67.520,32.480,87.470,12.530,7.79,256,1.000,bicubic,-11.280,-6.872,+18
mobilevitv2_125.cvnets_in1k,67.480,32.520,87.570,12.430,7.48,256,0.888,bicubic,-12.200,-7.288,-47
tf_efficientnet_cc_b1_8e.in1k,67.480,32.520,87.290,12.710,39.72,240,0.882,bicubic,-11.822,-7.084,-20
eca_halonext26ts.c1_in1k,67.480,32.520,87.240,12.760,10.76,256,0.940,bicubic,-12.006,-7.360,-35
res2net101_26w_4s.in1k,67.460,32.540,87.000,13.000,45.21,224,0.875,bilinear,-11.742,-7.436,-12
resnet101.gluon_in1k,67.450,32.550,87.230,12.770,44.55,224,0.875,bicubic,-11.860,-7.292,-26
res2net50_26w_8s.in1k,67.440,32.560,87.250,12.750,48.40,224,0.875,bilinear,-11.502,-7.044,0
regnety_008_tv.tv2_in1k,67.410,32.590,88.040,11.960,6.43,224,0.965,bicubic,-11.264,-6.346,+20
regnety_032.pycls_in1k,67.410,32.590,87.280,12.720,19.44,224,0.875,bicubic,-11.466,-7.128,+3
resnet34d.ra2_in1k,67.400,32.600,87.960,12.040,21.82,288,0.950,bicubic,-11.036,-6.384,+33
tf_efficientnet_b2.in1k,67.400,32.600,87.580,12.420,9.11,260,0.890,bicubic,-12.204,-7.134,-51
convnextv2_femto.fcmae_ft_in1k,67.340,32.660,87.580,12.420,5.23,288,0.950,bicubic,-11.998,-6.980,-34
cait_xxs24_224.fb_dist_in1k,67.340,32.660,87.520,12.480,11.96,224,1.000,bicubic,-11.044,-6.796,+38
xception41.tf_in1k,67.260,32.740,87.200,12.800,26.97,299,0.903,bicubic,-11.244,-7.076,+20
coat_tiny.in1k,67.240,32.760,87.310,12.690,5.50,224,0.900,bicubic,-11.186,-6.738,+30
regnetx_032.pycls_in1k,67.240,32.760,86.990,13.010,15.30,224,0.875,bicubic,-10.928,-7.092,+47
resnest26d.gluon_in1k,67.220,32.780,87.180,12.820,17.07,224,0.875,bilinear,-11.264,-7.114,+22
convnext_femto.d1_in1k,67.190,32.810,87.480,12.520,5.22,288,0.950,bicubic,-11.526,-6.950,+7
repvgg_b2.rvgg_in1k,67.160,32.840,87.320,12.680,89.02,224,0.875,bilinear,-11.632,-7.100,0
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,67.160,32.840,86.480,13.520,119.42,256,0.900,bicubic,-12.324,-7.658,-52
botnet26t_256.c1_in1k,67.140,32.860,87.510,12.490,12.49,256,0.950,bicubic,-12.116,-7.024,-33
legacy_seresnet101.in1k,67.110,32.890,87.050,12.950,49.33,224,0.875,bilinear,-11.276,-7.212,+27
resnet50s.gluon_in1k,67.080,32.920,86.860,13.140,25.68,224,0.875,bicubic,-11.638,-7.382,+1
dla60x.in1k,67.070,32.930,87.200,12.800,17.35,224,0.875,bilinear,-11.170,-6.834,+36
visformer_tiny.in1k,67.050,32.950,87.060,12.940,10.32,224,0.900,bicubic,-11.112,-7.104,+39
dla60_res2net.in1k,67.040,32.960,87.150,12.850,20.85,224,0.875,bilinear,-11.422,-7.052,+15
resnet34.a1_in1k,67.030,32.970,86.290,13.710,21.80,288,1.000,bicubic,-10.888,-6.624,+53
xcit_tiny_12_p16_224.fb_dist_in1k,67.020,32.980,87.390,12.610,6.72,224,1.000,bicubic,-11.554,-6.808,+3
resnet152.tv_in1k,66.990,33.010,87.550,12.450,60.19,224,0.875,bilinear,-11.332,-6.498,+25
dla102x.in1k,66.980,33.020,86.780,13.220,26.31,224,0.875,bilinear,-11.518,-7.456,+5
lambda_resnet26rpt_256.c1_in1k,66.960,33.040,87.130,12.870,10.99,256,0.940,bicubic,-12.008,-7.308,-26
mixnet_l.ft_in1k,66.960,33.040,86.930,13.070,7.33,224,0.875,bicubic,-12.006,-7.250,-26
repvgg_b1.rvgg_in1k,66.930,33.070,86.780,13.220,57.42,224,0.875,bilinear,-11.438,-7.316,+18
pit_xs_224.in1k,66.920,33.080,87.280,12.720,10.62,224,0.900,bicubic,-11.260,-6.884,+28
resnet50d.a3_in1k,66.920,33.080,86.540,13.460,25.58,224,0.950,bicubic,-11.802,-7.696,-12
pvt_v2_b1.in1k,66.910,33.090,87.420,12.580,14.01,224,0.900,bicubic,-11.792,-7.082,-9
res2net50_26w_6s.in1k,66.910,33.090,86.880,13.120,37.05,224,0.875,bilinear,-11.658,-7.242,-5
tf_efficientnet_b1.aa_in1k,66.900,33.100,87.030,12.970,7.79,240,0.882,bicubic,-11.930,-7.168,-23
xcit_nano_12_p8_384.fb_dist_in1k,66.870,33.130,87.110,12.890,3.05,384,1.000,bicubic,-10.950,-6.930,+47
efficientnet_es.ra_in1k,66.860,33.140,86.710,13.290,5.44,224,0.875,bicubic,-11.194,-7.214,+30
mobilevit_s.cvnets_in1k,66.850,33.150,87.070,12.930,5.58,256,0.900,bicubic,-11.462,-7.074,+14
regnetx_040.pycls_in1k,66.850,33.150,86.740,13.260,22.12,224,0.875,bicubic,-11.642,-7.502,-5
hrnet_w32.ms_in1k,66.780,33.220,87.290,12.710,41.23,224,0.875,bilinear,-11.664,-6.898,-1
resnet50.am_in1k,66.780,33.220,86.740,13.260,25.56,224,0.875,bicubic,-12.218,-7.656,-40
tf_mixnet_l.in1k,66.780,33.220,86.480,13.520,7.33,224,0.875,bicubic,-11.996,-7.522,-25
seresnext26t_32x4d.bt_in1k,66.770,33.230,86.720,13.280,16.81,288,0.950,bicubic,-11.974,-7.592,-24
hrnet_w30.ms_in1k,66.760,33.240,86.790,13.210,37.71,224,0.875,bilinear,-11.432,-7.432,+14
SelecSls60b.in1k,66.760,33.240,86.540,13.460,32.77,224,0.875,bicubic,-11.654,-7.494,-1
hrnet_w18.ms_aug_in1k,66.750,33.250,87.480,12.520,21.30,224,0.950,bilinear,-11.376,-6.572,+17
tf_efficientnetv2_b0.in1k,66.700,33.300,86.690,13.310,7.14,224,0.875,bicubic,-11.658,-7.324,+2
vit_small_patch16_224.augreg_in1k,66.690,33.310,86.720,13.280,22.05,224,0.900,bicubic,-12.156,-7.568,-38
wide_resnet101_2.tv_in1k,66.680,33.320,87.030,12.970,126.89,224,0.875,bilinear,-12.158,-7.252,-39
seresnext26d_32x4d.bt_in1k,66.680,33.320,86.830,13.170,16.81,288,0.950,bicubic,-12.134,-7.410,-36
dla60_res2next.in1k,66.660,33.340,87.020,12.980,17.03,224,0.875,bilinear,-11.774,-7.124,-10
wide_resnet50_2.tv_in1k,66.650,33.350,86.800,13.200,68.88,224,0.875,bilinear,-11.830,-7.286,-15
inception_v3.tf_adv_in1k,66.630,33.370,86.590,13.410,23.83,299,0.875,bicubic,-10.962,-7.140,+39
mobilevitv2_100.cvnets_in1k,66.610,33.390,87.020,12.980,4.90,256,0.888,bicubic,-11.466,-7.150,+12
vit_tiny_patch16_384.augreg_in21k_ft_in1k,66.580,33.420,87.250,12.750,5.79,384,1.000,bicubic,-11.844,-7.290,-13
cs3darknet_m.c2ns_in1k,66.580,33.420,87.180,12.820,9.31,288,0.950,bicubic,-11.054,-6.836,+35
levit_128.fb_dist_in1k,66.560,33.440,86.740,13.260,9.21,224,0.900,bicubic,-11.930,-7.272,-22
levit_conv_128.fb_dist_in1k,66.550,33.450,86.730,13.270,9.21,224,0.900,bicubic,-11.944,-7.278,-25
resnet50c.gluon_in1k,66.540,33.460,86.170,13.830,25.58,224,0.875,bicubic,-11.472,-7.818,+12
dla102.in1k,66.530,33.470,86.900,13.100,33.27,224,0.875,bilinear,-11.492,-7.032,+10
tf_efficientnet_b1.in1k,66.530,33.470,86.710,13.290,7.79,240,0.882,bicubic,-12.030,-7.384,-31
vit_base_patch16_224.augreg_in1k,66.480,33.520,86.260,13.740,86.57,224,0.900,bicubic,-12.674,-7.830,-70
vit_base_patch32_384.augreg_in1k,66.430,33.570,86.960,13.040,88.30,384,1.000,bicubic,-12.322,-7.264,-46
gmixer_24_224.ra3_in1k,66.430,33.570,86.160,13.840,24.72,224,0.875,bicubic,-11.596,-7.508,+6
inception_v3.tf_in1k,66.420,33.580,86.680,13.320,23.83,299,0.875,bicubic,-11.442,-7.186,+17
bat_resnext26ts.ch_in1k,66.390,33.610,86.850,13.150,10.73,256,0.900,bicubic,-11.862,-7.248,-12
hardcorenas_f.miil_green_in1k,66.360,33.640,86.190,13.810,8.20,224,0.875,bilinear,-11.738,-7.616,-2
seresnext26ts.ch_in1k,66.330,33.670,86.710,13.290,10.39,288,1.000,bicubic,-11.942,-7.384,-15
coat_lite_tiny.in1k,66.290,33.710,86.960,13.040,5.72,224,0.900,bicubic,-11.230,-6.962,+26
eca_resnext26ts.ch_in1k,66.270,33.730,86.410,13.590,10.30,288,1.000,bicubic,-11.730,-7.516,+3
legacy_seresnet50.in1k,66.270,33.730,86.300,13.700,28.09,224,0.875,bilinear,-11.374,-7.458,+18
efficientnet_b0.ra_in1k,66.250,33.750,85.970,14.030,5.29,224,0.875,bicubic,-11.444,-7.562,+15
cs3darknet_focus_m.c2ns_in1k,66.230,33.770,87.080,12.920,9.30,288,0.950,bicubic,-11.054,-6.886,+36
SelecSls60.in1k,66.220,33.780,86.330,13.670,30.67,224,0.875,bicubic,-11.764,-7.502,0
tf_efficientnet_cc_b0_8e.in1k,66.210,33.790,86.230,13.770,24.01,224,0.875,bicubic,-11.696,-7.430,+4
tf_efficientnet_em.in1k,66.170,33.830,86.350,13.650,6.90,240,0.882,bicubic,-11.952,-7.696,-12
res2net50_26w_4s.in1k,66.150,33.850,86.610,13.390,25.70,224,0.875,bilinear,-11.800,-7.242,-1
resnext50_32x4d.tv_in1k,66.150,33.850,86.040,13.960,25.03,224,0.875,bilinear,-11.468,-7.656,+13
resmlp_12_224.fb_distilled_in1k,66.120,33.880,86.620,13.380,15.35,224,0.875,bicubic,-11.834,-6.940,-6
densenetblur121d.ra_in1k,66.120,33.880,86.600,13.400,8.00,288,0.950,bicubic,-11.200,-7.190,+25
inception_v3.tv_in1k,66.120,33.880,86.330,13.670,23.83,299,0.875,bicubic,-11.314,-7.144,+20
resnet50.a3_in1k,66.100,33.900,85.820,14.180,25.56,224,0.950,bicubic,-11.948,-7.960,-14
efficientnet_b1_pruned.in1k,66.080,33.920,86.550,13.450,6.33,240,0.882,bicubic,-12.162,-7.342,-27
regnety_016.pycls_in1k,66.080,33.920,86.370,13.630,11.20,224,0.875,bicubic,-11.782,-7.348,-3
resnet26t.ra2_in1k,66.070,33.930,86.680,13.320,16.01,320,1.000,bicubic,-12.262,-7.444,-35
gcresnext26ts.ch_in1k,66.040,33.960,86.750,13.250,10.48,288,1.000,bicubic,-12.374,-7.420,-41
resnet50.gluon_in1k,66.030,33.970,86.280,13.720,25.56,224,0.875,bicubic,-11.550,-7.438,+7
rexnet_100.nav_in1k,66.020,33.980,86.490,13.510,4.80,224,0.875,bicubic,-11.842,-7.154,-8
tinynet_a.in1k,66.020,33.980,85.790,14.210,6.19,192,0.875,bicubic,-11.638,-7.750,-1
res2net50_14w_8s.in1k,66.000,34.000,86.230,13.770,25.06,224,0.875,bilinear,-12.158,-7.616,-28
poolformerv2_s12.sail_in1k,65.890,34.110,86.510,13.490,11.89,224,1.000,bicubic,-12.114,-7.354,-19
resnet34.a2_in1k,65.870,34.130,86.140,13.860,21.80,288,1.000,bicubic,-11.288,-7.134,+23
densenet161.tv_in1k,65.850,34.150,86.460,13.540,28.68,224,0.875,bicubic,-11.508,-7.182,+10
res2next50.in1k,65.850,34.150,85.830,14.170,24.67,224,0.875,bilinear,-12.392,-8.006,-39
convnextv2_atto.fcmae_ft_in1k,65.840,34.160,86.170,13.830,3.71,288,0.950,bicubic,-11.920,-7.556,-10
repvgg_b1g4.rvgg_in1k,65.820,34.180,86.110,13.890,39.97,224,0.875,bilinear,-11.762,-7.726,-4
hardcorenas_e.miil_green_in1k,65.820,34.180,85.970,14.030,8.07,224,0.875,bilinear,-11.964,-7.728,-12
regnetx_008.tv2_in1k,65.810,34.190,86.210,13.790,7.26,224,0.965,bicubic,-11.494,-7.456,+7
xcit_tiny_12_p16_224.fb_in1k,65.790,34.210,86.220,13.780,6.72,224,1.000,bicubic,-11.350,-7.496,+16
mobilenetv3_large_100.miil_in21k_ft_in1k,65.790,34.210,85.170,14.830,5.48,224,0.875,bilinear,-12.128,-8.596,-21
ese_vovnet19b_dw.ra_in1k,65.760,34.240,86.480,13.520,6.54,288,0.950,bicubic,-11.984,-7.308,-15
skresnet34.ra_in1k,65.740,34.260,85.950,14.050,22.28,224,0.875,bicubic,-11.170,-7.194,+25
resnet101.tv_in1k,65.700,34.300,85.990,14.010,44.55,224,0.875,bilinear,-11.676,-7.552,-1
convnext_tiny.fb_in22k_ft_in1k,65.650,34.350,86.620,13.380,28.59,288,1.000,bicubic,-13.248,-8.054,-97
hardcorenas_d.miil_green_in1k,65.650,34.350,85.450,14.550,7.50,224,0.875,bilinear,-11.790,-8.040,-6
poolformer_s12.sail_in1k,65.630,34.370,86.210,13.790,11.92,224,0.900,bicubic,-11.604,-7.322,+5
dpn68b.mx_in1k,65.620,34.380,85.950,14.050,12.61,224,0.875,bicubic,-11.898,-7.902,-11
SelecSls42b.in1k,65.610,34.390,85.840,14.160,32.46,224,0.875,bicubic,-11.560,-7.552,+6
convnext_atto_ols.a2_in1k,65.600,34.400,86.260,13.740,3.70,288,0.950,bicubic,-11.616,-7.416,+3
tf_efficientnet_b0.ap_in1k,65.500,34.500,85.570,14.430,5.29,224,0.875,bicubic,-11.590,-7.692,+8
tf_efficientnet_lite2.in1k,65.400,34.600,86.020,13.980,6.09,260,0.890,bicubic,-12.064,-7.730,-13
convmixer_1024_20_ks9_p14.in1k,65.390,34.610,85.610,14.390,24.38,224,0.960,bicubic,-11.546,-7.740,+13
res2net50_48w_2s.in1k,65.360,34.640,85.960,14.040,25.29,224,0.875,bilinear,-12.154,-7.592,-16
resnet26d.bt_in1k,65.340,34.660,86.000,14.000,16.01,288,0.950,bicubic,-12.068,-7.638,-13
densenet201.tv_in1k,65.270,34.730,85.670,14.330,20.01,224,0.875,bicubic,-12.016,-7.810,-7
dla60.in1k,65.230,34.770,85.750,14.250,22.04,224,0.875,bilinear,-11.816,-7.568,+4
seresnet50.a3_in1k,65.190,34.810,85.300,14.700,28.09,224,0.950,bicubic,-11.838,-7.774,+4
crossvit_9_dagger_240.in1k,65.180,34.820,86.570,13.430,8.78,240,0.875,bicubic,-11.800,-7.050,+5
gernet_s.idstcv_in1k,65.150,34.850,85.530,14.470,8.17,224,0.875,bilinear,-11.760,-7.786,+9
tf_efficientnet_cc_b0_4e.in1k,65.150,34.850,85.140,14.860,13.31,224,0.875,bicubic,-12.152,-8.196,-13
mobilenetv2_120d.ra_in1k,65.060,34.940,85.980,14.020,5.83,224,0.875,bicubic,-12.242,-7.524,-15
legacy_seresnext26_32x4d.in1k,65.030,34.970,85.630,14.370,16.79,224,0.875,bicubic,-12.078,-7.684,-5
resnet34.bt_in1k,64.930,35.070,86.200,13.800,21.80,288,0.950,bicubic,-11.550,-7.154,+18
convnext_atto.d2_in1k,64.920,35.080,86.230,13.770,3.70,288,0.950,bicubic,-12.088,-7.472,-2
hrnet_w18.ms_in1k,64.920,35.080,85.730,14.270,21.30,224,0.875,bilinear,-11.832,-7.714,+5
resnext26ts.ra2_in1k,64.890,35.110,85.710,14.290,10.30,288,1.000,bicubic,-12.284,-7.754,-13
hardcorenas_c.miil_green_in1k,64.880,35.120,85.260,14.740,5.52,224,0.875,bilinear,-12.178,-7.908,-8
efficientformerv2_s0.snap_dist_in1k,64.790,35.210,85.650,14.350,3.60,224,0.950,bicubic,-11.328,-7.210,+19
densenet169.tv_in1k,64.790,35.210,85.250,14.750,14.15,224,0.875,bicubic,-11.110,-7.778,+25
mixnet_m.ft_in1k,64.660,35.340,85.460,14.540,5.01,224,0.875,bicubic,-12.598,-7.958,-20
xcit_nano_12_p8_224.fb_dist_in1k,64.610,35.390,85.990,14.010,3.05,224,1.000,bicubic,-11.722,-7.104,+14
levit_128s.fb_dist_in1k,64.590,35.410,84.730,15.270,7.78,224,0.900,bicubic,-11.936,-8.142,+5
levit_conv_128s.fb_dist_in1k,64.590,35.410,84.730,15.270,7.78,224,0.900,bicubic,-11.930,-8.256,+6
repvgg_a2.rvgg_in1k,64.460,35.540,85.140,14.860,28.21,224,0.875,bilinear,-11.998,-7.862,+8
xcit_nano_12_p16_384.fb_dist_in1k,64.420,35.580,85.310,14.690,3.05,384,1.000,bicubic,-11.038,-7.386,+28
tf_efficientnet_lite1.in1k,64.380,35.620,85.480,14.520,5.42,240,0.882,bicubic,-12.264,-7.750,-5
regnetx_016.pycls_in1k,64.380,35.620,85.470,14.530,9.19,224,0.875,bicubic,-12.544,-7.946,-10
hardcorenas_b.miil_green_in1k,64.370,35.630,84.880,15.120,5.18,224,0.875,bilinear,-12.166,-7.880,-3
resmlp_12_224.fb_in1k,64.350,35.650,85.590,14.410,15.35,224,0.875,bicubic,-12.298,-7.588,-8
tf_efficientnet_b0.aa_in1k,64.300,35.700,85.270,14.730,5.29,224,0.875,bicubic,-12.534,-7.950,-11
tf_mixnet_m.in1k,64.260,35.740,85.100,14.900,5.01,224,0.875,bicubic,-12.694,-8.054,-17
densenet121.ra_in1k,64.250,35.750,85.830,14.170,7.98,288,0.950,bicubic,-12.250,-7.538,-2
resnet26.bt_in1k,64.200,35.800,85.210,14.790,16.00,288,0.950,bicubic,-12.166,-7.970,0
tf_efficientnet_es.in1k,64.200,35.800,84.740,15.260,5.44,224,0.875,bicubic,-12.400,-8.462,-10
regnety_008.pycls_in1k,64.150,35.850,85.250,14.750,6.26,224,0.875,bicubic,-12.152,-7.810,+1
dpn68.mx_in1k,64.120,35.880,85.080,14.920,12.61,224,0.875,bicubic,-12.226,-7.928,-2
vit_small_patch32_224.augreg_in21k_ft_in1k,64.090,35.910,85.550,14.450,22.88,224,0.900,bicubic,-11.904,-7.248,+2
mobilenetv2_140.ra_in1k,64.070,35.930,85.030,14.970,6.11,224,0.875,bicubic,-12.450,-7.836,-10
hardcorenas_a.miil_green_in1k,63.690,36.310,84.400,15.600,5.26,224,0.875,bilinear,-12.240,-8.110,+3
resnest14d.gluon_in1k,63.620,36.380,84.230,15.770,10.61,224,0.875,bilinear,-11.890,-8.274,+11
regnety_004.tv2_in1k,63.600,36.400,84.870,15.130,4.34,224,0.965,bicubic,-11.998,-7.826,+8
mobilevitv2_075.cvnets_in1k,63.590,36.410,84.950,15.050,2.87,256,0.888,bicubic,-12.018,-7.794,+6
tf_mixnet_s.in1k,63.580,36.420,84.270,15.730,4.13,224,0.875,bicubic,-12.072,-8.366,+3
tf_efficientnet_b0.in1k,63.520,36.480,84.860,15.140,5.29,224,0.875,bicubic,-13.012,-8.148,-18
mixnet_s.ft_in1k,63.390,36.610,84.700,15.300,4.13,224,0.875,bicubic,-12.604,-8.570,-5
mobilenetv3_large_100.ra_in1k,63.380,36.620,84.080,15.920,5.48,224,0.875,bicubic,-12.386,-8.458,-2
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,63.330,36.670,85.270,14.730,6.36,384,1.000,bicubic,-12.632,-7.994,-7
resnet50.tv_in1k,63.330,36.670,84.670,15.330,25.56,224,0.875,bilinear,-12.800,-8.188,-10
efficientnet_es_pruned.in1k,63.310,36.690,84.950,15.050,5.44,224,0.875,bicubic,-11.704,-7.496,+13
mixer_b16_224.goog_in21k_ft_in1k,63.290,36.710,83.340,16.660,59.88,224,0.875,bicubic,-13.312,-8.884,-27
mobilenetv3_rw.rmsp_in1k,63.240,36.760,84.520,15.480,5.48,224,0.875,bicubic,-12.380,-8.184,-4
efficientnet_lite0.ra_in1k,63.230,36.770,84.410,15.590,4.65,224,0.875,bicubic,-12.250,-8.112,0
semnasnet_100.rmsp_in1k,63.170,36.830,84.540,15.460,3.89,224,0.875,bicubic,-12.280,-8.054,+2
vit_tiny_patch16_224.augreg_in21k_ft_in1k,63.120,36.880,84.900,15.100,5.72,224,0.900,bicubic,-12.334,-7.944,0
regnety_006.pycls_in1k,63.090,36.910,84.240,15.760,6.06,224,0.875,bicubic,-12.180,-8.286,+1
pit_ti_distilled_224.in1k,63.020,36.980,83.810,16.190,5.10,224,0.900,bicubic,-11.234,-8.144,+18
mobilevit_xs.cvnets_in1k,62.940,37.060,84.810,15.190,2.32,256,0.900,bicubic,-11.696,-7.536,+12
densenet121.tv_in1k,62.940,37.060,84.250,15.750,7.98,224,0.875,bicubic,-11.824,-7.904,+10
legacy_seresnet34.in1k,62.880,37.120,84.230,15.770,21.96,224,0.875,bilinear,-11.928,-7.896,+7
hrnet_w18_small_v2.ms_in1k,62.830,37.170,83.970,16.030,15.60,224,0.875,bilinear,-12.284,-8.444,-1
edgenext_x_small.in1k,62.810,37.190,84.670,15.330,2.34,288,1.000,bicubic,-12.878,-8.096,-16
mobilenetv2_110d.ra_in1k,62.810,37.190,84.500,15.500,4.52,224,0.875,bicubic,-12.246,-7.686,-2
deit_tiny_distilled_patch16_224.fb_in1k,62.780,37.220,83.920,16.080,5.91,224,0.900,bicubic,-11.720,-7.972,+10
resnet18.fb_swsl_ig1b_ft_in1k,62.750,37.250,84.300,15.700,11.69,224,0.875,bilinear,-10.532,-7.432,+23
repvgg_b0.rvgg_in1k,62.720,37.280,83.870,16.130,15.82,224,0.875,bilinear,-12.422,-8.546,-8
tinynet_b.in1k,62.710,37.290,84.220,15.780,3.73,188,0.875,bicubic,-12.268,-7.966,-3
tf_efficientnet_lite0.in1k,62.570,37.430,84.230,15.770,4.65,224,0.875,bicubic,-12.260,-7.942,-2
xcit_nano_12_p8_224.fb_in1k,62.560,37.440,84.200,15.800,3.05,224,1.000,bicubic,-11.356,-7.970,+12
resnet34.gluon_in1k,62.550,37.450,84.000,16.000,21.80,224,0.875,bicubic,-12.028,-7.984,+3
dla34.in1k,62.490,37.510,83.920,16.080,15.74,224,0.875,bilinear,-12.148,-8.144,-1
regnetx_008.pycls_in1k,62.480,37.520,84.020,15.980,7.26,224,0.875,bicubic,-12.550,-8.318,-10
fbnetc_100.rmsp_in1k,62.450,37.550,83.370,16.630,5.57,224,0.875,bilinear,-12.680,-9.018,-14
tf_mobilenetv3_large_100.in1k,62.440,37.560,83.950,16.050,5.48,224,0.875,bilinear,-13.076,-8.644,-23
crossvit_9_240.in1k,62.260,37.740,84.250,15.750,8.55,240,0.875,bicubic,-11.698,-7.718,+5
crossvit_tiny_240.in1k,62.080,37.920,83.610,16.390,7.01,240,0.875,bicubic,-11.256,-8.298,+11
regnetx_004_tv.tv2_in1k,62.060,37.940,83.770,16.230,5.50,224,0.965,bicubic,-12.536,-8.408,-5
resnet18d.ra2_in1k,62.000,38.000,83.790,16.210,11.71,288,0.950,bicubic,-11.794,-8.048,+5
mnasnet_100.rmsp_in1k,61.920,38.080,83.720,16.280,4.38,224,0.875,bicubic,-12.730,-8.404,-10
vgg19_bn.tv_in1k,61.870,38.130,83.450,16.550,143.68,224,0.875,bilinear,-12.346,-8.394,-4
regnety_004.pycls_in1k,61.840,38.160,83.410,16.590,4.34,224,0.875,bicubic,-12.188,-8.340,-3
convit_tiny.fb_in1k,61.590,38.410,84.090,15.910,5.71,224,0.875,bicubic,-11.522,-7.622,+8
resnet18.a1_in1k,61.580,38.420,82.470,17.530,11.69,288,1.000,bicubic,-11.578,-8.556,+6
resnet34.a3_in1k,61.490,38.510,82.600,17.400,21.80,224,0.950,bicubic,-11.480,-8.508,+10
resnet18.fb_ssl_yfcc100m_ft_in1k,61.470,38.530,83.330,16.670,11.69,224,0.875,bilinear,-11.124,-8.088,+11
regnetx_006.pycls_in1k,61.360,38.640,83.450,16.550,6.20,224,0.875,bicubic,-12.508,-8.228,-4
spnasnet_100.rmsp_in1k,61.230,38.770,82.760,17.240,4.42,224,0.875,bilinear,-12.856,-9.060,-10
resnet34.tv_in1k,61.220,38.780,82.720,17.280,21.80,224,0.875,bilinear,,,
vit_base_patch32_224.augreg_in1k,61.040,38.960,82.730,17.270,88.22,224,0.900,bicubic,-13.854,-9.048,-24
pit_ti_224.in1k,60.970,39.030,83.860,16.140,4.85,224,0.900,bicubic,-11.940,-7.540,+5
skresnet18.ra_in1k,60.850,39.150,82.850,17.150,11.96,224,0.875,bicubic,-12.188,-8.322,0
ghostnet_100.in1k,60.810,39.190,82.380,17.620,5.18,224,0.875,bilinear,-13.172,-9.086,-13
vgg16_bn.tv_in1k,60.770,39.230,82.960,17.040,138.37,224,0.875,bilinear,-12.600,-8.554,-7
semnasnet_075.rmsp_in1k,60.710,39.290,82.540,17.460,2.91,224,0.875,bicubic,-12.282,-8.598,-2
tf_mobilenetv3_large_075.in1k,60.390,39.610,81.950,18.050,3.99,224,0.875,bilinear,-13.042,-9.402,-10
xcit_nano_12_p16_224.fb_dist_in1k,60.240,39.760,82.490,17.510,3.05,224,1.000,bicubic,-12.070,-8.370,+5
resnet18.a2_in1k,60.210,39.790,81.840,18.160,11.69,288,1.000,bicubic,-12.162,-8.756,+2
mobilenetv2_100.ra_in1k,60.170,39.830,82.210,17.790,3.50,224,0.875,bicubic,-12.806,-8.800,-5
vit_base_patch32_224.sam_in1k,59.990,40.010,81.220,18.780,88.22,224,0.900,bicubic,-13.704,-9.794,-15
deit_tiny_patch16_224.fb_in1k,59.800,40.200,82.670,17.330,5.72,224,0.900,bicubic,-12.372,-8.442,+4
legacy_seresnet18.in1k,59.790,40.210,81.680,18.320,11.78,224,0.875,bicubic,-11.976,-8.652,+8
vgg19.tv_in1k,59.710,40.290,81.460,18.540,143.67,224,0.875,bilinear,-12.668,-9.414,-4
edgenext_xx_small.in1k,59.410,40.590,81.840,18.160,1.33,288,1.000,bicubic,-12.468,-8.712,+4
regnetx_004.pycls_in1k,59.410,40.590,81.740,18.260,5.16,224,0.875,bicubic,-12.988,-9.088,-7
tf_mobilenetv3_large_minimal_100.in1k,59.090,40.910,81.130,18.870,3.92,224,0.875,bilinear,-13.168,-9.508,-3
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,59.080,40.920,81.760,18.240,6.34,224,0.900,bicubic,-12.718,-9.064,+2
hrnet_w18_small.ms_in1k,58.960,41.040,81.340,18.660,13.19,224,0.875,bilinear,-13.378,-9.338,-7
vgg13_bn.tv_in1k,58.960,41.040,81.090,18.910,133.05,224,0.875,bilinear,-12.628,-9.288,+3
lcnet_100.ra2_in1k,58.930,41.070,81.200,18.800,2.95,224,0.875,bicubic,-13.166,-9.162,-4
vgg16.tv_in1k,58.830,41.170,81.660,18.340,138.36,224,0.875,bilinear,-12.762,-8.724,0
pvt_v2_b0.in1k,58.770,41.230,82.120,17.880,3.67,224,0.900,bicubic,-11.890,-8.076,+3
resnet18.gluon_in1k,58.340,41.660,80.970,19.030,11.69,224,0.875,bicubic,-12.498,-8.784,+1
xcit_nano_12_p16_224.fb_in1k,58.330,41.670,80.920,19.080,3.05,224,1.000,bicubic,-11.632,-8.842,+5
tinynet_c.in1k,58.200,41.800,80.280,19.720,2.46,184,0.875,bicubic,-13.040,-9.454,-2
resnet14t.c3_in1k,58.180,41.820,80.300,19.700,10.08,224,0.950,bicubic,-14.074,-10.002,-12
mobilevitv2_050.cvnets_in1k,57.710,42.290,80.880,19.120,1.37,256,0.888,bicubic,-12.428,-9.040,+1
vgg11_bn.tv_in1k,57.410,42.590,80.010,19.990,132.87,224,0.875,bilinear,-12.972,-9.798,-2
resnet18.tv_in1k,57.170,42.830,80.190,19.810,11.69,224,0.875,bilinear,-12.590,-8.880,+2
mobilevit_xxs.cvnets_in1k,57.160,42.840,79.750,20.250,1.27,256,0.900,bicubic,-11.772,-9.192,+3
vgg13.tv_in1k,57.140,42.860,79.550,20.450,133.05,224,0.875,bilinear,-12.792,-9.700,-1
regnety_002.pycls_in1k,57.040,42.960,79.880,20.120,3.16,224,0.875,bicubic,-13.238,-9.648,-5
mixer_l16_224.goog_in21k_ft_in1k,56.660,43.340,76.010,23.990,208.20,224,0.875,bicubic,-15.394,-11.664,-16
regnetx_002.pycls_in1k,56.080,43.920,79.190,20.810,2.68,224,0.875,bicubic,-12.666,-9.346,+1
dla60x_c.in1k,56.010,43.990,78.980,21.020,1.32,224,0.875,bilinear,-11.888,-9.442,+4
resnet18.a3_in1k,56.000,44.000,78.980,21.020,11.69,224,0.950,bicubic,-12.254,-9.196,+1
vgg11.tv_in1k,55.820,44.180,78.830,21.170,132.86,224,0.875,bilinear,-13.202,-9.794,-5
resnet10t.c3_in1k,55.560,44.440,78.440,21.560,5.44,224,0.950,bicubic,-12.798,-9.600,-2
lcnet_075.ra2_in1k,55.440,44.560,78.350,21.650,2.36,224,0.875,bicubic,-13.342,-10.010,-5
mobilenetv3_small_100.lamb_in1k,54.690,45.310,77.760,22.240,2.54,224,0.875,bicubic,-12.966,-9.876,0
tf_mobilenetv3_small_100.in1k,54.470,45.530,77.080,22.920,2.54,224,0.875,bilinear,-13.448,-10.594,-3
tinynet_d.in1k,53.420,46.580,76.320,23.680,2.34,152,0.875,bicubic,-13.554,-10.742,-1
mnasnet_small.lamb_in1k,53.270,46.730,75.910,24.090,2.03,224,0.875,bicubic,-12.926,-10.594,-1
dla46x_c.in1k,53.040,46.960,76.820,23.180,1.07,224,0.875,bilinear,-12.948,-10.160,-1
mobilenetv2_050.lamb_in1k,52.860,47.140,75.460,24.540,1.97,224,0.875,bicubic,-13.090,-10.628,-1
dla46_c.in1k,52.190,47.810,75.680,24.320,1.30,224,0.875,bilinear,-12.682,-10.618,+1
tf_mobilenetv3_small_075.in1k,52.180,47.820,75.450,24.550,2.04,224,0.875,bilinear,-13.548,-10.676,-2
mobilenetv3_small_075.lamb_in1k,51.920,48.080,74.740,25.260,2.04,224,0.875,bicubic,-13.314,-10.706,-2
lcnet_050.ra2_in1k,49.980,50.020,73.450,26.550,1.88,224,0.875,bicubic,-13.140,-10.936,-1
tf_mobilenetv3_small_minimal_100.in1k,49.530,50.470,73.030,26.970,2.04,224,0.875,bilinear,-13.360,-11.210,-1
tinynet_e.in1k,46.720,53.280,70.350,29.650,2.04,106,0.875,bicubic,-13.146,-11.414,-1
mobilenetv3_small_050.lamb_in1k,44.850,55.150,67.680,32.320,1.59,224,0.875,bicubic,-13.060,-12.500,-1
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-sketch.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva_giant_patch14_336.clip_ft_in1k,71.177,28.823,90.299,9.701,"1,013.01",336,1.000,bicubic,-18.289,-8.527,+6
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,70.662,29.338,89.854,10.146,305.08,448,1.000,bicubic,-19.390,-9.194,-1
eva02_large_patch14_448.mim_m38m_ft_in1k,70.544,29.456,89.843,10.157,305.08,448,1.000,bicubic,-19.026,-9.079,+2
convnext_xxlarge.clip_laion2b_soup_ft_in1k,70.039,29.961,90.334,9.666,846.47,256,1.000,bicubic,-18.565,-8.374,+10
eva_giant_patch14_224.clip_ft_in1k,70.021,29.979,89.768,10.232,"1,012.56",224,0.900,bicubic,-18.859,-8.912,+4
eva_giant_patch14_336.m30m_ft_in22k_in1k,68.052,31.948,87.819,12.181,"1,013.01",336,1.000,bicubic,-21.514,-11.133,0
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,67.533,32.467,87.504,12.496,305.08,448,1.000,bicubic,-22.433,-11.508,-5
eva_giant_patch14_560.m30m_ft_in22k_in1k,67.486,32.514,87.473,12.527,"1,014.45",560,1.000,bicubic,-22.300,-11.519,-5
vit_huge_patch14_clip_224.laion2b_ft_in1k,67.396,32.604,87.882,12.118,632.05,224,1.000,bicubic,-20.192,-10.337,+39
eva02_large_patch14_448.mim_in22k_ft_in1k,66.985,33.015,87.443,12.557,305.08,448,1.000,bicubic,-22.639,-11.507,-6
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,66.435,33.565,87.349,12.651,200.13,384,1.000,bicubic,-21.413,-11.097,+31
vit_large_patch14_clip_336.laion2b_ft_in1k,65.745,34.255,86.905,13.095,304.53,336,1.000,bicubic,-22.111,-11.463,+28
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,65.731,34.269,87.017,12.983,200.13,384,1.000,bicubic,-22.575,-11.565,+10
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,65.323,34.677,86.826,13.174,632.05,224,1.000,bicubic,-22.933,-11.726,+11
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,65.260,34.740,86.757,13.242,632.46,336,1.000,bicubic,-23.332,-11.905,+1
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,65.091,34.909,86.284,13.716,200.13,256,1.000,bicubic,-22.243,-11.934,+42
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,64.920,35.080,86.651,13.349,200.13,320,1.000,bicubic,-23.038,-11.825,+19
vit_large_patch14_clip_224.laion2b_ft_in1k,64.814,35.186,86.575,13.425,304.20,224,1.000,bicubic,-22.468,-11.671,+42
regnety_1280.swag_ft_in1k,64.106,35.894,86.034,13.966,644.81,384,1.000,bicubic,-24.124,-12.652,+9
vit_large_patch14_clip_336.openai_ft_in12k_in1k,64.071,35.929,85.914,14.086,304.53,336,1.000,bicubic,-24.197,-12.612,+4
eva_large_patch14_336.in22k_ft_in1k,63.096,36.904,84.380,15.620,304.53,336,1.000,bicubic,-25.574,-14.342,-8
vit_large_patch14_clip_224.openai_ft_in1k,62.630,37.370,85.113,14.887,304.20,224,1.000,bicubic,-25.222,-13.313,+19
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,62.061,37.939,84.319,15.681,304.20,224,1.000,bicubic,-25.831,-14.091,+16
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,61.612,38.388,83.647,16.353,304.53,336,1.000,bicubic,-26.566,-14.923,+8
vit_large_patch14_clip_224.openai_ft_in12k_in1k,61.404,38.596,83.362,16.638,304.20,224,1.000,bicubic,-26.772,-15.182,+8
eva_large_patch14_196.in22k_ft_in1k,61.117,38.883,82.778,17.222,304.14,196,1.000,bicubic,-26.817,-15.720,+11
eva_large_patch14_336.in22k_ft_in22k_in1k,60.941,39.059,82.134,17.866,304.53,336,1.000,bicubic,-28.267,-16.720,-19
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,60.795,39.205,83.220,16.780,88.59,384,1.000,bicubic,-26.339,-15.002,+35
convnext_base.clip_laion2b_augreg_ft_in1k,60.276,39.724,82.678,17.322,88.59,256,1.000,bicubic,-25.882,-15.002,+88
regnety_1280.swag_lc_in1k,59.911,40.089,83.157,16.843,644.81,224,0.965,bicubic,-26.071,-14.692,+102
eva_large_patch14_196.in22k_ft_in22k_in1k,59.879,40.121,81.139,18.861,304.14,196,1.000,bicubic,-28.697,-17.519,-14
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,59.836,40.164,82.810,17.190,88.59,256,1.000,bicubic,-26.534,-15.174,+72
convnext_base.clip_laiona_augreg_ft_in1k_384,59.392,40.608,82.242,17.758,88.59,384,1.000,bicubic,-27.110,-15.726,+62
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,58.504,41.496,80.796,19.204,87.12,448,1.000,bicubic,-30.188,-17.928,-23
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,58.376,41.624,80.398,19.602,468.53,224,0.875,bilinear,-26.722,-17.040,+159
beitv2_large_patch16_224.in1k_ft_in22k_in1k,58.366,41.634,80.226,19.774,304.43,224,0.950,bicubic,-30.028,-18.372,-16
eva02_base_patch14_448.mim_in22k_ft_in1k,58.038,41.962,80.770,19.230,87.12,448,1.000,bicubic,-30.212,-17.794,-11
regnety_320.swag_ft_in1k,57.908,42.092,81.456,18.544,145.05,384,1.000,bicubic,-28.928,-16.906,+41
convnextv2_huge.fcmae_ft_in22k_in1k_384,57.865,42.135,79.671,20.329,660.29,384,1.000,bicubic,-30.805,-19.067,-27
convnextv2_huge.fcmae_ft_in22k_in1k_512,57.851,42.149,79.497,20.503,660.29,512,1.000,bicubic,-31.007,-19.251,-30
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,57.696,42.304,79.907,20.093,194.03,224,0.875,bilinear,-26.470,-17.291,+236
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,57.478,42.522,80.373,19.627,194.03,224,0.875,bilinear,-25.858,-16.473,+315
beit_large_patch16_384.in22k_ft_in22k_in1k,56.879,43.121,79.221,20.779,305.00,384,1.000,bicubic,-31.523,-19.387,-24
vit_base_patch16_clip_384.laion2b_ft_in1k,56.877,43.123,80.002,19.998,86.86,384,1.000,bicubic,-29.743,-18.006,+44
beit_large_patch16_512.in22k_ft_in22k_in1k,56.757,43.243,78.911,21.089,305.67,512,1.000,bicubic,-31.839,-19.745,-30
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,56.438,43.562,78.931,21.069,88.79,224,0.875,bilinear,-27.864,-18.245,+217
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,56.323,43.677,77.302,22.698,116.14,384,1.000,bicubic,-31.505,-21.070,-4
maxvit_xlarge_tf_384.in21k_ft_in1k,56.211,43.789,78.746,21.254,475.32,384,1.000,bicubic,-32.103,-19.798,-26
maxvit_xlarge_tf_512.in21k_ft_in1k,56.157,43.843,78.632,21.368,475.77,512,1.000,bicubic,-32.380,-20.012,-31
maxvit_base_tf_512.in21k_ft_in1k,56.081,43.919,78.606,21.394,119.88,512,1.000,bicubic,-32.137,-19.922,-20
deit3_huge_patch14_224.fb_in22k_ft_in1k,55.767,44.233,77.626,22.374,632.13,224,1.000,bicubic,-31.420,-20.634,+11
maxvit_base_tf_384.in21k_ft_in1k,55.649,44.351,78.074,21.926,119.65,384,1.000,bicubic,-32.273,-20.470,-14
vit_base_patch16_clip_224.laion2b_ft_in1k,55.411,44.589,79.049,20.951,86.57,224,1.000,bicubic,-30.055,-18.525,+108
regnety_320.swag_lc_in1k,55.356,44.644,79.711,20.289,145.05,224,0.965,bicubic,-29.190,-17.731,+178
regnety_160.swag_ft_in1k,55.181,44.819,79.320,20.680,83.59,384,1.000,bicubic,-30.829,-18.734,+71
maxvit_large_tf_512.in21k_ft_in1k,55.171,44.829,77.276,22.724,212.33,512,1.000,bicubic,-33.055,-21.322,-27
maxvit_large_tf_384.in21k_ft_in1k,55.077,44.923,77.142,22.858,212.03,384,1.000,bicubic,-32.909,-21.426,-22
beit_large_patch16_224.in22k_ft_in22k_in1k,54.965,45.035,77.608,22.392,304.43,224,0.900,bicubic,-32.513,-20.696,-8
convnext_xlarge.fb_in22k_ft_in1k_384,54.961,45.039,76.824,23.176,350.20,384,1.000,bicubic,-32.791,-21.732,-15
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,54.908,45.092,77.541,22.459,88.79,224,0.875,bilinear,-27.790,-18.603,+353
deit3_large_patch16_384.fb_in22k_ft_in1k,54.886,45.114,77.372,22.628,304.76,384,1.000,bicubic,-32.834,-21.140,-16
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,54.747,45.253,76.848,23.152,116.09,384,1.000,bicubic,-32.717,-21.526,-10
caformer_b36.sail_in22k_ft_in1k_384,54.442,45.558,76.832,23.168,98.75,384,1.000,bicubic,-33.616,-21.750,-29
deit3_large_patch16_224.fb_in22k_ft_in1k,54.359,45.641,76.563,23.437,304.37,224,1.000,bicubic,-32.623,-21.673,+8
beitv2_large_patch16_224.in1k_ft_in1k,54.161,45.839,75.562,24.438,304.43,224,0.950,bicubic,-33.251,-22.671,-9
convnextv2_large.fcmae_ft_in22k_in1k_384,53.947,46.053,76.007,23.993,197.96,384,1.000,bicubic,-34.251,-22.521,-35
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,53.733,46.267,75.142,24.858,116.14,224,0.950,bicubic,-33.161,-22.872,+8
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,53.587,46.413,76.337,23.663,44.18,224,0.875,bilinear,-29.645,-20.421,+296
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,53.491,46.509,75.659,24.341,86.86,384,1.000,bicubic,-33.717,-22.375,-8
vit_base_patch16_clip_384.openai_ft_in1k,53.080,46.920,76.661,23.339,86.86,384,1.000,bicubic,-33.124,-21.213,+42
regnety_160.swag_lc_in1k,53.049,46.951,78.088,21.912,83.59,224,0.965,bicubic,-30.733,-19.192,+242
convnextv2_base.fcmae_ft_in22k_in1k_384,52.907,47.093,75.083,24.917,88.72,384,1.000,bicubic,-34.737,-23.333,-26
convformer_b36.sail_in22k_ft_in1k_384,52.874,47.126,74.979,25.021,99.88,384,1.000,bicubic,-34.728,-23.455,-26
convnext_large.fb_in22k_ft_in1k_384,52.774,47.226,74.700,25.300,197.77,384,1.000,bicubic,-34.698,-23.686,-23
vit_large_patch16_384.augreg_in21k_ft_in1k,52.758,47.242,74.706,25.294,304.72,384,1.000,bicubic,-34.326,-23.596,-9
caformer_b36.sail_in22k_ft_in1k,52.756,47.244,75.309,24.691,98.75,224,1.000,bicubic,-34.666,-23.017,-21
convformer_b36.sail_in22k_ft_in1k,52.746,47.254,74.896,25.104,99.88,224,1.000,bicubic,-34.252,-23.276,-7
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,52.389,47.611,73.802,26.198,73.88,384,1.000,bicubic,-34.994,-24.510,-21
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,52.304,47.696,74.415,25.585,196.74,384,1.000,bicubic,-35.160,-23.835,-26
convnext_xlarge.fb_in22k_ft_in1k,52.216,47.784,73.955,26.045,350.20,288,1.000,bicubic,-35.114,-24.373,-21
vit_large_r50_s32_384.augreg_in21k_ft_in1k,52.043,47.957,73.568,26.432,329.09,384,1.000,bicubic,-34.139,-24.354,+33
vit_large_patch16_224.augreg_in21k_ft_in1k,51.821,48.179,73.688,26.312,304.33,224,0.900,bicubic,-34.013,-24.130,+55
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,51.783,48.217,74.631,25.369,86.57,224,0.950,bicubic,-34.391,-23.125,+32
convnext_base.fb_in22k_ft_in1k_384,51.565,48.435,74.543,25.457,88.59,384,1.000,bicubic,-35.231,-23.721,-2
tf_efficientnet_l2.ns_jft_in1k_475,51.496,48.504,73.937,26.063,480.31,475,0.936,bicubic,-36.738,-24.609,-58
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,51.190,48.810,73.126,26.874,116.09,224,0.950,bicubic,-35.452,-24.894,+1
vit_base_patch16_clip_384.openai_ft_in12k_in1k,51.153,48.847,74.323,25.677,86.86,384,0.950,bicubic,-35.871,-23.858,-18
caformer_m36.sail_in22k_ft_in1k_384,51.052,48.948,73.436,26.564,56.20,384,1.000,bicubic,-36.398,-24.872,-34
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,50.976,49.024,73.295,26.705,87.92,384,1.000,bicubic,-36.120,-24.939,-24
vit_base_patch16_clip_224.openai_ft_in1k,50.938,49.062,74.839,25.161,86.57,224,0.900,bicubic,-34.354,-22.599,+85
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,50.463,49.537,73.366,26.634,25.03,224,0.875,bilinear,-31.711,-22.856,+395
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,50.433,49.567,72.735,27.265,196.74,256,0.900,bicubic,-36.519,-25.371,-19
swin_large_patch4_window12_384.ms_in22k_ft_in1k,50.394,49.606,72.538,27.462,196.74,384,1.000,bicubic,-36.738,-25.696,-29
convnextv2_large.fcmae_ft_in22k_in1k,50.158,49.842,72.399,27.601,197.96,288,1.000,bicubic,-37.326,-25.957,-45
convnext_large.fb_in22k_ft_in1k,49.999,50.001,72.269,27.731,197.77,288,1.000,bicubic,-37.027,-25.935,-27
tf_efficientnetv2_xl.in21k_ft_in1k,49.722,50.278,72.124,27.876,208.12,512,1.000,bicubic,-37.026,-25.890,-12
caformer_m36.sail_in22k_ft_in1k,49.698,50.302,72.141,27.859,56.20,224,1.000,bicubic,-36.896,-25.883,-7
vit_base_patch16_clip_224.openai_ft_in12k_in1k,49.696,50.304,72.874,27.126,86.57,224,0.950,bicubic,-36.244,-24.854,+35
resnet50.fb_swsl_ig1b_ft_in1k,49.518,50.482,72.330,27.670,25.56,224,0.875,bilinear,-31.664,-23.662,+477
beitv2_base_patch16_224.in1k_ft_in22k_in1k,49.516,50.484,72.391,27.609,86.53,224,0.900,bicubic,-36.958,-25.661,-1
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,49.394,50.606,70.846,29.154,73.88,224,0.950,bicubic,-37.110,-27.048,-7
convnextv2_base.fcmae_ft_in22k_in1k,49.142,50.858,71.230,28.770,88.72,288,1.000,bicubic,-37.856,-26.939,-31
convformer_m36.sail_in22k_ft_in1k_384,49.132,50.868,71.387,28.613,57.05,384,1.000,bicubic,-37.760,-26.729,-27
convformer_m36.sail_in22k_ft_in1k,49.091,50.909,71.469,28.531,57.05,224,1.000,bicubic,-37.055,-26.381,+14
vit_base_patch32_clip_224.laion2b_ft_in1k,49.062,50.938,72.584,27.416,88.22,224,0.900,bicubic,-33.520,-23.617,+328
swin_large_patch4_window7_224.ms_in22k_ft_in1k,48.993,51.007,71.387,28.613,196.53,224,0.900,bicubic,-37.319,-26.515,0
convnext_base.fb_in22k_ft_in1k,48.938,51.062,71.748,28.252,88.59,288,1.000,bicubic,-37.336,-26.344,+1
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,48.781,51.219,71.410,28.590,87.92,256,0.900,bicubic,-37.487,-26.472,+1
tf_efficientnetv2_l.in21k_ft_in1k,48.737,51.263,71.994,28.006,118.52,480,1.000,bicubic,-38.065,-26.142,-29
coatnet_2_rw_224.sw_in12k_ft_in1k,48.678,51.322,70.121,29.879,73.87,224,0.950,bicubic,-37.889,-27.773,-18
beit_base_patch16_384.in22k_ft_in22k_in1k,48.669,51.331,72.102,27.898,86.74,384,1.000,bicubic,-38.131,-26.034,-30
swin_base_patch4_window12_384.ms_in22k_ft_in1k,48.545,51.455,71.819,28.181,87.90,384,1.000,bicubic,-37.893,-26.247,-11
caformer_s36.sail_in22k_ft_in1k_384,48.480,51.520,71.514,28.485,39.30,384,1.000,bicubic,-38.376,-26.698,-36
maxvit_base_tf_512.in1k,48.240,51.760,70.793,29.207,119.88,512,1.000,bicubic,-38.362,-27.125,-25
vit_large_r50_s32_224.augreg_in21k_ft_in1k,48.189,51.811,70.870,29.130,328.99,224,0.900,bicubic,-36.233,-26.300,+139
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,47.934,52.066,70.923,29.077,88.30,384,1.000,bicubic,-37.432,-26.733,+56
tf_efficientnet_b7.ns_jft_in1k,47.798,52.202,69.638,30.362,66.35,600,0.949,bicubic,-39.042,-28.456,-39
tf_efficientnet_b6.ns_jft_in1k,47.769,52.231,69.958,30.042,43.04,528,0.942,bicubic,-38.687,-27.926,-18
vit_base_patch8_224.augreg_in21k_ft_in1k,47.733,52.267,70.931,29.069,86.58,224,0.900,bicubic,-38.065,-26.859,+21
deit3_base_patch16_384.fb_in22k_ft_in1k,47.682,52.318,69.758,30.242,86.88,384,1.000,bicubic,-39.056,-28.356,-35
tf_efficientnet_l2.ns_jft_in1k,47.572,52.428,70.021,29.979,480.31,800,0.960,bicubic,-40.780,-28.627,-100
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,47.568,52.432,70.047,29.953,88.34,448,1.000,bicubic,-38.210,-27.591,+21
vit_base_patch8_224.augreg2_in21k_ft_in1k,47.511,52.489,70.328,29.672,86.58,224,0.900,bicubic,-38.707,-27.504,-12
tf_efficientnetv2_m.in21k_ft_in1k,47.458,52.542,70.931,29.069,54.14,480,1.000,bicubic,-38.542,-27.015,+3
deit3_base_patch16_224.fb_in22k_ft_in1k,47.374,52.626,69.772,30.229,86.59,224,1.000,bicubic,-38.326,-27.974,+23
convformer_s36.sail_in22k_ft_in1k_384,47.152,52.848,69.498,30.502,40.01,384,1.000,bicubic,-39.226,-28.486,-23
maxvit_large_tf_512.in1k,47.022,52.978,69.506,30.494,212.33,512,1.000,bicubic,-39.504,-28.374,-34
convnext_small.fb_in22k_ft_in1k_384,46.882,53.118,69.528,30.472,50.22,384,1.000,bicubic,-38.896,-28.362,+14
convnextv2_huge.fcmae_ft_in1k,46.880,53.120,67.785,32.215,660.29,288,1.000,bicubic,-39.700,-30.187,-38
convformer_s36.sail_in22k_ft_in1k,46.863,53.137,69.532,30.468,40.01,224,1.000,bicubic,-38.551,-28.036,+36
caformer_s36.sail_in22k_ft_in1k,46.708,53.292,69.744,30.256,39.30,224,1.000,bicubic,-39.084,-28.080,+10
beit_base_patch16_224.in22k_ft_in22k_in1k,46.254,53.746,69.885,30.115,86.53,224,0.900,bicubic,-38.958,-27.773,+52
vit_base_patch32_clip_384.openai_ft_in12k_in1k,46.250,53.750,69.292,30.708,88.30,384,0.950,bicubic,-38.966,-28.114,+50
maxvit_base_tf_384.in1k,46.234,53.766,68.529,31.471,119.65,384,1.000,bicubic,-40.068,-29.269,-27
hrnet_w48_ssld.paddle_in1k,46.173,53.827,68.054,31.946,77.47,288,1.000,bilinear,-38.305,-29.180,+107
beitv2_base_patch16_224.in1k_ft_in1k,46.002,53.998,67.859,32.141,86.53,224,0.900,bicubic,-39.592,-29.647,+17
vit_base_patch16_384.augreg_in21k_ft_in1k,45.890,54.110,68.557,31.443,86.86,384,1.000,bicubic,-40.106,-29.445,-9
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,45.863,54.137,68.608,31.392,93.59,320,1.000,bicubic,-40.861,-29.568,-52
tf_efficientnet_b8.ap_in1k,45.778,54.222,67.907,32.093,87.41,672,0.954,bicubic,-39.586,-29.385,+34
maxvit_large_tf_384.in1k,45.760,54.240,68.156,31.844,212.03,384,1.000,bicubic,-40.468,-29.534,-30
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,45.758,54.242,68.881,31.119,88.22,224,0.900,bicubic,-37.538,-27.647,+221
convnext_small.in12k_ft_in1k_384,45.721,54.279,67.818,32.182,50.22,384,1.000,bicubic,-40.461,-30.104,-29
tf_efficientnet_b5.ns_jft_in1k,45.611,54.389,67.852,32.148,30.39,456,0.934,bicubic,-40.477,-29.902,-22
swin_base_patch4_window7_224.ms_in22k_ft_in1k,45.532,54.468,68.504,31.496,87.77,224,0.900,bicubic,-39.740,-29.060,+33
mvitv2_large.fb_in1k,45.277,54.723,65.183,34.817,217.99,224,0.900,bicubic,-39.967,-32.031,+35
vit_base_patch16_224.augreg2_in21k_ft_in1k,45.114,54.886,67.394,32.606,86.57,224,0.900,bicubic,-39.982,-30.136,+49
vit_base_patch32_clip_224.openai_ft_in1k,45.031,54.969,68.453,31.547,88.22,224,0.900,bicubic,-36.899,-27.513,+364
seresnextaa101d_32x8d.sw_in12k_ft_in1k,44.801,55.199,67.370,32.630,93.59,288,1.000,bicubic,-41.683,-30.660,-50
convnextv2_large.fcmae_ft_in1k,44.797,55.203,65.853,34.147,197.96,288,1.000,bicubic,-41.320,-31.969,-30
volo_d5_512.sail_in1k,44.577,55.423,65.767,34.233,296.09,512,1.150,bicubic,-42.481,-32.203,-83
convnextv2_tiny.fcmae_ft_in22k_in1k_384,44.322,55.678,66.655,33.345,28.64,384,1.000,bicubic,-40.784,-30.973,+41
cait_m48_448.fb_dist_in1k,44.212,55.788,64.674,35.326,356.46,448,1.000,bicubic,-42.280,-33.078,-55
deit3_large_patch16_384.fb_in1k,44.181,55.819,64.845,35.155,304.76,384,1.000,bicubic,-41.631,-32.753,-14
volo_d5_448.sail_in1k,44.100,55.900,65.065,34.935,295.91,448,1.150,bicubic,-42.852,-32.873,-80
eva02_small_patch14_336.mim_in22k_ft_in1k,43.966,56.034,65.934,34.066,22.13,336,1.000,bicubic,-41.750,-31.698,-8
deit3_huge_patch14_224.fb_in1k,43.807,56.193,64.350,35.650,632.13,224,0.900,bicubic,-41.417,-33.010,+26
convnext_small.fb_in22k_ft_in1k,43.620,56.380,66.464,33.536,50.22,288,1.000,bicubic,-41.642,-31.218,+21
deit3_large_patch16_224.fb_in1k,43.514,56.486,63.574,36.426,304.37,224,0.900,bicubic,-41.262,-33.464,+62
vit_base_r50_s16_384.orig_in21k_ft_in1k,43.507,56.493,66.783,33.217,98.95,384,1.000,bicubic,-41.469,-30.507,+46
tf_efficientnet_b4.ns_jft_in1k,43.447,56.553,65.511,34.489,19.34,380,0.922,bicubic,-41.715,-31.959,+28
deit3_medium_patch16_224.fb_in22k_ft_in1k,43.275,56.725,64.882,35.118,38.85,224,1.000,bicubic,-41.277,-32.306,+70
volo_d5_224.sail_in1k,43.245,56.755,64.079,35.921,295.46,224,0.960,bicubic,-42.825,-33.497,-39
vit_base_patch16_224.augreg_in21k_ft_in1k,43.221,56.779,65.722,34.279,86.57,224,0.900,bicubic,-41.310,-31.570,+71
volo_d4_448.sail_in1k,43.133,56.867,64.108,35.892,193.41,448,1.150,bicubic,-43.659,-33.776,-81
efficientnet_b5.sw_in12k_ft_in1k,42.872,57.128,65.413,34.587,30.39,448,1.000,bicubic,-43.020,-32.323,-31
xcit_large_24_p8_384.fb_dist_in1k,42.838,57.162,63.418,36.582,188.93,384,1.000,bicubic,-43.158,-34.272,-37
regnety_160.lion_in12k_ft_in1k,42.746,57.254,64.204,35.796,83.59,288,1.000,bicubic,-43.240,-33.632,-37
regnety_160.sw_in12k_ft_in1k,42.687,57.313,64.346,35.654,83.59,288,1.000,bicubic,-43.299,-33.488,-37
maxvit_small_tf_512.in1k,42.679,57.321,64.538,35.462,69.13,512,1.000,bicubic,-43.403,-33.225,-47
convnext_small.in12k_ft_in1k,42.669,57.331,64.342,35.658,50.22,288,1.000,bicubic,-42.661,-33.204,+4
xcit_large_24_p8_224.fb_dist_in1k,42.557,57.443,63.098,36.902,188.93,224,1.000,bicubic,-42.845,-34.304,-3
tf_efficientnet_b8.ra_in1k,42.496,57.504,64.882,35.118,87.41,672,0.954,bicubic,-42.872,-32.512,-1
caformer_b36.sail_in1k,42.465,57.535,62.849,37.151,98.75,224,1.000,bicubic,-43.041,-34.461,-14
caformer_b36.sail_in1k_384,42.457,57.543,62.856,37.144,98.75,384,1.000,bicubic,-43.951,-34.958,-72
maxvit_large_tf_224.in1k,42.414,57.586,63.399,36.601,211.79,224,0.950,bicubic,-42.528,-33.571,+32
cait_m36_384.fb_dist_in1k,42.408,57.592,63.324,36.676,271.22,384,1.000,bicubic,-43.652,-34.406,-52
volo_d4_224.sail_in1k,42.304,57.696,63.002,36.998,192.96,224,0.960,bicubic,-43.568,-34.470,-42
caformer_s18.sail_in22k_ft_in1k_384,42.052,57.948,64.770,35.230,26.34,384,1.000,bicubic,-43.362,-32.932,-13
deit3_small_patch16_384.fb_in22k_ft_in1k,41.952,58.048,64.554,35.446,22.21,384,1.000,bicubic,-42.872,-32.934,+37
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,41.895,58.105,63.701,36.299,39.03,384,0.950,bicubic,-43.637,-33.937,-22
maxvit_tiny_tf_512.in1k,41.842,58.158,63.576,36.424,31.05,512,1.000,bicubic,-43.822,-34.008,-31
caformer_s36.sail_in1k_384,41.738,58.262,62.760,37.240,39.30,384,1.000,bicubic,-44.002,-34.912,-36
swin_small_patch4_window7_224.ms_in22k_ft_in1k,41.590,58.410,64.542,35.458,49.61,224,0.900,bicubic,-41.708,-32.422,+178
convformer_s18.sail_in22k_ft_in1k_384,41.571,58.429,63.348,36.652,26.77,384,1.000,bicubic,-43.427,-34.222,+20
regnety_2560.seer_ft_in1k,41.530,58.470,64.894,35.106,"1,282.60",384,1.000,bicubic,-43.622,-32.542,+4
caformer_m36.sail_in1k_384,41.498,58.502,61.528,38.472,56.20,384,1.000,bicubic,-44.668,-36.292,-70
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,41.486,58.514,61.485,38.515,41.72,224,0.950,bicubic,-43.424,-35.473,+24
tf_efficientnet_b7.ra_in1k,41.439,58.561,63.027,36.973,66.35,600,0.949,bicubic,-43.493,-34.181,+20
tf_efficientnet_b7.ap_in1k,41.429,58.571,62.876,37.124,66.35,600,0.949,bicubic,-43.693,-34.376,+2
tf_efficientnet_b5.ap_in1k,41.414,58.586,62.082,37.918,30.39,456,0.934,bicubic,-42.844,-34.892,+77
regnety_120.sw_in12k_ft_in1k,41.331,58.669,63.187,36.813,51.82,288,1.000,bicubic,-44.069,-34.395,-22
dm_nfnet_f3.dm_in1k,41.323,58.677,62.110,37.890,254.92,416,0.940,bicubic,-44.363,-35.460,-43
resnetv2_152x4_bit.goog_in21k_ft_in1k,41.308,58.692,64.312,35.688,936.53,480,1.000,bilinear,-43.608,-33.126,+16
dm_nfnet_f5.dm_in1k,41.290,58.710,62.011,37.989,377.21,544,0.954,bicubic,-44.808,-35.677,-74
caformer_s18.sail_in22k_ft_in1k,41.213,58.787,63.837,36.163,26.34,224,1.000,bicubic,-42.863,-33.361,+88
dm_nfnet_f6.dm_in1k,41.166,58.834,62.843,37.157,438.36,576,0.956,bicubic,-45.196,-35.053,-91
convnext_tiny.in12k_ft_in1k_384,41.113,58.887,62.825,37.175,28.59,384,1.000,bicubic,-44.009,-34.781,-7
tf_efficientnet_b6.ap_in1k,41.089,58.911,62.359,37.641,43.04,528,0.942,bicubic,-43.697,-34.777,+21
xcit_large_24_p16_384.fb_dist_in1k,41.036,58.964,61.237,38.763,189.10,384,1.000,bicubic,-44.718,-36.301,-55
xcit_large_24_p16_224.fb_dist_in1k,40.942,59.058,61.324,38.676,189.10,224,1.000,bicubic,-43.974,-35.806,+10
tf_efficientnetv2_l.in1k,40.930,59.070,62.011,37.989,118.52,480,1.000,bicubic,-44.734,-35.463,-50
tf_efficientnetv2_s.in21k_ft_in1k,40.924,59.076,63.849,36.151,21.46,384,1.000,bicubic,-43.364,-33.403,+62
maxvit_small_tf_384.in1k,40.846,59.154,61.972,38.028,69.02,384,1.000,bicubic,-44.694,-35.490,-47
edgenext_base.in21k_ft_in1k,40.836,59.164,61.774,38.226,18.51,320,1.000,bicubic,-43.218,-35.422,+83
maxvit_base_tf_224.in1k,40.787,59.213,61.202,38.798,119.47,224,0.950,bicubic,-44.073,-35.786,+9
xcit_medium_24_p8_224.fb_dist_in1k,40.498,59.502,60.502,39.498,84.32,224,1.000,bicubic,-44.576,-36.772,-8
vit_small_r26_s32_384.augreg_in21k_ft_in1k,40.474,59.526,62.735,37.265,36.47,384,1.000,bicubic,-43.574,-34.593,+82
tf_efficientnet_b4.ap_in1k,40.472,59.528,61.717,38.283,19.34,380,0.922,bicubic,-42.778,-34.677,+155
deit3_base_patch16_224.fb_in1k,40.374,59.626,60.168,39.832,86.59,224,0.900,bicubic,-43.412,-36.420,+103
convformer_s18.sail_in22k_ft_in1k,40.301,59.699,61.719,38.281,26.77,224,1.000,bicubic,-43.437,-35.329,+110
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,40.276,59.724,61.668,38.332,38.86,256,0.950,bicubic,-44.170,-35.176,+35
flexivit_large.600ep_in1k,40.272,59.728,60.367,39.633,304.36,240,0.950,bicubic,-45.266,-37.121,-55
vit_base_patch16_224_miil.in21k_ft_in1k,40.162,59.838,60.889,39.111,86.54,224,0.875,bilinear,-44.104,-35.915,+53
deit3_small_patch16_224.fb_in22k_ft_in1k,40.152,59.848,61.860,38.140,22.06,224,1.000,bicubic,-42.922,-34.916,+167
regnetz_e8.ra3_in1k,40.136,59.864,61.316,38.684,57.70,320,1.000,bicubic,-44.900,-35.956,-14
maxvit_rmlp_small_rw_224.sw_in1k,40.109,59.891,59.516,40.484,64.90,224,0.900,bicubic,-44.383,-37.494,+25
flexivit_large.1200ep_in1k,40.097,59.903,60.646,39.354,304.36,240,0.950,bicubic,-45.549,-36.896,-65
xcit_medium_24_p8_384.fb_dist_in1k,40.050,59.950,60.451,39.549,84.32,384,1.000,bicubic,-45.766,-37.141,-80
flexivit_large.300ep_in1k,39.993,60.007,59.986,40.014,304.36,240,0.950,bicubic,-45.297,-37.415,-43
maxvit_tiny_tf_384.in1k,39.971,60.029,60.909,39.091,30.98,384,1.000,bicubic,-45.129,-36.469,-27
convnextv2_tiny.fcmae_ft_in22k_in1k,39.938,60.062,61.835,38.165,28.64,288,1.000,bicubic,-44.478,-35.425,+34
dm_nfnet_f4.dm_in1k,39.924,60.076,60.447,39.553,316.07,512,0.951,bicubic,-45.912,-37.217,-86
xcit_medium_24_p16_384.fb_dist_in1k,39.897,60.103,60.097,39.903,84.40,384,1.000,bicubic,-45.525,-37.309,-59
convnext_tiny.fb_in22k_ft_in1k_384,39.787,60.213,61.536,38.464,28.59,384,1.000,bicubic,-44.301,-35.608,+58
cait_s36_384.fb_dist_in1k,39.767,60.233,60.469,39.531,68.37,384,1.000,bicubic,-45.687,-37.009,-63
convnextv2_base.fcmae_ft_in1k,39.755,60.245,59.875,40.125,88.72,288,1.000,bicubic,-45.719,-37.509,-66
volo_d3_448.sail_in1k,39.702,60.298,59.760,40.240,86.63,448,1.000,bicubic,-46.798,-37.950,-131
efficientnetv2_rw_m.agc_in1k,39.681,60.319,59.679,40.321,53.24,416,1.000,bicubic,-45.131,-37.471,-10
xception65.ra3_in1k,39.621,60.379,60.919,39.081,39.92,299,0.940,bicubic,-43.561,-35.673,+138
ecaresnet269d.ra2_in1k,39.604,60.396,60.345,39.655,102.09,352,1.000,bicubic,-45.362,-36.877,-24
tf_efficientnet_b3.ns_jft_in1k,39.588,60.412,61.475,38.525,12.23,300,0.904,bicubic,-44.468,-35.443,+55
caformer_m36.sail_in1k,39.572,60.428,58.691,41.309,56.20,224,1.000,bicubic,-45.660,-38.510,-51
convformer_b36.sail_in1k,39.525,60.475,58.081,41.919,99.88,224,1.000,bicubic,-45.293,-38.865,-16
caformer_s36.sail_in1k,39.519,60.481,59.760,40.240,39.30,224,1.000,bicubic,-44.989,-37.236,+4
volo_d3_224.sail_in1k,39.482,60.518,59.860,40.140,86.33,224,0.960,bicubic,-45.932,-37.416,-68
convnext_large.fb_in1k,39.460,60.540,59.188,40.812,197.77,288,1.000,bicubic,-45.386,-38.026,-21
deit3_base_patch16_384.fb_in1k,39.411,60.589,58.928,41.072,86.88,384,1.000,bicubic,-45.661,-38.324,-38
xcit_small_24_p8_224.fb_dist_in1k,39.327,60.673,59.402,40.598,47.63,224,1.000,bicubic,-45.541,-37.788,-25
xcit_medium_24_p16_224.fb_dist_in1k,39.272,60.728,59.463,40.537,84.40,224,1.000,bicubic,-45.014,-37.469,+26
convformer_m36.sail_in1k,39.236,60.764,57.629,42.371,57.05,224,1.000,bicubic,-45.258,-39.237,-1
coat_lite_medium_384.in1k,39.175,60.825,59.280,40.720,44.57,384,1.000,bicubic,-45.703,-38.092,-29
efficientnet_b4.ra2_in1k,39.091,60.909,59.618,40.382,19.34,384,1.000,bicubic,-44.323,-36.980,+105
hrnet_w18_ssld.paddle_in1k,39.067,60.933,60.650,39.350,21.30,288,1.000,bilinear,-42.979,-35.600,+255
tresnet_v2_l.miil_in21k_ft_in1k,39.016,60.984,59.480,40.520,46.17,224,0.875,bilinear,-44.884,-37.014,+55
xcit_small_24_p8_384.fb_dist_in1k,39.010,60.990,59.166,40.834,47.63,384,1.000,bicubic,-46.544,-38.404,-90
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,38.975,61.025,62.434,37.566,236.34,384,1.000,bicubic,-44.859,-34.692,+60
convformer_b36.sail_in1k_384,38.930,61.070,58.413,41.587,99.88,384,1.000,bicubic,-46.812,-39.111,-102
convnext_tiny.in12k_ft_in1k,38.912,61.088,59.862,40.138,28.59,288,1.000,bicubic,-45.538,-37.478,-4
maxvit_small_tf_224.in1k,38.871,61.129,59.160,40.840,68.93,224,0.950,bicubic,-45.555,-37.664,+4
coatnet_rmlp_2_rw_224.sw_in1k,38.869,61.131,58.018,41.982,73.88,224,0.950,bicubic,-45.737,-38.722,-22
vit_base_patch32_384.augreg_in21k_ft_in1k,38.796,61.204,60.327,39.673,88.30,384,1.000,bicubic,-44.554,-36.511,+105
tf_efficientnetv2_m.in1k,38.714,61.286,59.793,40.207,54.14,480,1.000,bicubic,-46.490,-37.571,-67
eca_nfnet_l2.ra3_in1k,38.655,61.345,59.443,40.557,56.72,384,1.000,bicubic,-46.047,-37.739,-30
davit_small.msft_in1k,38.631,61.369,58.203,41.797,49.75,224,0.950,bicubic,-45.621,-38.737,+14
mvitv2_small.fb_in1k,38.578,61.422,58.130,41.870,34.87,224,0.900,bicubic,-45.192,-38.446,+59
xcit_small_12_p8_384.fb_dist_in1k,38.547,61.453,58.795,41.205,26.21,384,1.000,bicubic,-46.531,-38.487,-59
convformer_m36.sail_in1k_384,38.531,61.469,57.736,42.264,57.05,384,1.000,bicubic,-47.049,-39.806,-103
xcit_small_24_p16_384.fb_dist_in1k,38.499,61.501,58.396,41.604,47.67,384,1.000,bicubic,-46.591,-38.916,-62
davit_base.msft_in1k,38.490,61.510,57.535,42.465,87.95,224,0.950,bicubic,-46.152,-39.485,-33
mvitv2_base.fb_in1k,38.458,61.542,57.934,42.066,51.47,224,0.900,bicubic,-45.992,-38.924,-15
rexnetr_300.sw_in12k_ft_in1k,38.431,61.569,60.610,39.390,34.81,288,1.000,bicubic,-46.113,-36.646,-28
convformer_s36.sail_in1k,38.405,61.595,57.710,42.290,40.01,224,1.000,bicubic,-45.655,-39.036,+23
xcit_small_12_p8_224.fb_dist_in1k,38.360,61.640,58.824,41.176,26.21,224,1.000,bicubic,-45.876,-38.048,+8
tf_efficientnet_b5.ra_in1k,38.331,61.669,59.928,40.072,30.39,456,0.934,bicubic,-45.480,-36.826,+45
deit_base_distilled_patch16_384.fb_in1k,38.246,61.754,57.783,42.217,87.63,384,1.000,bicubic,-47.180,-39.547,-102
xcit_large_24_p8_224.fb_in1k,38.106,61.894,57.873,42.127,188.93,224,1.000,bicubic,-46.288,-38.791,-6
vit_base_patch16_384.orig_in21k_ft_in1k,38.105,61.895,60.418,39.582,86.86,384,1.000,bicubic,-46.096,-36.802,+7
resnetv2_152x2_bit.goog_in21k_ft_in1k,38.000,62.000,61.137,38.863,236.34,448,1.000,bilinear,-46.510,-35.855,-32
pvt_v2_b4.in1k,37.949,62.051,58.217,41.783,62.56,224,0.900,bicubic,-45.761,-38.457,+54
coat_lite_medium.in1k,37.880,62.120,57.796,42.204,44.57,224,0.900,bicubic,-45.719,-38.932,+63
cait_s24_384.fb_dist_in1k,37.879,62.121,58.069,41.931,47.06,384,1.000,bicubic,-47.169,-39.277,-71
convnextv2_nano.fcmae_ft_in22k_in1k_384,37.875,62.125,59.443,40.557,15.62,384,1.000,bicubic,-45.499,-37.301,+82
resnet152d.ra2_in1k,37.851,62.149,58.362,41.638,60.21,320,1.000,bicubic,-45.833,-38.378,+55
convformer_s36.sail_in1k_384,37.812,62.188,57.488,42.512,40.01,384,1.000,bicubic,-47.566,-39.988,-104
resnetrs420.tf_in1k,37.745,62.255,58.207,41.793,191.89,416,1.000,bicubic,-47.261,-38.917,-72
deit3_medium_patch16_224.fb_in1k,37.709,62.291,57.085,42.915,38.85,224,0.900,bicubic,-45.377,-39.211,+104
xcit_small_24_p16_224.fb_dist_in1k,37.702,62.298,57.372,42.628,47.67,224,1.000,bicubic,-46.172,-39.364,+24
resnetrs350.tf_in1k,37.666,62.334,58.097,41.903,163.96,384,1.000,bicubic,-47.048,-38.895,-57
caformer_s18.sail_in1k_384,37.664,62.336,57.612,42.388,26.34,384,1.000,bicubic,-47.362,-39.746,-77
regnety_640.seer_ft_in1k,37.584,62.416,59.868,40.132,281.38,384,1.000,bicubic,-46.324,-37.054,+17
xcit_small_12_p16_384.fb_dist_in1k,37.582,62.418,57.761,42.239,26.25,384,1.000,bicubic,-47.130,-39.357,-59
pvt_v2_b5.in1k,37.544,62.456,57.303,42.697,81.96,224,0.900,bicubic,-46.198,-39.331,+37
resnet200d.ra2_in1k,37.503,62.497,58.301,41.699,64.69,320,1.000,bicubic,-46.461,-38.523,+10
maxvit_rmlp_tiny_rw_256.sw_in1k,37.381,62.619,57.193,42.807,29.15,256,0.950,bicubic,-46.843,-39.675,-11
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,37.344,62.656,59.402,40.598,236.34,224,0.875,bicubic,-45.532,-37.180,+112
regnety_1280.seer_ft_in1k,37.330,62.670,59.133,40.867,644.81,384,1.000,bicubic,-47.102,-37.959,-36
resnest269e.in1k,37.311,62.689,57.490,42.510,110.93,416,0.928,bicubic,-47.199,-39.944,-50
convnext_base.fb_in1k,37.307,62.693,57.319,42.681,88.59,288,1.000,bicubic,-47.121,-39.649,-37
resmlp_big_24_224.fb_in22k_ft_in1k,37.242,62.758,58.191,41.809,129.14,224,0.875,bicubic,-47.156,-38.921,-30
vit_small_r26_s32_224.augreg_in21k_ft_in1k,37.230,62.770,59.072,40.928,36.43,224,0.900,bicubic,-44.634,-36.950,+224
pit_b_distilled_224.in1k,37.197,62.803,56.507,43.493,74.79,224,0.900,bicubic,-46.569,-39.959,+24
cait_s24_224.fb_dist_in1k,37.153,62.847,56.724,43.276,46.92,224,1.000,bicubic,-46.289,-39.850,+52
dm_nfnet_f2.dm_in1k,37.128,62.872,56.983,43.017,193.78,352,0.920,bicubic,-48.066,-40.363,-106
resnetaa101d.sw_in12k_ft_in1k,37.116,62.884,57.851,42.149,44.57,288,1.000,bicubic,-47.008,-39.255,-14
efficientformer_l7.snap_dist_in1k,37.112,62.888,56.900,43.100,82.23,224,0.950,bicubic,-46.270,-39.636,+58
pvt_v2_b3.in1k,37.110,62.890,57.340,42.660,45.24,224,0.900,bicubic,-46.010,-39.214,+81
vit_base_patch32_224.augreg_in21k_ft_in1k,37.073,62.927,59.307,40.693,88.22,224,0.900,bicubic,-43.643,-36.259,+327
volo_d1_384.sail_in1k,37.069,62.931,57.138,42.862,26.78,384,1.000,bicubic,-48.175,-40.056,-119
tf_efficientnet_b3.ap_in1k,37.053,62.947,57.236,42.764,12.23,300,0.904,bicubic,-44.773,-38.390,+216
efficientnetv2_rw_s.ra2_in1k,37.053,62.947,56.816,43.184,23.94,384,1.000,bicubic,-46.755,-39.916,+11
maxvit_tiny_tf_224.in1k,37.020,62.980,56.904,43.096,30.92,224,0.950,bicubic,-46.382,-39.686,+47
swinv2_base_window16_256.ms_in1k,36.990,63.010,56.140,43.860,87.92,256,0.900,bicubic,-47.610,-40.950,-73
xcit_small_12_p16_224.fb_dist_in1k,36.975,63.025,56.725,43.275,26.25,224,1.000,bicubic,-46.353,-39.691,+57
regnetz_040_h.ra3_in1k,36.973,63.027,57.276,42.724,28.94,320,1.000,bicubic,-47.519,-39.482,-64
volo_d1_224.sail_in1k,36.884,63.116,56.633,43.367,26.63,224,0.960,bicubic,-47.278,-40.143,-27
seresnet152d.ra2_in1k,36.784,63.216,56.727,43.273,66.84,320,1.000,bicubic,-47.576,-40.313,-45
efficientformerv2_l.snap_dist_in1k,36.762,63.238,56.635,43.365,26.32,224,0.950,bicubic,-46.870,-39.925,+24
maxxvit_rmlp_small_rw_256.sw_in1k,36.707,63.293,56.012,43.988,66.01,256,0.950,bicubic,-47.917,-41.056,-81
seresnext101d_32x8d.ah_in1k,36.633,63.367,56.325,43.675,93.59,288,1.000,bicubic,-47.725,-40.596,-47
volo_d2_224.sail_in1k,36.627,63.373,56.466,43.534,58.68,224,0.960,bicubic,-48.575,-40.724,-124
caformer_s18.sail_in1k,36.576,63.424,55.827,44.173,26.34,224,1.000,bicubic,-47.080,-40.689,+19
xception65p.ra3_in1k,36.564,63.436,56.435,43.565,39.82,299,0.940,bicubic,-46.572,-39.673,+60
seresnextaa101d_32x8d.ah_in1k,36.532,63.468,56.409,43.591,93.59,288,1.000,bicubic,-48.034,-40.667,-83
focalnet_base_srf.ms_in1k,36.460,63.540,56.217,43.783,88.15,224,0.900,bicubic,-47.358,-40.465,-6
regnetz_d32.ra3_in1k,36.448,63.552,57.368,42.632,27.58,320,0.950,bicubic,-47.574,-39.500,-25
cait_xs24_384.fb_dist_in1k,36.416,63.584,56.944,43.056,26.67,384,1.000,bicubic,-47.645,-39.941,-32
efficientnet_b3.ra2_in1k,36.409,63.591,56.836,43.164,12.23,320,1.000,bicubic,-45.833,-39.282,+158
deit_base_distilled_patch16_224.fb_in1k,36.407,63.593,56.613,43.387,87.34,224,0.900,bicubic,-46.979,-39.875,+33
volo_d2_384.sail_in1k,36.407,63.593,56.323,43.677,58.87,384,1.000,bicubic,-49.635,-41.251,-194
resnetv2_101x3_bit.goog_in21k_ft_in1k,36.383,63.617,59.068,40.932,387.93,448,1.000,bilinear,-48.055,-38.314,-71
gcvit_base.in1k,36.381,63.619,55.880,44.120,90.32,224,0.875,bicubic,-48.065,-41.328,-74
dm_nfnet_f1.dm_in1k,36.326,63.674,55.747,44.253,132.63,320,0.910,bicubic,-48.376,-41.519,-98
resnetrs270.tf_in1k,36.308,63.692,56.564,43.436,129.86,352,1.000,bicubic,-48.120,-40.404,-71
tresnet_m.miil_in21k_ft_in1k,36.281,63.719,55.784,44.216,31.39,224,0.875,bilinear,-46.793,-40.330,+58
mixer_b16_224.miil_in21k_ft_in1k,36.267,63.733,55.965,44.035,59.88,224,0.875,bilinear,-46.039,-39.755,+144
convnext_small.fb_in1k,36.248,63.752,55.912,44.088,50.22,288,1.000,bicubic,-47.453,-40.896,-1
convformer_s18.sail_in1k_384,36.204,63.796,56.059,43.941,26.77,384,1.000,bicubic,-48.198,-41.053,-69
deit3_small_patch16_384.fb_in1k,36.191,63.809,55.558,44.442,22.21,384,1.000,bicubic,-47.240,-41.116,+17
tf_efficientnet_b2.ns_jft_in1k,36.167,63.833,57.559,42.441,9.11,260,0.890,bicubic,-46.211,-38.695,+126
mvitv2_tiny.fb_in1k,36.167,63.833,55.132,44.868,24.17,224,0.900,bicubic,-46.243,-41.020,+122
focalnet_base_lrf.ms_in1k,36.118,63.882,55.810,44.190,88.75,224,0.900,bicubic,-47.720,-40.798,-26
regnety_320.seer_ft_in1k,36.067,63.933,58.480,41.520,145.05,384,1.000,bicubic,-47.267,-38.226,+26
regnetz_040.ra3_in1k,36.053,63.947,55.735,44.265,27.12,320,1.000,bicubic,-48.187,-41.197,-63
resnest200e.in1k,35.929,64.071,55.853,44.147,70.20,320,0.909,bicubic,-47.917,-41.031,-31
resnet18.fb_swsl_ig1b_ft_in1k,35.866,64.134,58.461,41.539,11.69,224,0.875,bilinear,-37.416,-33.271,+615
sequencer2d_l.in1k,35.831,64.169,55.715,44.285,54.30,224,0.875,bicubic,-47.563,-40.781,+14
eca_nfnet_l1.ra2_in1k,35.815,64.185,55.955,44.045,41.41,320,1.000,bicubic,-48.195,-41.071,-45
gcvit_small.in1k,35.760,64.240,54.794,45.206,51.09,224,0.875,bicubic,-48.132,-41.864,-38
vit_base_patch16_224.orig_in21k_ft_in1k,35.758,64.242,57.401,42.599,86.57,224,0.900,bicubic,-46.028,-38.723,+181
vit_relpos_medium_patch16_cls_224.sw_in1k,35.725,64.275,54.919,45.081,38.76,224,0.900,bicubic,-46.847,-41.148,+96
xcit_small_24_p8_224.fb_in1k,35.554,64.446,54.780,45.220,47.63,224,1.000,bicubic,-48.280,-41.852,-34
xcit_large_24_p16_224.fb_in1k,35.528,64.472,54.743,45.257,189.10,224,1.000,bicubic,-47.372,-41.143,+53
coat_small.in1k,35.520,64.480,55.157,44.843,21.69,224,0.900,bicubic,-46.842,-41.051,+114
xcit_small_12_p8_224.fb_in1k,35.519,64.481,55.507,44.493,26.21,224,1.000,bicubic,-47.813,-40.977,+15
flexivit_base.1200ep_in1k,35.517,64.484,53.843,46.157,86.59,240,0.950,bicubic,-49.161,-43.153,-120
vit_small_patch16_384.augreg_in21k_ft_in1k,35.467,64.533,57.535,42.465,22.20,384,1.000,bicubic,-48.335,-39.563,-35
regnetz_d8_evos.ch_in1k,35.458,64.542,55.749,44.251,23.46,320,1.000,bicubic,-48.668,-41.263,-68
xcit_medium_24_p8_224.fb_in1k,35.452,64.548,54.823,45.177,84.32,224,1.000,bicubic,-48.296,-41.577,-31
swinv2_base_window8_256.ms_in1k,35.450,64.550,54.607,45.393,87.92,256,0.900,bicubic,-48.800,-42.317,-80
swinv2_small_window16_256.ms_in1k,35.428,64.572,54.623,45.377,49.73,256,0.900,bicubic,-48.796,-42.155,-78
dm_nfnet_f0.dm_in1k,35.407,64.594,55.527,44.473,71.49,256,0.900,bicubic,-48.080,-41.041,-10
resnest101e.in1k,35.377,64.623,55.784,44.216,48.28,256,0.875,bilinear,-47.507,-40.538,+44
resnet152.a1h_in1k,35.357,64.643,54.627,45.373,60.19,288,1.000,bicubic,-48.093,-41.911,-10
tf_efficientnet_b5.aa_in1k,35.320,64.680,56.036,43.964,30.39,456,0.934,bicubic,-48.368,-40.676,-27
convit_base.fb_in1k,35.302,64.698,54.939,45.061,86.54,224,0.875,bicubic,-46.988,-40.997,+115
focalnet_small_lrf.ms_in1k,35.275,64.725,54.888,45.112,50.34,224,0.900,bicubic,-48.219,-41.692,-16
efficientformer_l3.snap_dist_in1k,35.245,64.755,54.507,45.493,31.41,224,0.950,bicubic,-47.305,-41.741,+83
xcit_tiny_24_p8_224.fb_dist_in1k,35.241,64.759,55.267,44.733,12.11,224,1.000,bicubic,-47.325,-40.791,+80
edgenext_base.usi_in1k,35.216,64.784,55.126,44.874,18.51,320,1.000,bicubic,-48.742,-41.644,-65
convformer_s18.sail_in1k,35.208,64.792,54.629,45.371,26.77,224,1.000,bicubic,-47.778,-41.621,+30
flexivit_base.600ep_in1k,35.137,64.863,53.650,46.350,86.59,240,0.950,bicubic,-49.385,-43.286,-126
twins_svt_large.in1k,35.082,64.918,54.717,45.283,99.27,224,0.900,bicubic,-48.596,-41.873,-33
repvgg_b3.rvgg_in1k,35.055,64.945,54.554,45.446,123.09,224,0.875,bilinear,-45.451,-40.700,+277
repvgg_b3g4.rvgg_in1k,35.049,64.951,54.788,45.212,83.83,224,0.875,bilinear,-45.167,-40.304,+307
convnextv2_tiny.fcmae_ft_in1k,35.045,64.955,54.224,45.776,28.64,288,1.000,bicubic,-48.419,-42.494,-23
regnetz_d8.ra3_in1k,35.011,64.989,55.935,44.065,23.37,320,1.000,bicubic,-49.043,-41.060,-78
xcit_tiny_24_p8_384.fb_dist_in1k,34.915,65.085,55.148,44.852,12.11,384,1.000,bicubic,-48.831,-41.562,-49
resnet101d.ra2_in1k,34.876,65.124,54.210,45.790,44.57,320,1.000,bicubic,-48.140,-42.242,+19
rexnetr_200.sw_in12k_ft_in1k,34.864,65.136,55.853,44.147,16.52,288,1.000,bicubic,-48.272,-40.783,+2
coatnet_1_rw_224.sw_in1k,34.854,65.146,53.442,46.558,41.72,224,0.950,bicubic,-48.742,-42.940,-36
coatnet_rmlp_1_rw_224.sw_in1k,34.805,65.195,53.957,46.043,41.69,224,0.950,bicubic,-48.557,-42.493,-16
swin_s3_base_224.ms_in1k,34.803,65.197,53.703,46.297,71.13,224,0.900,bicubic,-49.117,-42.969,-76
flexivit_base.300ep_in1k,34.797,65.203,53.165,46.835,86.59,240,0.950,bicubic,-49.609,-43.721,-116
seresnext101_32x8d.ah_in1k,34.789,65.211,53.452,46.548,93.57,288,1.000,bicubic,-49.397,-43.422,-98
resmlp_big_24_224.fb_distilled_in1k,34.788,65.213,54.642,45.358,129.14,224,0.875,bicubic,-48.804,-42.008,-40
maxvit_tiny_rw_224.sw_in1k,34.780,65.220,53.351,46.649,29.06,224,0.950,bicubic,-48.724,-43.153,-38
vit_relpos_base_patch16_clsgap_224.sw_in1k,34.725,65.275,54.214,45.786,86.43,224,0.900,bicubic,-48.035,-41.958,+30
vit_base_patch16_rpn_224.sw_in1k,34.717,65.283,54.658,45.342,86.54,224,0.900,bicubic,-47.489,-41.340,+103
sequencer2d_m.in1k,34.703,65.297,53.996,46.004,38.31,224,0.875,bicubic,-48.109,-42.278,+22
deit3_small_patch16_224.fb_in1k,34.689,65.311,53.167,46.833,22.06,224,0.900,bicubic,-46.681,-42.289,+180
vit_large_patch32_384.orig_in21k_ft_in1k,34.673,65.326,55.731,44.269,306.63,384,1.000,bicubic,-46.837,-40.359,+162
davit_tiny.msft_in1k,34.673,65.326,54.342,45.658,28.36,224,0.950,bicubic,-48.023,-41.932,+34
focalnet_small_srf.ms_in1k,34.670,65.330,54.420,45.580,49.89,224,0.900,bicubic,-48.746,-42.018,-37
ecaresnet101d.miil_in1k,34.644,65.356,54.499,45.501,44.57,288,0.950,bicubic,-48.340,-42.043,+7
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,34.615,65.385,55.937,44.063,194.03,224,0.875,bilinear,-47.227,-40.155,+130
vit_relpos_base_patch16_224.sw_in1k,34.607,65.393,54.291,45.709,86.43,224,0.900,bicubic,-47.889,-41.847,+59
repvgg_b2g4.rvgg_in1k,34.593,65.407,54.768,45.232,61.76,224,0.875,bilinear,-44.789,-39.908,+338
gcvit_tiny.in1k,34.554,65.446,53.249,46.751,28.22,224,0.875,bicubic,-48.830,-43.149,-36
resnetrs200.tf_in1k,34.502,65.498,54.281,45.719,93.21,320,1.000,bicubic,-49.942,-42.801,-141
poolformerv2_m48.sail_in1k,34.485,65.515,54.027,45.973,73.35,224,1.000,bicubic,-48.135,-42.045,+34
convnextv2_nano.fcmae_ft_in22k_in1k,34.379,65.621,55.014,44.986,15.62,288,1.000,bicubic,-48.285,-41.506,+26
resnest50d_4s2x40d.in1k,34.369,65.631,54.725,45.275,30.42,224,0.875,bicubic,-46.751,-40.835,+191
resnetrs152.tf_in1k,34.351,65.649,53.564,46.436,86.62,320,1.000,bicubic,-49.351,-43.048,-69
pvt_v2_b2_li.in1k,34.318,65.682,54.104,45.896,22.55,224,0.900,bicubic,-47.876,-41.988,+90
crossvit_18_dagger_408.in1k,34.245,65.755,53.106,46.894,44.61,408,1.000,bicubic,-49.953,-43.710,-120
xcit_medium_24_p16_224.fb_in1k,34.241,65.759,53.157,46.843,84.40,224,1.000,bicubic,-48.399,-42.827,+24
efficientnetv2_rw_t.ra2_in1k,34.163,65.837,53.127,46.873,13.65,288,1.000,bicubic,-48.187,-43.065,+64
pit_s_distilled_224.in1k,34.159,65.841,53.363,46.637,24.04,224,0.900,bicubic,-47.649,-42.365,+120
tf_efficientnet_b1.ns_jft_in1k,34.157,65.843,55.505,44.495,7.79,240,0.882,bicubic,-47.237,-40.231,+158
twins_pcpvt_large.in1k,34.117,65.883,54.133,45.867,60.99,224,0.900,bicubic,-49.013,-42.469,-26
tf_efficientnet_b4.aa_in1k,34.060,65.940,54.212,45.788,19.34,380,0.922,bicubic,-48.958,-42.088,-16
efficientformerv2_s2.snap_dist_in1k,34.060,65.940,53.467,46.533,12.71,224,0.950,bicubic,-48.106,-42.443,+86
resnetv2_101.a1h_in1k,34.051,65.949,52.316,47.684,44.54,288,1.000,bicubic,-48.951,-44.138,-15
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,34.033,65.967,55.596,44.404,88.79,224,0.875,bilinear,-47.573,-40.445,+128
tf_efficientnet_b6.aa_in1k,34.005,65.995,54.540,45.460,43.04,528,0.942,bicubic,-50.111,-42.340,-124
xcit_small_24_p16_224.fb_in1k,34.005,65.995,53.281,46.719,47.67,224,1.000,bicubic,-48.575,-42.731,+28
nfnet_l0.ra2_in1k,33.998,66.002,54.373,45.627,35.07,288,1.000,bicubic,-48.754,-42.143,+1
efficientnet_b3_pruned.in1k,33.994,66.006,54.110,45.890,9.86,300,0.904,bicubic,-46.858,-41.134,+205
regnety_160.deit_in1k,33.982,66.018,53.554,46.446,83.59,288,1.000,bicubic,-49.708,-43.228,-83
resnext101_64x4d.tv_in1k,33.966,66.034,52.530,47.470,83.46,224,0.875,bilinear,-49.026,-43.714,-21
gc_efficientnetv2_rw_t.agc_in1k,33.960,66.040,53.230,46.770,13.68,288,1.000,bicubic,-48.492,-43.062,+38
swinv2_cr_small_ns_224.sw_in1k,33.846,66.154,52.640,47.360,49.70,224,0.900,bicubic,-49.652,-43.844,-73
resnext101_64x4d.c1_in1k,33.842,66.158,52.161,47.839,83.46,288,1.000,bicubic,-49.314,-44.213,-43
poolformerv2_s36.sail_in1k,33.827,66.173,53.685,46.315,30.79,224,1.000,bicubic,-47.741,-42.005,+121
resnet101.a1h_in1k,33.777,66.223,53.102,46.898,44.55,288,1.000,bicubic,-49.001,-43.208,-10
xcit_small_12_p16_224.fb_in1k,33.756,66.244,53.228,46.772,26.25,224,1.000,bicubic,-48.214,-42.560,+89
swin_s3_small_224.ms_in1k,33.697,66.303,52.365,47.635,49.74,224,0.900,bicubic,-50.059,-44.087,-101
resnetv2_50x3_bit.goog_in21k_ft_in1k,33.671,66.329,55.888,44.112,217.32,448,1.000,bilinear,-50.349,-41.238,-127
swinv2_small_window8_256.ms_in1k,33.634,66.366,52.827,47.173,49.73,256,0.900,bicubic,-50.220,-43.817,-117
resnet152.tv2_in1k,33.620,66.380,51.656,48.344,60.19,224,0.965,bilinear,-48.668,-44.348,+51
resnet51q.ra2_in1k,33.557,66.443,53.027,46.973,35.70,288,1.000,bilinear,-48.803,-43.155,+38
xcit_tiny_24_p16_384.fb_dist_in1k,33.508,66.492,52.768,47.232,12.12,384,1.000,bicubic,-49.062,-43.508,+15
vit_relpos_medium_patch16_224.sw_in1k,33.498,66.502,52.620,47.380,38.75,224,0.900,bicubic,-48.964,-43.462,+25
cs3edgenet_x.c2_in1k,33.457,66.543,52.925,47.075,47.82,288,1.000,bicubic,-49.251,-43.445,-12
regnety_080.ra3_in1k,33.453,66.547,52.939,47.061,39.18,288,1.000,bicubic,-50.473,-43.951,-130
convmixer_1536_20.in1k,33.432,66.568,53.029,46.971,51.63,224,0.960,bicubic,-47.930,-42.585,+135
sequencer2d_s.in1k,33.430,66.570,52.383,47.617,27.65,224,0.875,bicubic,-48.910,-43.647,+36
regnety_032.ra_in1k,33.416,66.584,52.768,47.232,19.44,288,1.000,bicubic,-49.312,-43.648,-18
crossvit_18_240.in1k,33.386,66.614,52.247,47.753,43.27,240,0.875,bicubic,-49.010,-43.813,+24
vit_srelpos_medium_patch16_224.sw_in1k,33.373,66.627,52.459,47.541,38.74,224,0.900,bicubic,-48.867,-43.483,+46
gernet_l.idstcv_in1k,33.371,66.629,51.911,48.089,31.08,256,0.875,bilinear,-47.983,-43.619,+132
tf_efficientnetv2_b3.in21k_ft_in1k,33.355,66.645,54.922,45.078,14.36,300,0.900,bicubic,-49.315,-41.705,-16
regnetz_c16.ra3_in1k,33.335,66.665,54.243,45.757,13.46,320,1.000,bicubic,-49.299,-42.079,-13
crossvit_15_dagger_408.in1k,33.335,66.665,52.202,47.798,28.50,408,1.000,bicubic,-50.507,-44.576,-129
crossvit_18_dagger_240.in1k,33.282,66.718,52.196,47.804,44.27,240,0.875,bicubic,-49.234,-43.870,+8
wide_resnet101_2.tv2_in1k,33.261,66.739,51.426,48.574,126.89,224,0.965,bilinear,-49.237,-44.592,+8
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,33.257,66.743,55.295,44.705,28.29,224,0.900,bicubic,-47.711,-40.719,+162
tresnet_xl.miil_in1k,33.255,66.745,52.298,47.702,78.44,224,0.875,bilinear,-48.819,-43.628,+57
nest_base_jx.goog_in1k,33.215,66.785,51.825,48.175,67.72,224,0.875,bicubic,-50.319,-44.549,-102
convnext_tiny.fb_in1k,33.168,66.832,52.677,47.323,28.59,288,1.000,bicubic,-49.530,-43.955,-26
resnest50d_1s4x24d.in1k,33.145,66.855,52.840,47.160,25.68,224,0.875,bicubic,-47.843,-42.485,+155
convnext_nano.in12k_ft_in1k,33.119,66.881,53.988,46.012,15.59,288,1.000,bicubic,-49.743,-42.568,-44
vit_relpos_medium_patch16_rpn_224.sw_in1k,33.092,66.908,52.363,47.637,38.73,224,0.900,bicubic,-49.218,-43.609,+25
resnet61q.ra2_in1k,33.090,66.910,51.746,48.254,36.85,288,1.000,bicubic,-49.434,-44.384,-3
maxxvit_rmlp_nano_rw_256.sw_in1k,33.074,66.926,51.852,48.148,16.78,256,0.950,bicubic,-49.968,-44.498,-60
nest_small_jx.goog_in1k,33.052,66.948,51.064,48.936,38.35,224,0.875,bicubic,-50.072,-45.256,-71
rexnet_300.nav_in1k,33.048,66.952,52.369,47.631,34.71,224,0.875,bicubic,-49.726,-43.867,-41
crossvit_base_240.in1k,33.033,66.967,51.390,48.610,105.03,240,0.875,bicubic,-49.183,-44.534,+32
poolformerv2_m36.sail_in1k,33.031,66.969,51.858,48.142,56.08,224,1.000,bicubic,-49.185,-43.974,+30
twins_pcpvt_base.in1k,33.023,66.977,52.506,47.494,43.83,224,0.900,bicubic,-49.687,-43.840,-39
pvt_v2_b2.in1k,33.013,66.987,52.003,47.997,25.36,224,0.900,bicubic,-49.073,-43.953,+43
xcit_tiny_24_p16_224.fb_dist_in1k,32.999,67.001,52.074,47.926,12.12,224,1.000,bicubic,-47.455,-43.144,+198
rexnet_200.nav_in1k,32.966,67.034,52.923,47.077,16.37,224,0.875,bicubic,-48.670,-42.741,+76
resnest50d.in1k,32.966,67.034,52.705,47.295,27.48,224,0.875,bilinear,-47.998,-42.679,+147
crossvit_15_dagger_240.in1k,32.927,67.073,51.807,48.193,28.21,240,0.875,bicubic,-49.403,-44.153,+10
convit_small.fb_in1k,32.923,67.077,52.115,47.885,27.78,224,0.875,bicubic,-48.497,-43.629,+98
tf_efficientnetv2_s.in1k,32.913,67.087,51.738,48.262,21.46,384,1.000,bicubic,-50.987,-44.960,-159
swin_base_patch4_window12_384.ms_in1k,32.905,67.095,51.750,48.250,87.90,384,1.000,bicubic,-51.571,-45.142,-215
vit_small_patch16_224.augreg_in21k_ft_in1k,32.883,67.117,53.921,46.079,22.05,224,0.900,bicubic,-48.501,-42.215,+99
convnext_tiny_hnf.a2h_in1k,32.881,67.119,51.194,48.806,28.59,288,1.000,bicubic,-49.703,-44.814,-28
tf_efficientnet_b3.aa_in1k,32.862,67.138,52.962,47.038,12.23,300,0.904,bicubic,-48.782,-42.760,+67
pnasnet5large.tf_in1k,32.862,67.138,50.516,49.484,86.06,331,0.911,bicubic,-49.920,-45.524,-57
twins_svt_base.in1k,32.832,67.168,51.565,48.435,56.07,224,0.900,bicubic,-50.290,-44.851,-87
regnetv_064.ra3_in1k,32.828,67.172,52.862,47.138,30.58,288,1.000,bicubic,-50.888,-43.882,-142
convnextv2_nano.fcmae_ft_in1k,32.815,67.185,52.656,47.344,15.62,288,1.000,bicubic,-49.671,-43.570,-19
nasnetalarge.tf_in1k,32.781,67.219,50.141,49.859,88.75,331,0.911,bicubic,-49.845,-45.901,-43
gernet_m.idstcv_in1k,32.760,67.240,51.897,48.103,21.14,224,0.875,bilinear,-47.974,-43.289,+155
inception_resnet_v2.tf_in1k,32.736,67.264,50.642,49.358,55.84,299,0.897,bicubic,-47.726,-44.666,+179
resnet152d.gluon_in1k,32.726,67.274,51.080,48.920,60.21,224,0.875,bicubic,-47.750,-44.122,+176
pit_b_224.in1k,32.724,67.276,49.852,50.148,73.76,224,0.900,bicubic,-49.714,-45.864,-20
tf_efficientnet_b2.ap_in1k,32.685,67.315,52.237,47.763,9.11,260,0.890,bicubic,-47.623,-42.791,+189
swin_base_patch4_window7_224.ms_in1k,32.644,67.356,51.573,48.427,87.77,224,0.900,bicubic,-50.962,-44.879,-140
fbnetv3_g.ra2_in1k,32.624,67.376,52.888,47.112,16.62,288,0.950,bilinear,-49.412,-43.170,+26
regnety_320.tv2_in1k,32.616,67.384,50.298,49.702,145.05,224,0.965,bicubic,-50.546,-46.118,-105
tresnet_l.miil_in1k,32.565,67.435,51.139,48.861,55.99,224,0.875,bilinear,-48.915,-44.485,+72
cait_xxs36_384.fb_dist_in1k,32.553,67.447,52.221,47.779,17.37,384,1.000,bicubic,-49.651,-43.923,+6
resnext101_32x8d.tv2_in1k,32.549,67.451,50.164,49.836,88.79,224,0.965,bilinear,-50.283,-46.068,-78
regnetz_c16_evos.ch_in1k,32.539,67.461,52.919,47.081,13.49,320,0.950,bicubic,-50.093,-43.555,-56
gmlp_s16_224.ra3_in1k,32.420,67.580,51.817,48.183,19.42,224,0.875,bicubic,-47.224,-42.805,+229
inception_resnet_v2.tf_ens_adv_in1k,32.365,67.635,50.429,49.571,55.84,299,0.897,bicubic,-47.613,-44.517,+200
deit_base_patch16_224.fb_in1k,32.357,67.643,50.986,49.014,86.57,224,0.900,bicubic,-49.635,-44.751,+22
maxvit_nano_rw_256.sw_in1k,32.355,67.645,50.622,49.378,15.45,256,0.950,bicubic,-50.573,-45.598,-88
swin_small_patch4_window7_224.ms_in1k,32.347,67.653,50.903,49.097,49.61,224,0.900,bicubic,-50.861,-45.413,-118
resnet152s.gluon_in1k,32.331,67.669,50.539,49.461,60.32,224,0.875,bicubic,-48.671,-44.871,+111
xcit_tiny_24_p8_224.fb_in1k,32.292,67.708,51.895,48.105,12.11,224,1.000,bicubic,-49.600,-44.075,+27
deit_small_distilled_patch16_224.fb_in1k,32.274,67.726,52.113,47.887,22.44,224,0.900,bicubic,-48.944,-43.271,+86
poolformerv2_s24.sail_in1k,32.268,67.732,51.492,48.508,21.34,224,1.000,bicubic,-48.480,-43.818,+134
seresnext101_64x4d.gluon_in1k,32.192,67.808,50.313,49.687,88.23,224,0.875,bicubic,-48.704,-44.983,+119
regnetx_320.tv2_in1k,32.174,67.826,49.349,50.651,107.81,224,0.965,bicubic,-50.636,-46.859,-87
seresnext101_32x4d.gluon_in1k,32.121,67.879,51.243,48.757,48.96,224,0.875,bicubic,-48.771,-44.053,+118
coat_lite_small.in1k,32.115,67.885,49.928,50.072,19.84,224,0.900,bicubic,-50.199,-45.920,-24
flexivit_small.1200ep_in1k,32.091,67.909,50.327,49.673,22.06,240,0.950,bicubic,-50.429,-45.797,-50
coatnext_nano_rw_224.sw_in1k,32.070,67.930,51.017,48.983,14.70,224,0.900,bicubic,-49.872,-44.899,+16
maxxvitv2_nano_rw_256.sw_in1k,32.068,67.932,50.345,49.655,23.70,256,0.950,bicubic,-51.042,-45.979,-116
focalnet_tiny_lrf.ms_in1k,32.052,67.948,51.451,48.549,28.65,224,0.900,bicubic,-50.102,-44.497,-7
gcvit_xtiny.in1k,32.044,67.956,50.991,49.009,19.98,224,0.875,bicubic,-49.910,-44.977,+12
deit_base_patch16_384.fb_in1k,31.989,68.011,50.553,49.447,86.86,384,1.000,bicubic,-51.117,-45.815,-118
maxvit_rmlp_nano_rw_256.sw_in1k,31.976,68.025,50.618,49.382,15.50,256,0.950,bicubic,-50.978,-45.648,-106
xcit_tiny_12_p8_224.fb_dist_in1k,31.940,68.060,51.402,48.598,6.71,224,1.000,bicubic,-49.272,-44.200,+76
coatnet_bn_0_rw_224.sw_in1k,31.907,68.093,51.017,48.983,27.44,224,0.950,bicubic,-50.493,-45.169,-48
tf_efficientnet_b7.aa_in1k,31.877,68.123,51.905,48.095,66.35,600,0.949,bicubic,-52.537,-45.001,-245
levit_384.fb_dist_in1k,31.875,68.125,50.594,49.406,39.13,224,0.900,bicubic,-50.721,-45.424,-74
levit_conv_384.fb_dist_in1k,31.873,68.127,50.593,49.407,39.13,224,0.900,bicubic,-50.717,-45.656,-73
resnetrs101.tf_in1k,31.858,68.142,51.023,48.977,63.62,288,0.940,bicubic,-50.426,-44.991,-32
cs3se_edgenet_x.c2ns_in1k,31.806,68.194,50.767,49.233,50.72,320,1.000,bicubic,-51.736,-45.905,-169
vit_relpos_small_patch16_224.sw_in1k,31.781,68.219,50.620,49.380,21.98,224,0.900,bicubic,-49.681,-45.200,+43
convnext_tiny.fb_in22k_ft_in1k,31.667,68.333,51.785,48.215,28.59,288,1.000,bicubic,-47.231,-42.889,+253
poolformer_m48.sail_in1k,31.665,68.335,49.800,50.200,73.47,224,0.950,bicubic,-50.817,-46.165,-61
flexivit_small.600ep_in1k,31.643,68.357,49.384,50.616,22.06,240,0.950,bicubic,-50.719,-46.698,-51
tnt_s_patch16_224,31.634,68.366,51.153,48.847,23.76,224,0.900,bicubic,-49.902,-44.584,+29
eca_nfnet_l0.ra2_in1k,31.616,68.384,51.593,48.407,24.14,288,1.000,bicubic,-50.962,-44.901,-76
focalnet_tiny_srf.ms_in1k,31.604,68.396,50.864,49.136,28.43,224,0.900,bicubic,-50.530,-45.104,-22
resnetv2_50x1_bit.goog_distilled_in1k,31.581,68.419,51.268,48.732,25.55,224,0.875,bicubic,-51.239,-45.246,-113
coatnet_rmlp_nano_rw_224.sw_in1k,31.551,68.449,50.180,49.820,15.15,224,0.900,bicubic,-50.499,-45.700,-17
mobilevitv2_200.cvnets_in22k_ft_in1k,31.521,68.478,51.777,48.223,18.45,256,0.888,bicubic,-50.810,-44.165,-51
wide_resnet50_2.racm_in1k,31.521,68.478,50.388,49.612,68.88,288,0.950,bicubic,-50.758,-45.676,-43
xception41p.ra3_in1k,31.508,68.492,50.380,49.620,26.91,299,0.940,bicubic,-50.464,-45.416,-13
regnety_064.ra3_in1k,31.463,68.537,50.516,49.484,30.58,288,1.000,bicubic,-52.257,-46.206,-197
poolformer_m36.sail_in1k,31.447,68.553,50.015,49.985,56.17,224,0.950,bicubic,-50.655,-45.863,-26
resnet152.a1_in1k,31.433,68.567,48.653,51.347,60.19,288,1.000,bicubic,-51.299,-47.067,-111
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,31.429,68.571,52.127,47.873,44.18,224,0.875,bilinear,-49.495,-43.607,+83
flexivit_small.300ep_in1k,31.425,68.575,49.217,50.783,22.06,240,0.950,bicubic,-50.753,-46.819,-37
inception_v4.tf_in1k,31.384,68.616,49.237,50.763,42.68,299,0.875,bicubic,-48.772,-45.731,+153
rexnet_150.nav_in1k,31.376,68.624,51.284,48.716,9.73,224,0.875,bicubic,-48.948,-43.890,+135
pit_s_224.in1k,31.347,68.653,49.679,50.321,23.46,224,0.900,bicubic,-49.745,-45.891,+65
resnet101.tv2_in1k,31.347,68.653,49.679,50.321,44.55,224,0.965,bilinear,-50.543,-46.089,-13
efficientformer_l1.snap_dist_in1k,31.343,68.657,50.443,49.557,12.29,224,0.950,bicubic,-49.155,-44.545,+115
regnety_032.tv2_in1k,31.335,68.665,50.127,49.873,19.44,224,0.965,bicubic,-50.421,-45.713,-6
crossvit_15_240.in1k,31.325,68.675,50.176,49.824,27.53,240,0.875,bicubic,-50.209,-45.516,+11
swinv2_tiny_window16_256.ms_in1k,31.305,68.695,49.667,50.333,28.35,256,0.900,bicubic,-51.499,-46.569,-127
cait_xxs36_224.fb_dist_in1k,31.272,68.728,50.614,49.386,17.30,224,1.000,bicubic,-48.474,-44.260,+167
cspresnet50.ra_in1k,31.268,68.732,51.227,48.773,21.62,256,0.887,bilinear,-48.322,-43.485,+179
crossvit_small_240.in1k,31.262,68.738,50.192,49.808,26.86,240,0.875,bicubic,-49.754,-45.264,+61
vit_srelpos_small_patch16_224.sw_in1k,31.260,68.740,50.233,49.767,21.97,224,0.900,bicubic,-49.832,-45.099,+55
swinv2_cr_small_224.sw_in1k,31.254,68.746,48.745,51.255,49.70,224,0.900,bicubic,-51.882,-47.739,-162
coatnet_0_rw_224.sw_in1k,31.252,68.748,48.633,51.367,27.44,224,0.950,bicubic,-51.138,-47.203,-82
swin_s3_tiny_224.ms_in1k,31.248,68.752,49.728,50.272,28.33,224,0.900,bicubic,-50.896,-46.226,-48
cspresnext50.ra_in1k,31.227,68.773,50.871,49.129,20.57,256,0.887,bilinear,-49.329,-44.451,+98
regnetv_040.ra3_in1k,31.225,68.775,50.121,49.879,20.64,288,1.000,bicubic,-51.973,-46.537,-173
convmixer_768_32.in1k,31.219,68.781,50.928,49.072,21.11,224,0.960,bicubic,-48.949,-44.145,+133
coat_mini.in1k,31.195,68.805,49.761,50.239,10.34,224,0.900,bicubic,-50.075,-45.621,+27
xcit_tiny_12_p8_384.fb_dist_in1k,31.182,68.818,50.508,49.492,6.71,384,1.000,bicubic,-51.206,-45.712,-87
resnet101s.gluon_in1k,31.105,68.895,49.799,50.201,44.67,224,0.875,bicubic,-49.197,-45.351,+118
edgenext_small.usi_in1k,31.101,68.899,50.135,49.865,5.59,320,1.000,bicubic,-50.463,-45.577,-8
coatnet_nano_rw_224.sw_in1k,31.097,68.903,49.581,50.419,15.14,224,0.900,bicubic,-50.601,-46.066,-21
tf_efficientnet_cc_b0_8e.in1k,31.091,68.909,50.781,49.219,24.01,224,0.875,bicubic,-46.815,-42.879,+286
resmlp_36_224.fb_distilled_in1k,31.062,68.938,49.696,50.304,44.69,224,0.875,bicubic,-50.086,-45.782,+34
ecaresnet50t.ra2_in1k,31.050,68.950,50.573,49.427,25.57,320,0.950,bicubic,-51.302,-45.567,-88
resnet152c.gluon_in1k,31.017,68.984,48.932,51.068,60.21,224,0.875,bicubic,-48.893,-45.912,+134
cs3sedarknet_x.c2ns_in1k,31.013,68.987,50.131,49.869,35.40,288,1.000,bicubic,-51.645,-46.219,-132
cspdarknet53.ra_in1k,31.003,68.997,50.404,49.596,27.64,256,0.887,bilinear,-49.059,-44.676,+127
resnext101_64x4d.gluon_in1k,30.993,69.007,48.549,51.451,83.46,224,0.875,bicubic,-49.607,-46.443,+83
tresnet_m.miil_in1k,30.991,69.009,48.684,51.316,31.39,224,0.875,bilinear,-49.807,-46.172,+63
twins_svt_small.in1k,30.981,69.019,49.221,50.779,24.06,224,0.900,bicubic,-50.697,-46.439,-29
regnety_160.tv2_in1k,30.946,69.054,49.058,50.942,83.59,224,0.965,bicubic,-51.696,-47.158,-136
gcresnet50t.ra2_in1k,30.936,69.064,50.034,49.966,25.90,288,1.000,bicubic,-50.520,-45.684,-6
tf_efficientnet_cc_b1_8e.in1k,30.907,69.094,50.078,49.922,39.72,240,0.882,bicubic,-48.396,-44.296,+177
resmlp_24_224.fb_distilled_in1k,30.901,69.099,50.174,49.826,30.02,224,0.875,bicubic,-49.855,-45.050,+62
regnety_080_tv.tv2_in1k,30.893,69.107,48.724,51.276,39.38,224,0.965,bicubic,-51.697,-47.294,-130
resnext101_32x4d.gluon_in1k,30.891,69.109,48.539,51.461,44.18,224,0.875,bicubic,-49.449,-46.391,+97
tf_efficientnetv2_b3.in1k,30.855,69.145,49.814,50.186,14.36,300,0.904,bicubic,-51.115,-45.998,-55
tf_efficientnet_lite4.in1k,30.834,69.166,50.392,49.608,13.01,380,0.920,bilinear,-50.698,-45.272,-21
resnetaa50d.sw_in12k_ft_in1k,30.802,69.198,50.569,49.431,25.58,288,1.000,bicubic,-51.798,-45.929,-136
nf_resnet50.ra2_in1k,30.700,69.300,49.948,50.052,25.56,288,0.940,bicubic,-49.940,-45.384,+67
poolformer_s36.sail_in1k,30.692,69.308,49.447,50.553,30.86,224,0.900,bicubic,-50.738,-45.997,-13
xcit_tiny_24_p16_224.fb_in1k,30.671,69.329,50.416,49.584,12.12,224,1.000,bicubic,-48.777,-44.462,+155
resnet50.a1h_in1k,30.635,69.365,49.421,50.579,25.56,224,1.000,bicubic,-50.041,-45.885,+60
dpn107.mx_in1k,30.625,69.374,48.739,51.261,86.92,224,0.875,bicubic,-49.543,-46.203,+106
tresnet_xl.miil_in1k_448,30.622,69.379,49.074,50.926,78.44,448,0.875,bilinear,-52.439,-47.101,-186
resnet152.gluon_in1k,30.614,69.386,48.513,51.487,60.19,224,0.875,bicubic,-49.080,-46.223,+132
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,30.602,69.398,50.667,49.333,25.03,224,0.875,bilinear,-49.736,-44.731,+87
haloregnetz_b.ra3_in1k,30.592,69.408,48.993,51.007,11.68,224,0.940,bicubic,-50.456,-46.207,+20
pit_xs_distilled_224.in1k,30.543,69.457,50.184,49.816,11.00,224,0.900,bicubic,-48.637,-44.178,+171
regnetx_080.tv2_in1k,30.523,69.477,48.032,51.968,39.57,224,0.965,bicubic,-51.015,-47.510,-36
resnet101d.gluon_in1k,30.506,69.494,47.973,52.027,44.57,224,0.875,bicubic,-49.920,-47.051,+76
resnest26d.gluon_in1k,30.494,69.506,50.663,49.337,17.07,224,0.875,bilinear,-47.990,-43.631,+213
mobilevitv2_200.cvnets_in22k_ft_in1k_384,30.490,69.510,50.575,49.425,18.45,384,1.000,bicubic,-52.910,-46.007,-228
efficientformerv2_s1.snap_dist_in1k,30.451,69.549,49.588,50.412,6.19,224,0.950,bicubic,-49.237,-45.466,+126
resnet50.ram_in1k,30.451,69.549,48.997,51.003,25.56,288,0.950,bicubic,-49.525,-46.055,+102
efficientnet_b2.ra_in1k,30.427,69.573,49.688,50.312,9.11,288,1.000,bicubic,-50.183,-45.625,+53
tf_efficientnet_b1.ap_in1k,30.421,69.579,49.549,50.451,7.79,240,0.882,bicubic,-48.859,-44.759,+154
cs3darknet_x.c2ns_in1k,30.409,69.591,49.197,50.803,35.05,288,1.000,bicubic,-51.813,-47.033,-104
xcit_tiny_12_p16_384.fb_dist_in1k,30.403,69.597,50.131,49.869,6.72,384,1.000,bicubic,-50.535,-45.283,+19
twins_pcpvt_small.in1k,30.392,69.608,49.388,50.612,24.11,224,0.900,bicubic,-50.704,-46.260,+4
ecaresnetlight.miil_in1k,30.382,69.618,49.146,50.854,30.16,288,0.950,bicubic,-51.026,-46.670,-30
resnet50d.ra2_in1k,30.376,69.624,48.792,51.208,25.58,288,0.950,bicubic,-50.980,-46.946,-24
ecaresnet101d_pruned.miil_in1k,30.360,69.640,48.810,51.190,24.88,288,0.950,bicubic,-51.638,-47.350,-85
visformer_small.in1k,30.337,69.663,48.285,51.715,40.22,224,0.900,bicubic,-51.765,-47.412,-96
tf_efficientnet_b5.in1k,30.295,69.705,49.761,50.239,30.39,456,0.934,bicubic,-52.881,-46.775,-221
resnet101.a1_in1k,30.293,69.707,46.580,53.420,44.55,288,1.000,bicubic,-52.029,-49.052,-124
regnety_040.ra3_in1k,30.270,69.730,48.926,51.074,20.65,288,1.000,bicubic,-52.774,-47.572,-207
regnetx_160.tv2_in1k,30.217,69.783,47.055,52.945,54.28,224,0.965,bicubic,-52.349,-49.117,-153
mobilevitv2_175.cvnets_in22k_ft_in1k,30.209,69.791,49.056,50.944,14.25,256,0.888,bicubic,-51.729,-46.734,-83
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,30.201,69.799,48.688,51.312,119.42,256,0.900,bicubic,-49.283,-45.450,+124
resnet50.b1k_in1k,30.197,69.803,49.187,50.813,25.56,288,1.000,bicubic,-50.509,-46.245,+30
seresnext50_32x4d.racm_in1k,30.168,69.832,49.093,50.907,27.56,288,0.950,bicubic,-52.028,-47.055,-113
resnet50.b2k_in1k,30.152,69.848,48.244,51.756,25.56,288,1.000,bicubic,-50.302,-47.074,+52
regnety_016.tv2_in1k,30.138,69.862,49.223,50.777,11.20,224,0.965,bicubic,-50.530,-46.107,+31
wide_resnet50_2.tv2_in1k,30.119,69.882,48.360,51.640,68.88,224,0.965,bilinear,-51.487,-47.402,-66
dpn68b.ra_in1k,30.101,69.899,48.172,51.828,12.61,288,1.000,bicubic,-49.261,-46.267,+127
convmixer_1024_20_ks9_p14.in1k,30.093,69.907,49.934,50.066,24.38,224,0.960,bicubic,-46.843,-43.416,+277
efficientnet_el.ra_in1k,30.030,69.970,48.842,51.158,10.59,300,0.904,bicubic,-51.282,-46.694,-38
tf_efficientnet_b2.aa_in1k,30.012,69.988,49.590,50.410,9.11,260,0.890,bicubic,-50.072,-45.316,+74
xcit_tiny_12_p16_224.fb_dist_in1k,30.010,69.990,49.651,50.349,6.72,224,1.000,bicubic,-48.564,-44.547,+177
halo2botnet50ts_256.a1h_in1k,29.995,70.005,48.376,51.624,22.64,256,0.950,bicubic,-52.067,-47.258,-109
legacy_senet154.in1k,29.991,70.009,48.038,51.962,115.09,224,0.875,bilinear,-51.317,-47.454,-41
dpn98.mx_in1k,29.981,70.019,48.146,51.854,61.57,224,0.875,bicubic,-49.689,-46.508,+100
mobilevitv2_150.cvnets_in22k_ft_in1k,29.971,70.029,49.217,50.783,10.59,256,0.888,bicubic,-51.517,-46.451,-63
resnetv2_50d_gn.ah_in1k,29.957,70.043,48.195,51.805,25.57,288,1.000,bicubic,-52.001,-47.733,-102
dpn92.mx_in1k,29.946,70.054,49.113,50.887,37.67,224,0.875,bicubic,-50.092,-45.747,+69
resnext50_32x4d.a1h_in1k,29.942,70.058,48.234,51.766,25.03,288,1.000,bicubic,-52.072,-47.700,-111
convnextv2_pico.fcmae_ft_in1k,29.938,70.062,48.847,51.153,9.07,288,0.950,bicubic,-51.148,-46.633,-21
dpn131.mx_in1k,29.934,70.066,48.034,51.966,79.25,224,0.875,bicubic,-49.880,-46.666,+81
resnetv2_101x1_bit.goog_in21k_ft_in1k,29.896,70.104,51.111,48.889,44.54,448,1.000,bilinear,-52.446,-45.409,-152
senet154.gluon_in1k,29.885,70.115,47.879,52.121,115.09,224,0.875,bicubic,-51.341,-47.479,-45
tf_efficientnet_b4.in1k,29.863,70.137,49.013,50.987,19.34,380,0.922,bicubic,-52.745,-46.739,-191
legacy_xception.tf_in1k,29.855,70.145,48.684,51.316,22.86,299,0.897,bicubic,-49.185,-45.698,+136
resnet50.tv2_in1k,29.840,70.160,48.010,51.990,25.56,224,0.965,bilinear,-51.010,-47.424,-4
cs3sedarknet_l.c2ns_in1k,29.828,70.172,49.009,50.991,21.91,288,0.950,bicubic,-51.956,-46.949,-98
inception_v3.tf_adv_in1k,29.826,70.174,47.839,52.161,23.83,299,0.875,bicubic,-47.766,-45.891,+225
resnet152.a2_in1k,29.826,70.174,45.961,54.039,60.19,288,1.000,bicubic,-52.782,-50.167,-195
vit_base_patch16_384.augreg_in1k,29.798,70.202,48.333,51.667,86.86,384,1.000,bicubic,-51.304,-46.787,-36
resnetaa50.a1h_in1k,29.788,70.212,48.014,51.986,25.56,288,1.000,bicubic,-51.826,-47.788,-92
lamhalobotnet50ts_256.a1h_in1k,29.755,70.245,48.335,51.665,22.57,256,0.950,bicubic,-51.799,-47.155,-87
fbnetv3_d.ra2_in1k,29.743,70.257,49.461,50.539,10.31,256,0.950,bilinear,-49.939,-45.483,+80
ese_vovnet39b.ra_in1k,29.733,70.267,49.046,50.954,24.57,288,0.950,bicubic,-50.617,-46.320,+29
resmlp_36_224.fb_in1k,29.698,70.302,48.958,51.042,44.69,224,0.875,bicubic,-50.074,-45.926,+70
convnext_nano.d1h_in1k,29.684,70.316,47.920,52.080,15.59,288,1.000,bicubic,-51.798,-47.738,-82
resnet50.c1_in1k,29.672,70.328,48.458,51.542,25.56,288,1.000,bicubic,-51.240,-47.094,-24
vit_base_patch32_384.augreg_in1k,29.649,70.351,48.989,51.011,88.30,384,1.000,bicubic,-49.103,-45.235,+141
ecaresnet50d.miil_in1k,29.562,70.438,48.967,51.033,25.58,288,0.950,bicubic,-52.088,-46.915,-103
nest_tiny_jx.goog_in1k,29.558,70.442,46.975,53.025,17.06,224,0.875,bicubic,-51.868,-48.643,-79
resnet152.a3_in1k,29.529,70.471,47.034,52.966,60.19,224,0.950,bicubic,-51.017,-47.966,+4
gcresnext50ts.ch_in1k,29.519,70.481,47.853,52.147,15.67,288,1.000,bicubic,-51.713,-47.691,-65
resnext50_32x4d.tv2_in1k,29.480,70.520,47.232,52.768,25.03,224,0.965,bilinear,-51.702,-48.108,-58
cs3darknet_l.c2ns_in1k,29.464,70.536,48.230,51.770,21.16,288,0.950,bicubic,-51.432,-47.432,-30
efficientnet_em.ra2_in1k,29.462,70.538,48.914,51.086,6.90,240,0.882,bicubic,-49.778,-45.880,+102
resnet50.fb_ssl_yfcc100m_ft_in1k,29.439,70.561,49.789,50.211,25.56,224,0.875,bilinear,-49.795,-45.037,+102
resnext101_32x8d.tv_in1k,29.437,70.563,48.488,51.512,88.79,224,0.875,bilinear,-49.873,-46.032,+92
coat_lite_mini.in1k,29.435,70.565,47.718,52.282,11.01,224,0.900,bicubic,-49.667,-46.890,+108
resnetv2_50.a1h_in1k,29.435,70.565,47.441,52.559,25.55,288,1.000,bicubic,-51.963,-48.285,-85
deit_small_patch16_224.fb_in1k,29.425,70.575,48.256,51.744,22.05,224,0.900,bicubic,-50.419,-46.788,+48
sebotnet33ts_256.a1h_in1k,29.423,70.577,47.160,52.840,13.70,256,0.940,bicubic,-51.743,-48.009,-65
nf_regnet_b1.ra2_in1k,29.409,70.591,49.431,50.569,10.22,288,0.900,bicubic,-49.897,-45.309,+88
cait_xxs24_384.fb_dist_in1k,29.391,70.609,48.751,51.249,12.03,384,1.000,bicubic,-51.581,-46.889,-48
resnetv2_50d_evos.ah_in1k,29.382,70.618,47.226,52.774,25.59,288,1.000,bicubic,-52.618,-48.674,-145
edgenext_small_rw.sw_in1k,29.346,70.654,48.722,51.278,7.83,320,1.000,bicubic,-51.112,-46.468,+1
convnext_nano_ols.d1h_in1k,29.325,70.675,47.478,52.522,15.65,288,1.000,bicubic,-52.275,-48.158,-114
regnetz_b16.ra3_in1k,29.321,70.679,47.885,52.115,9.72,288,1.000,bicubic,-51.407,-47.635,-26
swin_tiny_patch4_window7_224.ms_in1k,29.321,70.679,47.609,52.391,28.29,224,0.900,bicubic,-52.055,-47.934,-91
cait_xxs24_224.fb_dist_in1k,29.295,70.705,48.537,51.463,11.96,224,1.000,bicubic,-49.089,-45.779,+149
eca_resnet33ts.ra2_in1k,29.277,70.723,48.928,51.072,19.68,288,1.000,bicubic,-51.395,-46.436,-24
resnet50.d_in1k,29.250,70.750,47.213,52.787,25.56,288,1.000,bicubic,-51.722,-48.217,-55
resnet50.c2_in1k,29.244,70.756,47.165,52.835,25.56,288,1.000,bicubic,-51.626,-48.369,-43
pvt_v2_b1.in1k,29.238,70.762,48.958,51.042,14.01,224,0.900,bicubic,-49.464,-45.544,+121
maxvit_rmlp_pico_rw_256.sw_in1k,29.238,70.762,47.719,52.281,7.52,256,0.950,bicubic,-51.276,-47.495,-16
gcvit_xxtiny.in1k,29.218,70.781,48.348,51.652,12.00,224,0.875,bicubic,-50.509,-46.732,+42
poolformer_s24.sail_in1k,29.207,70.793,48.079,51.921,21.39,224,0.900,bicubic,-51.089,-46.993,+6
tresnet_l.miil_in1k_448,29.165,70.835,47.226,52.774,55.99,448,0.875,bilinear,-53.111,-48.752,-185
seresnet50.ra2_in1k,29.148,70.852,47.753,52.247,28.09,288,0.950,bicubic,-52.136,-47.899,-93
inception_v3.gluon_in1k,29.112,70.888,46.943,53.057,23.83,299,0.875,bicubic,-49.694,-47.431,+106
lambda_resnet50ts.a1h_in1k,29.108,70.891,46.961,53.039,21.54,256,0.950,bicubic,-52.048,-48.137,-82
resnet101.a2_in1k,29.091,70.909,45.762,54.238,44.55,288,1.000,bicubic,-53.145,-49.968,-186
xception71.tf_in1k,29.034,70.966,47.395,52.605,42.34,299,0.903,bicubic,-50.838,-47.533,+22
resnet34d.ra2_in1k,29.018,70.982,48.052,51.948,21.82,288,0.950,bicubic,-49.418,-46.292,+128
hrnet_w64.ms_in1k,28.987,71.013,47.138,52.862,128.06,224,0.875,bilinear,-50.487,-47.508,+53
xcit_tiny_12_p8_224.fb_in1k,28.979,71.021,47.501,52.499,6.71,224,1.000,bicubic,-50.709,-47.209,+36
regnetx_032.tv2_in1k,28.975,71.025,47.069,52.931,15.30,224,0.965,bicubic,-51.951,-48.209,-65
cs3darknet_focus_l.c2ns_in1k,28.934,71.066,47.641,52.359,21.15,288,0.950,bicubic,-51.946,-48.041,-60
tf_efficientnet_b0.ns_jft_in1k,28.902,71.098,49.003,50.997,5.29,224,0.875,bicubic,-49.768,-45.371,+109
xception65.tf_in1k,28.898,71.102,47.156,52.844,39.92,299,0.903,bicubic,-50.658,-47.498,+42
tf_efficientnet_b1.aa_in1k,28.886,71.114,47.523,52.477,7.79,240,0.882,bicubic,-49.944,-46.675,+93
vit_small_patch32_384.augreg_in21k_ft_in1k,28.875,71.125,48.883,51.117,22.92,384,1.000,bicubic,-51.611,-46.713,-30
mobilevitv2_150.cvnets_in22k_ft_in1k_384,28.871,71.129,47.936,52.064,10.59,384,1.000,bicubic,-53.715,-48.378,-243
resnet101.gluon_in1k,28.853,71.147,46.377,53.623,44.55,224,0.875,bicubic,-50.457,-48.145,+55
skresnext50_32x4d.ra_in1k,28.810,71.190,46.501,53.499,27.48,224,0.875,bicubic,-51.352,-48.139,-1
sehalonet33ts.ra2_in1k,28.776,71.224,46.574,53.426,13.69,256,0.940,bicubic,-52.180,-48.702,-76
levit_256.fb_dist_in1k,28.741,71.259,46.727,53.273,18.89,224,0.900,bicubic,-52.773,-48.765,-135
levit_conv_256.fb_dist_in1k,28.741,71.259,46.721,53.279,18.89,224,0.900,bicubic,-52.779,-48.769,-137
resnet50.ra_in1k,28.694,71.306,47.366,52.634,25.56,288,0.950,bicubic,-51.142,-47.600,+13
resnetblur50.bt_in1k,28.662,71.338,46.902,53.098,25.56,288,0.950,bicubic,-51.572,-48.332,-12
tf_efficientnet_lite3.in1k,28.654,71.346,47.350,52.650,8.20,300,0.904,bilinear,-51.154,-47.562,+13
darknetaa53.c2ns_in1k,28.654,71.346,46.953,53.047,36.02,288,1.000,bilinear,-51.847,-48.369,-41
hrnet_w40.ms_in1k,28.647,71.353,47.452,52.548,57.56,224,0.875,bilinear,-50.283,-47.012,+74
skresnet34.ra_in1k,28.635,71.365,47.963,52.037,22.28,224,0.875,bicubic,-48.275,-45.181,+195
swinv2_tiny_window8_256.ms_in1k,28.627,71.373,46.189,53.811,28.35,256,0.900,bicubic,-53.193,-49.805,-169
seresnext50_32x4d.gluon_in1k,28.621,71.379,46.438,53.562,27.56,224,0.875,bicubic,-51.289,-48.386,-3
mobilevitv2_175.cvnets_in22k_ft_in1k_384,28.598,71.403,47.118,52.882,14.25,384,1.000,bicubic,-54.341,-49.308,-295
tf_efficientnet_b3.in1k,28.582,71.418,47.987,52.013,12.23,300,0.904,bicubic,-52.296,-47.313,-79
halonet50ts.a1h_in1k,28.578,71.422,46.179,53.821,22.73,256,0.940,bicubic,-53.082,-49.431,-164
tf_efficientnetv2_b0.in1k,28.566,71.434,47.075,52.925,7.14,224,0.875,bicubic,-49.792,-46.939,+111
poolformerv2_s12.sail_in1k,28.556,71.444,47.403,52.597,11.89,224,1.000,bicubic,-49.448,-46.461,+133
resnet152.tv_in1k,28.543,71.457,47.106,52.894,60.19,224,0.875,bilinear,-49.779,-46.942,+111
xcit_tiny_12_p16_224.fb_in1k,28.515,71.485,47.403,52.597,6.72,224,1.000,bicubic,-48.625,-46.313,+175
eva02_tiny_patch14_336.mim_in22k_ft_in1k,28.507,71.493,47.541,52.459,5.76,336,1.000,bicubic,-52.123,-47.985,-63
ecaresnet50t.a1_in1k,28.456,71.544,45.576,54.424,25.57,288,1.000,bicubic,-53.672,-50.066,-205
repvgg_b2.rvgg_in1k,28.421,71.579,47.044,52.956,89.02,224,0.875,bilinear,-50.371,-47.376,+72
hrnet_w48.ms_in1k,28.407,71.593,47.572,52.428,77.47,224,0.875,bilinear,-50.895,-46.944,+35
swinv2_cr_tiny_ns_224.sw_in1k,28.379,71.621,45.902,54.098,28.33,224,0.900,bicubic,-53.423,-49.916,-180
resnext50_32x4d.gluon_in1k,28.372,71.628,45.332,54.668,25.03,224,0.875,bicubic,-50.988,-49.098,+28
efficientnet_b2_pruned.in1k,28.360,71.640,47.061,52.939,8.31,260,0.890,bicubic,-51.560,-47.791,-19
tf_efficientnet_b0.ap_in1k,28.352,71.648,47.543,52.457,5.29,224,0.875,bicubic,-48.738,-45.719,+169
dla169.in1k,28.328,71.672,47.388,52.612,53.39,224,0.875,bilinear,-50.380,-46.959,+73
darknet53.c2ns_in1k,28.324,71.676,46.880,53.120,41.61,288,1.000,bicubic,-52.212,-48.553,-66
tf_efficientnet_cc_b0_4e.in1k,28.317,71.683,47.364,52.636,13.31,224,0.875,bicubic,-48.986,-45.972,+155
dla102x2.in1k,28.315,71.685,46.772,53.228,41.28,224,0.875,bilinear,-51.129,-47.866,+16
resnext50_32x4d.ra_in1k,28.303,71.697,46.081,53.919,25.03,288,0.950,bicubic,-52.395,-49.309,-81
mixnet_xl.ra_in1k,28.291,71.709,46.708,53.292,11.90,224,0.875,bicubic,-52.191,-48.229,-64
resnet50d.gluon_in1k,28.234,71.766,45.884,54.116,25.58,224,0.875,bicubic,-50.844,-48.584,+41
seresnet33ts.ra2_in1k,28.232,71.768,47.578,52.422,19.78,288,1.000,bicubic,-52.552,-47.784,-92
resnet50.a1_in1k,28.220,71.780,44.937,55.063,25.56,288,1.000,bicubic,-52.994,-50.165,-137
densenet161.tv_in1k,28.108,71.892,46.653,53.347,28.68,224,0.875,bicubic,-49.250,-46.989,+144
resnet101c.gluon_in1k,28.104,71.896,45.953,54.047,44.57,224,0.875,bicubic,-51.438,-48.633,+2
resnet34.a1_in1k,28.102,71.898,45.703,54.297,21.80,288,1.000,bicubic,-49.816,-47.211,+116
wide_resnet101_2.tv_in1k,28.095,71.906,46.426,53.574,126.89,224,0.875,bilinear,-50.743,-47.855,+49
regnetx_320.pycls_in1k,28.079,71.921,45.118,54.882,107.81,224,0.875,bicubic,-52.167,-49.904,-49
regnety_320.pycls_in1k,28.075,71.925,45.460,54.540,145.05,224,0.875,bicubic,-52.733,-49.778,-102
resnext50_32x4d.a1_in1k,28.075,71.925,44.815,55.185,25.03,288,1.000,bicubic,-53.391,-50.359,-170
gernet_s.idstcv_in1k,28.051,71.949,46.727,53.273,8.17,224,0.875,bilinear,-48.859,-46.589,+162
ecaresnet50d_pruned.miil_in1k,28.043,71.957,47.036,52.964,19.94,288,0.950,bicubic,-52.747,-48.534,-103
levit_conv_192.fb_dist_in1k,28.032,71.968,45.874,54.126,10.95,224,0.900,bicubic,-51.806,-48.904,-30
mobilevitv2_175.cvnets_in1k,28.028,71.972,46.098,53.902,14.25,256,0.888,bicubic,-52.832,-49.158,-111
levit_192.fb_dist_in1k,28.028,71.972,45.870,54.130,10.95,224,0.900,bicubic,-51.810,-48.914,-30
efficientnet_el_pruned.in1k,28.002,71.998,46.804,53.196,10.59,300,0.904,bicubic,-52.298,-48.418,-62
vit_base_patch16_224.augreg_in1k,27.973,72.027,45.739,54.261,86.57,224,0.900,bicubic,-51.181,-48.351,+20
resnet101.a3_in1k,27.925,72.075,45.012,54.988,44.55,224,0.950,bicubic,-51.888,-49.594,-31
resnet50_gn.a1h_in1k,27.916,72.084,46.069,53.931,25.56,288,0.950,bicubic,-53.300,-49.559,-154
xception41.tf_in1k,27.880,72.120,45.890,54.110,26.97,299,0.903,bicubic,-50.624,-48.386,+56
regnetx_160.pycls_in1k,27.827,72.173,45.631,54.369,54.28,224,0.875,bicubic,-52.039,-49.197,-41
dpn68b.mx_in1k,27.814,72.186,47.415,52.585,12.61,224,0.875,bicubic,-49.704,-46.437,+119
resnet50d.a1_in1k,27.790,72.210,44.377,55.623,25.58,288,1.000,bicubic,-53.660,-50.841,-180
inception_v3.tf_in1k,27.782,72.218,45.723,54.277,23.83,299,0.875,bicubic,-50.080,-48.143,+103
res2net101_26w_4s.in1k,27.778,72.222,45.159,54.841,45.21,224,0.875,bilinear,-51.424,-49.277,+10
tf_efficientnetv2_b1.in1k,27.743,72.257,46.574,53.426,8.14,240,0.882,bicubic,-51.719,-48.148,-14
vit_base_patch16_224.sam_in1k,27.703,72.297,45.100,54.900,86.57,224,0.900,bicubic,-52.541,-49.656,-67
fbnetv3_b.ra2_in1k,27.670,72.330,46.987,53.013,8.60,256,0.950,bilinear,-51.472,-47.755,+10
repvgg_b1.rvgg_in1k,27.637,72.363,46.533,53.467,57.42,224,0.875,bilinear,-50.731,-47.563,+62
mobilevitv2_200.cvnets_in1k,27.637,72.363,45.784,54.216,18.45,256,0.888,bicubic,-53.497,-49.578,-155
regnety_160.pycls_in1k,27.637,72.363,45.531,54.469,83.59,224,0.875,bicubic,-52.661,-49.431,-75
hrnet_w44.ms_in1k,27.633,72.367,45.839,54.161,67.06,224,0.875,bilinear,-51.259,-48.525,+21
resnet50.am_in1k,27.580,72.420,45.371,54.629,25.56,224,0.875,bicubic,-51.418,-49.025,+13
inception_v3.tv_in1k,27.554,72.446,45.265,54.735,23.83,299,0.875,bicubic,-49.880,-48.209,+111
resmlp_24_224.fb_in1k,27.517,72.483,45.690,54.310,30.02,224,0.875,bicubic,-51.857,-48.856,-17
pit_xs_224.in1k,27.477,72.522,45.904,54.096,10.62,224,0.900,bicubic,-50.703,-48.260,+68
gcresnet33ts.ra2_in1k,27.401,72.599,46.161,53.839,19.88,288,1.000,bicubic,-53.199,-49.159,-111
regnetx_080.pycls_in1k,27.397,72.603,45.012,54.988,39.57,224,0.875,bicubic,-51.805,-49.540,-4
hrnet_w30.ms_in1k,27.393,72.607,46.554,53.446,37.71,224,0.875,bilinear,-50.799,-47.668,+64
hrnet_w32.ms_in1k,27.375,72.625,45.998,54.002,41.23,224,0.875,bilinear,-51.069,-48.190,+44
convnext_pico.d1_in1k,27.361,72.638,45.660,54.340,9.05,288,0.950,bicubic,-53.053,-49.390,-97
vit_small_patch16_384.augreg_in1k,27.332,72.668,46.118,53.882,22.20,384,1.000,bicubic,-53.784,-49.456,-166
resnet50s.gluon_in1k,27.328,72.672,45.208,54.792,25.68,224,0.875,bicubic,-51.390,-49.034,+23
res2net50_26w_8s.in1k,27.306,72.694,44.815,55.185,48.40,224,0.875,bilinear,-51.635,-49.479,+5
convnext_pico_ols.d1_in1k,27.301,72.699,45.644,54.356,9.06,288,1.000,bicubic,-53.163,-49.602,-107
densenet201.tv_in1k,27.271,72.729,46.210,53.790,20.01,224,0.875,bicubic,-50.015,-47.270,+107
resnet33ts.ra2_in1k,27.257,72.743,45.183,54.817,19.68,288,1.000,bicubic,-52.469,-49.645,-55
ecaresnet50t.a2_in1k,27.246,72.754,44.047,55.953,25.57,288,1.000,bicubic,-54.412,-51.505,-229
regnety_064.pycls_in1k,27.232,72.768,44.868,55.132,30.58,224,0.875,bicubic,-52.482,-49.898,-56
efficientnet_b1_pruned.in1k,27.196,72.804,45.863,54.137,6.33,240,0.882,bicubic,-51.046,-48.029,+51
tf_efficientnetv2_b2.in1k,27.167,72.833,44.572,55.428,10.10,260,0.890,bicubic,-53.027,-50.471,-87
vit_base_patch32_224.augreg_in1k,27.140,72.861,45.175,54.825,88.22,224,0.900,bicubic,-47.755,-46.603,+167
seresnet50.a1_in1k,27.124,72.876,43.563,56.437,28.09,288,1.000,bicubic,-53.978,-51.767,-174
resnet50d.a2_in1k,27.118,72.882,43.817,56.183,25.58,288,1.000,bicubic,-54.046,-51.267,-183
resnetrs50.tf_in1k,27.098,72.902,45.025,54.975,35.69,224,0.910,bicubic,-52.798,-49.947,-78
rexnet_130.nav_in1k,27.077,72.923,45.957,54.043,7.56,224,0.875,bicubic,-52.427,-48.721,-48
gmixer_24_224.ra3_in1k,27.031,72.969,44.353,55.647,24.72,224,0.875,bicubic,-50.995,-49.315,+57
dla102x.in1k,27.026,72.975,45.503,54.497,26.31,224,0.875,bilinear,-51.473,-48.733,+19
resnet101.tv_in1k,26.966,73.034,45.234,54.766,44.55,224,0.875,bilinear,-50.410,-48.308,+88
regnetx_120.pycls_in1k,26.866,73.134,44.684,55.316,46.11,224,0.875,bicubic,-52.722,-50.059,-56
resnet32ts.ra2_in1k,26.851,73.149,45.045,54.955,17.96,288,1.000,bicubic,-52.541,-49.607,-45
rexnet_100.nav_in1k,26.839,73.161,45.377,54.623,4.80,224,0.875,bicubic,-51.023,-48.267,+63
legacy_seresnext101_32x4d.in1k,26.823,73.177,43.507,56.493,48.96,224,0.875,bilinear,-53.407,-51.519,-101
densenet169.tv_in1k,26.819,73.181,45.381,54.619,14.15,224,0.875,bicubic,-49.081,-47.647,+134
tinynet_a.in1k,26.813,73.187,45.082,54.918,6.19,192,0.875,bicubic,-50.845,-48.458,+68
regnetx_064.pycls_in1k,26.799,73.201,44.915,55.085,26.21,224,0.875,bicubic,-52.267,-49.543,-22
regnety_120.pycls_in1k,26.784,73.216,44.442,55.558,51.82,224,0.875,bicubic,-53.602,-50.686,-122
regnetx_032.pycls_in1k,26.717,73.283,45.230,54.770,15.30,224,0.875,bicubic,-51.451,-48.852,+37
res2net101d.in1k,26.711,73.289,44.336,55.664,45.23,224,0.875,bilinear,-54.513,-51.016,-206
resnext50_32x4d.a2_in1k,26.682,73.318,42.768,57.232,25.03,288,1.000,bicubic,-54.622,-52.328,-212
legacy_seresnet152.in1k,26.670,73.330,43.953,56.047,66.82,224,0.875,bilinear,-51.990,-50.418,+1
efficientnet_es.ra_in1k,26.623,73.377,45.096,54.904,5.44,224,0.875,bicubic,-51.431,-48.828,+40
res2net50_26w_6s.in1k,26.589,73.411,44.004,55.996,37.05,224,0.875,bilinear,-51.979,-50.118,+1
repvgg_b1g4.rvgg_in1k,26.579,73.421,45.100,54.900,39.97,224,0.875,bilinear,-51.003,-48.736,+64
dla60x.in1k,26.578,73.422,45.031,54.969,17.35,224,0.875,bilinear,-51.662,-49.003,+27
coat_lite_tiny.in1k,26.517,73.484,44.646,55.354,5.72,224,0.900,bicubic,-51.003,-49.276,+64
regnety_080.pycls_in1k,26.515,73.485,44.355,55.645,39.18,224,0.875,bicubic,-53.353,-50.477,-98
mobilenetv3_large_100.miil_in21k_ft_in1k,26.499,73.501,44.497,55.503,5.48,224,0.875,bilinear,-51.419,-49.269,+45
tf_efficientnet_b0.aa_in1k,26.479,73.521,45.641,54.359,5.29,224,0.875,bicubic,-50.355,-47.579,+95
res2net50_14w_8s.in1k,26.477,73.523,44.371,55.629,25.06,224,0.875,bilinear,-51.681,-49.475,+27
tf_efficientnet_b2.in1k,26.462,73.538,44.788,55.212,9.11,260,0.890,bicubic,-53.142,-49.926,-79
resnet50.gluon_in1k,26.426,73.574,44.037,55.963,25.56,224,0.875,bicubic,-51.154,-49.681,+57
ecaresnet50t.a3_in1k,26.416,73.584,43.508,56.492,25.57,224,0.950,bicubic,-53.136,-51.186,-77
tf_efficientnet_el.in1k,26.355,73.645,44.175,55.825,10.59,300,0.904,bicubic,-53.897,-50.943,-126
levit_conv_128.fb_dist_in1k,26.330,73.670,44.120,55.880,9.21,224,0.900,bicubic,-52.164,-49.888,-7
levit_128.fb_dist_in1k,26.328,73.672,44.116,55.884,9.21,224,0.900,bicubic,-52.162,-49.896,-6
lambda_resnet26t.c1_in1k,26.326,73.674,44.430,55.570,10.96,256,0.940,bicubic,-52.762,-50.160,-46
resmlp_big_24_224.fb_in1k,26.318,73.682,43.554,56.446,129.14,224,0.875,bicubic,-54.718,-51.464,-203
resmlp_12_224.fb_distilled_in1k,26.316,73.684,44.874,55.126,15.35,224,0.875,bicubic,-51.638,-48.686,+31
visformer_tiny.in1k,26.255,73.745,44.184,55.816,10.32,224,0.900,bicubic,-51.907,-49.980,+16
regnetx_040.pycls_in1k,26.241,73.759,44.424,55.576,22.12,224,0.875,bicubic,-52.251,-49.818,-12
mobilevitv2_150.cvnets_in1k,26.178,73.822,43.762,56.238,10.59,256,0.888,bicubic,-54.192,-51.312,-146
crossvit_9_dagger_240.in1k,26.173,73.827,44.544,55.456,8.78,240,0.875,bicubic,-50.807,-49.076,+75
vit_small_patch32_224.augreg_in21k_ft_in1k,26.165,73.835,45.106,54.894,22.88,224,0.900,bicubic,-49.829,-47.692,+99
seresnet50.a2_in1k,26.165,73.835,42.675,57.325,28.09,288,1.000,bicubic,-54.941,-52.547,-217
densenetblur121d.ra_in1k,26.135,73.865,45.039,54.961,8.00,288,0.950,bicubic,-51.185,-48.751,+53
resnet50.a2_in1k,26.094,73.906,42.583,57.417,25.56,288,1.000,bicubic,-54.678,-52.405,-186
resnet50d.a3_in1k,26.090,73.910,42.976,57.024,25.58,224,0.950,bicubic,-52.632,-51.260,-33
resnet34.a2_in1k,26.084,73.916,43.108,56.892,21.80,288,1.000,bicubic,-51.074,-50.166,+61
resnext50_32x4d.a3_in1k,26.082,73.918,42.946,57.054,25.03,224,0.950,bicubic,-53.186,-51.360,-71
efficientnet_b1.ft_in1k,26.055,73.945,44.080,55.920,7.79,256,1.000,bicubic,-52.745,-50.262,-41
convnextv2_femto.fcmae_ft_in1k,26.037,73.963,44.302,55.698,5.23,288,0.950,bicubic,-53.301,-50.258,-81
lambda_resnet26rpt_256.c1_in1k,26.033,73.967,44.190,55.810,10.99,256,0.940,bicubic,-52.935,-50.248,-56
mobilevitv2_125.cvnets_in1k,26.021,73.979,43.668,56.332,7.48,256,0.888,bicubic,-53.659,-51.190,-106
dpn68.mx_in1k,26.006,73.994,44.084,55.916,12.61,224,0.875,bicubic,-50.340,-48.924,+84
hrnet_w18.ms_in1k,25.978,74.022,44.807,55.193,21.30,224,0.875,bilinear,-50.774,-48.637,+69
hardcorenas_f.miil_green_in1k,25.939,74.061,44.208,55.792,8.20,224,0.875,bilinear,-52.159,-49.598,+3
vit_small_patch16_224.augreg_in1k,25.939,74.061,43.964,56.036,22.05,224,0.900,bicubic,-52.907,-50.324,-53
regnety_040.pycls_in1k,25.907,74.093,43.858,56.142,20.65,224,0.875,bicubic,-53.325,-50.798,-76
regnetx_016.tv2_in1k,25.878,74.122,43.353,56.647,9.19,224,0.965,bicubic,-53.558,-51.415,-95
res2net50_26w_4s.in1k,25.876,74.124,43.163,56.837,25.70,224,0.875,bilinear,-52.074,-50.689,+10
resnext50d_32x4d.bt_in1k,25.876,74.124,42.956,57.044,25.05,288,0.950,bicubic,-54.788,-52.464,-190
tresnet_m.miil_in1k_448,25.860,74.140,42.868,57.132,31.39,448,0.875,bilinear,-55.848,-52.706,-298
coat_tiny.in1k,25.858,74.142,43.275,56.725,5.50,224,0.900,bicubic,-52.568,-50.773,-27
densenet121.ra_in1k,25.821,74.179,44.860,55.140,7.98,288,0.950,bicubic,-50.679,-48.508,+70
hardcorenas_c.miil_green_in1k,25.819,74.181,44.778,55.222,5.52,224,0.875,bilinear,-51.239,-48.390,+48
resnet50c.gluon_in1k,25.791,74.209,43.021,56.979,25.58,224,0.875,bicubic,-52.221,-50.967,-1
halonet26t.a1h_in1k,25.776,74.224,43.220,56.780,12.48,256,0.950,bicubic,-53.330,-51.086,-79
SelecSls60.in1k,25.725,74.275,44.072,55.928,30.67,224,0.875,bicubic,-52.259,-49.760,0
hardcorenas_e.miil_green_in1k,25.662,74.338,43.422,56.578,8.07,224,0.875,bilinear,-52.122,-50.276,+9
poolformer_s12.sail_in1k,25.650,74.350,44.157,55.843,11.92,224,0.900,bicubic,-51.584,-49.375,+34
dla60_res2net.in1k,25.650,74.350,43.593,56.407,20.85,224,0.875,bilinear,-52.812,-50.609,-38
dla60_res2next.in1k,25.648,74.352,43.679,56.321,17.03,224,0.875,bilinear,-52.786,-50.465,-37
ecaresnet26t.ra2_in1k,25.538,74.462,43.660,56.340,16.01,320,0.950,bicubic,-54.312,-51.430,-143
resmlp_12_224.fb_in1k,25.528,74.472,44.330,55.670,15.35,224,0.875,bicubic,-51.120,-48.848,+51
tf_efficientnet_lite1.in1k,25.512,74.488,43.579,56.421,5.42,240,0.882,bicubic,-51.132,-49.651,+50
mixnet_l.ft_in1k,25.512,74.488,43.475,56.525,7.33,224,0.875,bicubic,-53.454,-50.705,-78
convnext_femto.d1_in1k,25.510,74.490,43.672,56.328,5.22,288,0.950,bicubic,-53.206,-50.759,-61
res2net50d.in1k,25.495,74.505,43.041,56.959,25.72,224,0.875,bilinear,-54.759,-51.995,-172
cs3darknet_focus_m.c2ns_in1k,25.493,74.507,43.762,56.238,9.30,288,0.950,bicubic,-51.791,-50.204,+24
resnext50_32x4d.tv_in1k,25.467,74.533,42.787,57.213,25.03,224,0.875,bilinear,-52.151,-50.909,+5
bat_resnext26ts.ch_in1k,25.455,74.545,43.194,56.806,10.73,256,0.900,bicubic,-52.797,-50.904,-33
botnet26t_256.c1_in1k,25.451,74.549,42.660,57.340,12.49,256,0.950,bicubic,-53.805,-51.875,-103
eca_halonext26ts.c1_in1k,25.442,74.558,43.182,56.818,10.76,256,0.940,bicubic,-54.044,-51.418,-125
repvgg_a2.rvgg_in1k,25.434,74.566,43.947,56.053,28.21,224,0.875,bilinear,-51.024,-49.055,+52
tf_mixnet_l.in1k,25.412,74.588,42.534,57.466,7.33,224,0.875,bicubic,-53.364,-51.468,-74
hardcorenas_b.miil_green_in1k,25.402,74.598,44.182,55.818,5.18,224,0.875,bilinear,-51.134,-48.578,+43
regnety_008_tv.tv2_in1k,25.395,74.606,43.430,56.570,6.43,224,0.965,bicubic,-53.280,-50.956,-68
res2next50.in1k,25.392,74.608,42.492,57.508,24.67,224,0.875,bilinear,-52.850,-51.343,-39
convnext_femto_ols.d1_in1k,25.387,74.613,43.137,56.863,5.23,288,0.950,bicubic,-53.537,-51.389,-89
efficientformerv2_s0.snap_dist_in1k,25.363,74.637,43.921,56.079,3.60,224,0.950,bicubic,-50.755,-48.939,+52
SelecSls60b.in1k,25.326,74.674,43.554,56.446,32.77,224,0.875,bicubic,-53.088,-50.480,-53
resnetv2_50x1_bit.goog_in21k_ft_in1k,25.322,74.678,45.350,54.650,25.55,448,1.000,bilinear,-55.018,-50.330,-197
hardcorenas_d.miil_green_in1k,25.322,74.678,43.141,56.859,7.50,224,0.875,bilinear,-52.118,-50.349,+1
legacy_seresnet101.in1k,25.322,74.678,42.831,57.169,49.33,224,0.875,bilinear,-53.064,-51.431,-53
regnety_032.pycls_in1k,25.316,74.684,42.909,57.091,19.44,224,0.875,bicubic,-53.560,-51.499,-92
wide_resnet50_2.tv_in1k,25.310,74.690,42.176,57.824,68.88,224,0.875,bilinear,-53.170,-51.910,-65
dla102.in1k,25.302,74.698,43.840,56.160,33.27,224,0.875,bilinear,-52.720,-50.092,-33
resnest14d.gluon_in1k,25.279,74.722,44.104,55.896,10.61,224,0.875,bilinear,-50.232,-48.400,+57
legacy_seresnext50_32x4d.in1k,25.212,74.788,41.944,58.056,27.56,224,0.875,bilinear,-53.864,-52.488,-107
mixer_b16_224.goog_in21k_ft_in1k,25.111,74.888,41.225,58.775,59.88,224,0.875,bicubic,-51.491,-50.999,+27
res2net50_48w_2s.in1k,25.025,74.975,42.206,57.794,25.29,224,0.875,bilinear,-52.489,-51.346,-10
efficientnet_b0.ra_in1k,25.015,74.985,42.797,57.203,5.29,224,0.875,bicubic,-52.679,-50.735,-21
resnet50.a3_in1k,25.002,74.999,41.891,58.109,25.56,224,0.950,bicubic,-53.047,-51.889,-41
resnet34.gluon_in1k,24.942,75.058,42.239,57.761,21.80,224,0.875,bicubic,-49.636,-49.745,+72
dla60.in1k,24.941,75.059,43.294,56.706,22.04,224,0.875,bilinear,-52.105,-50.024,+9
mobilenetv2_120d.ra_in1k,24.915,75.085,43.051,56.949,5.83,224,0.875,bicubic,-52.387,-50.453,-6
convnextv2_atto.fcmae_ft_in1k,24.890,75.111,42.469,57.531,3.71,288,0.950,bicubic,-52.871,-51.257,-28
eca_botnext26ts_256.c1_in1k,24.868,75.132,42.943,57.057,10.59,256,0.950,bicubic,-54.400,-51.663,-131
resnet34.bt_in1k,24.828,75.171,42.080,57.920,21.80,288,0.950,bicubic,-51.652,-51.274,+26
regnety_016.pycls_in1k,24.827,75.173,42.612,57.388,11.20,224,0.875,bicubic,-53.035,-51.106,-35
xcit_nano_12_p8_224.fb_dist_in1k,24.801,75.199,43.072,56.928,3.05,224,1.000,bicubic,-51.531,-50.022,+28
seresnet50.a3_in1k,24.793,75.207,42.101,57.899,28.09,224,0.950,bicubic,-52.235,-50.973,+3
pit_ti_distilled_224.in1k,24.715,75.285,43.227,56.773,5.10,224,0.900,bicubic,-49.539,-48.727,+65
cs3darknet_m.c2ns_in1k,24.626,75.374,42.970,57.030,9.31,288,0.950,bicubic,-53.008,-51.046,-30
eca_resnext26ts.ch_in1k,24.561,75.439,42.534,57.466,10.30,288,1.000,bicubic,-53.439,-51.392,-48
resnet50.bt_in1k,24.559,75.441,41.445,58.555,25.56,288,0.950,bicubic,-55.081,-53.447,-166
mobilevitv2_100.cvnets_in1k,24.553,75.447,42.921,57.079,4.90,256,0.888,bicubic,-53.523,-51.249,-57
tf_efficientnet_lite2.in1k,24.534,75.466,42.292,57.708,6.09,260,0.890,bicubic,-52.930,-51.458,-26
seresnext26ts.ch_in1k,24.506,75.494,42.665,57.335,10.39,288,1.000,bicubic,-53.766,-51.429,-72
skresnet18.ra_in1k,24.497,75.504,42.534,57.466,11.96,224,0.875,bicubic,-48.541,-48.638,+74
regnetx_016.pycls_in1k,24.477,75.523,42.490,57.510,9.19,224,0.875,bicubic,-52.447,-50.925,-1
tf_efficientnet_lite0.in1k,24.375,75.625,42.508,57.492,4.65,224,0.875,bicubic,-50.455,-49.664,+47
hardcorenas_a.miil_green_in1k,24.365,75.635,43.306,56.694,5.26,224,0.875,bilinear,-51.565,-49.204,+23
hrnet_w18.ms_aug_in1k,24.333,75.667,42.888,57.112,21.30,224,0.950,bilinear,-53.793,-51.165,-67
gcresnext26ts.ch_in1k,24.153,75.847,41.306,58.694,10.48,288,1.000,bicubic,-54.261,-52.864,-86
resnet50.tv_in1k,24.090,75.910,41.323,58.677,25.56,224,0.875,bilinear,-52.040,-51.535,+15
tf_efficientnet_b1.in1k,24.070,75.930,41.512,58.488,7.79,240,0.882,bicubic,-54.490,-52.582,-103
levit_128s.fb_dist_in1k,24.060,75.940,41.001,58.999,7.78,224,0.900,bicubic,-52.466,-51.871,+3
levit_conv_128s.fb_dist_in1k,24.052,75.948,41.003,58.997,7.78,224,0.900,bicubic,-52.468,-51.983,+4
legacy_seresnet34.in1k,24.023,75.977,41.903,58.097,21.96,224,0.875,bilinear,-50.785,-50.223,+40
xcit_nano_12_p16_384.fb_dist_in1k,24.005,75.995,42.306,57.694,3.05,384,1.000,bicubic,-51.453,-50.390,+26
xcit_nano_12_p8_384.fb_dist_in1k,23.956,76.044,41.954,58.046,3.05,384,1.000,bicubic,-53.864,-52.086,-55
efficientnet_lite0.ra_in1k,23.909,76.091,42.115,57.885,4.65,224,0.875,bicubic,-51.571,-50.407,+23
densenet121.tv_in1k,23.840,76.160,41.928,58.072,7.98,224,0.875,bicubic,-50.924,-50.225,+37
efficientnet_es_pruned.in1k,23.820,76.180,41.993,58.007,5.44,224,0.875,bicubic,-51.194,-50.453,+31
regnetx_008.tv2_in1k,23.779,76.221,40.689,59.311,7.26,224,0.965,bicubic,-53.525,-52.977,-37
mixnet_m.ft_in1k,23.716,76.284,41.148,58.852,5.01,224,0.875,bicubic,-53.542,-52.270,-33
resnet26t.ra2_in1k,23.712,76.288,41.319,58.681,16.01,320,1.000,bicubic,-54.620,-52.805,-94
mobilenetv2_140.ra_in1k,23.701,76.299,41.475,58.525,6.11,224,0.875,bicubic,-52.819,-51.391,-7
dla34.in1k,23.685,76.315,41.549,58.451,15.74,224,0.875,bilinear,-50.953,-50.515,+33
legacy_seresnet50.in1k,23.640,76.360,40.079,59.921,28.09,224,0.875,bilinear,-54.004,-53.679,-58
convnext_atto.d2_in1k,23.589,76.411,41.087,58.913,3.70,288,0.950,bicubic,-53.419,-52.614,-26
resnext26ts.ra2_in1k,23.589,76.411,40.891,59.109,10.30,288,1.000,bicubic,-53.585,-52.573,-36
tf_mixnet_m.in1k,23.484,76.516,40.997,59.003,5.01,224,0.875,bicubic,-53.470,-52.157,-26
resnet34.tv_in1k,23.475,76.525,41.361,58.639,21.80,224,0.875,bilinear,,,
SelecSls42b.in1k,23.369,76.632,40.665,59.335,32.46,224,0.875,bicubic,-53.802,-52.727,-39
tf_efficientnet_em.in1k,23.369,76.632,40.398,59.602,6.90,240,0.882,bicubic,-54.754,-53.648,-90
resnet34.a3_in1k,23.369,76.632,40.072,59.928,21.80,224,0.950,bicubic,-49.602,-51.037,+51
repvgg_b0.rvgg_in1k,23.319,76.681,41.164,58.836,15.82,224,0.875,bilinear,-51.823,-51.252,+12
regnety_004.tv2_in1k,23.282,76.718,40.993,59.007,4.34,224,0.965,bicubic,-52.316,-51.703,+3
xcit_nano_12_p16_224.fb_dist_in1k,23.260,76.740,41.368,58.632,3.05,224,1.000,bicubic,-49.050,-49.492,+53
convnext_atto_ols.a2_in1k,23.129,76.871,40.881,59.119,3.70,288,0.950,bicubic,-54.087,-52.795,-46
mobilenetv2_110d.ra_in1k,23.078,76.922,40.757,59.243,4.52,224,0.875,bicubic,-51.978,-51.429,+11
resnet18.a1_in1k,23.058,76.942,39.551,60.449,11.69,288,1.000,bicubic,-50.100,-51.475,+38
vit_base_patch32_224.sam_in1k,23.046,76.954,39.565,60.435,88.22,224,0.900,bicubic,-50.648,-51.449,+32
tinynet_b.in1k,23.021,76.979,40.974,59.026,3.73,188,0.875,bicubic,-51.957,-51.213,+11
resnet18d.ra2_in1k,23.005,76.995,41.197,58.803,11.71,288,0.950,bicubic,-50.789,-50.640,+29
deit_tiny_distilled_patch16_224.fb_in1k,22.728,77.272,40.775,59.225,5.91,224,0.900,bicubic,-51.772,-51.117,+19
mobilenetv3_large_100.ra_in1k,22.659,77.341,40.767,59.233,5.48,224,0.875,bicubic,-53.107,-51.771,-11
mobilenetv3_rw.rmsp_in1k,22.630,77.370,40.362,59.638,5.48,224,0.875,bicubic,-52.990,-52.342,-9
edgenext_x_small.in1k,22.590,77.410,39.500,60.500,2.34,288,1.000,bicubic,-53.098,-53.266,-12
tf_mobilenetv3_large_100.in1k,22.579,77.421,39.773,60.227,5.48,224,0.875,bilinear,-52.937,-52.821,-8
tf_efficientnet_b0.in1k,22.563,77.437,39.570,60.430,5.29,224,0.875,bicubic,-53.969,-53.438,-33
mobilevit_s.cvnets_in1k,22.484,77.516,38.649,61.351,5.58,256,0.900,bicubic,-55.828,-55.495,-118
xcit_nano_12_p8_224.fb_in1k,22.408,77.592,40.628,59.372,3.05,224,1.000,bicubic,-51.508,-51.542,+19
tf_efficientnet_es.in1k,22.406,77.594,39.089,60.911,5.44,224,0.875,bicubic,-54.194,-54.113,-38
hrnet_w18_small_v2.ms_in1k,22.341,77.659,39.863,60.137,15.60,224,0.875,bilinear,-52.773,-52.551,-4
convit_tiny.fb_in1k,22.268,77.732,39.675,60.325,5.71,224,0.875,bicubic,-50.844,-52.037,+25
regnetx_004_tv.tv2_in1k,22.207,77.793,39.111,60.889,5.50,224,0.965,bicubic,-52.389,-53.067,+6
regnety_008.pycls_in1k,22.115,77.885,38.902,61.098,6.26,224,0.875,bicubic,-54.187,-54.158,-30
ese_vovnet19b_dw.ra_in1k,22.072,77.928,39.456,60.544,6.54,288,0.950,bicubic,-55.672,-54.331,-91
regnety_006.pycls_in1k,21.975,78.025,38.961,61.039,6.06,224,0.875,bicubic,-53.295,-53.565,-12
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,21.948,78.052,39.407,60.593,6.36,384,1.000,bicubic,-54.014,-53.857,-29
regnetx_008.pycls_in1k,21.948,78.052,38.932,61.068,7.26,224,0.875,bicubic,-53.082,-53.406,-8
semnasnet_100.rmsp_in1k,21.887,78.113,38.600,61.400,3.89,224,0.875,bicubic,-53.563,-53.994,-16
pit_ti_224.in1k,21.877,78.123,39.527,60.473,4.85,224,0.900,bicubic,-51.033,-51.873,+22
pvt_v2_b0.in1k,21.836,78.164,40.152,59.848,3.67,224,0.900,bicubic,-48.824,-50.044,+40
regnetx_006.pycls_in1k,21.747,78.253,38.914,61.086,6.20,224,0.875,bicubic,-52.121,-52.764,+7
vit_tiny_patch16_384.augreg_in21k_ft_in1k,21.718,78.282,39.327,60.673,5.79,384,1.000,bicubic,-56.706,-55.213,-142
crossvit_9_240.in1k,21.694,78.306,39.258,60.742,8.55,240,0.875,bicubic,-52.264,-52.710,+3
vgg19_bn.tv_in1k,21.623,78.376,39.276,60.724,143.68,224,0.875,bilinear,-52.592,-52.568,-3
ghostnet_100.in1k,21.623,78.376,38.708,61.292,5.18,224,0.875,bilinear,-52.358,-52.758,+1
semnasnet_075.rmsp_in1k,21.576,78.424,38.924,61.076,2.91,224,0.875,bicubic,-51.416,-52.214,+12
resnet18.gluon_in1k,21.547,78.453,38.873,61.127,11.69,224,0.875,bicubic,-49.291,-50.881,+32
mobilevitv2_075.cvnets_in1k,21.535,78.465,38.631,61.369,2.87,256,0.888,bicubic,-54.073,-54.113,-33
fbnetc_100.rmsp_in1k,21.500,78.500,38.181,61.819,5.57,224,0.875,bilinear,-53.630,-54.207,-24
xcit_nano_12_p16_224.fb_in1k,21.437,78.563,39.791,60.209,3.05,224,1.000,bicubic,-48.525,-49.971,+34
mnasnet_100.rmsp_in1k,21.346,78.653,37.709,62.291,4.38,224,0.875,bicubic,-53.304,-54.414,-16
resnet18.fb_ssl_yfcc100m_ft_in1k,21.290,78.710,39.099,60.901,11.69,224,0.875,bilinear,-51.304,-52.319,+9
lcnet_100.ra2_in1k,21.290,78.710,38.873,61.127,2.95,224,0.875,bicubic,-50.806,-51.489,+19
mixnet_s.ft_in1k,21.278,78.722,38.199,61.801,4.13,224,0.875,bicubic,-54.716,-55.071,-47
legacy_seresnext26_32x4d.in1k,21.087,78.913,37.635,62.365,16.79,224,0.875,bicubic,-56.021,-55.679,-81
crossvit_tiny_240.in1k,21.052,78.948,38.067,61.933,7.01,240,0.875,bicubic,-52.284,-53.841,-3
resnet18.a2_in1k,20.944,79.056,36.851,63.149,11.69,288,1.000,bicubic,-51.428,-53.745,+8
regnetx_004.pycls_in1k,20.889,79.111,37.533,62.467,5.16,224,0.875,bicubic,-51.509,-53.295,+5
spnasnet_100.rmsp_in1k,20.871,79.129,37.890,62.110,4.42,224,0.875,bilinear,-53.215,-53.930,-16
legacy_seresnet18.in1k,20.847,79.153,37.639,62.361,11.78,224,0.875,bicubic,-50.919,-52.693,+15
seresnext26t_32x4d.bt_in1k,20.847,79.153,36.344,63.656,16.81,288,0.950,bicubic,-57.897,-57.968,-185
mobilenetv2_100.ra_in1k,20.757,79.243,37.764,62.236,3.50,224,0.875,bicubic,-52.219,-53.246,-3
tf_mixnet_s.in1k,20.476,79.524,36.627,63.373,4.13,224,0.875,bicubic,-55.176,-56.009,-50
vit_tiny_patch16_224.augreg_in21k_ft_in1k,20.458,79.542,37.601,62.399,5.72,224,0.900,bicubic,-54.996,-55.243,-43
regnety_004.pycls_in1k,20.413,79.587,37.014,62.986,4.34,224,0.875,bicubic,-53.615,-54.736,-21
tf_mobilenetv3_large_075.in1k,20.372,79.628,36.784,63.216,3.99,224,0.875,bilinear,-53.060,-54.568,-15
hrnet_w18_small.ms_in1k,20.364,79.636,37.090,62.910,13.19,224,0.875,bilinear,-51.974,-53.587,-1
resnet26d.bt_in1k,20.266,79.734,36.348,63.652,16.01,288,0.950,bicubic,-57.142,-57.290,-110
resnet18.tv_in1k,20.230,79.770,37.260,62.740,11.69,224,0.875,bilinear,-49.530,-51.810,+17
mixer_l16_224.goog_in21k_ft_in1k,20.175,79.825,32.938,67.062,208.20,224,0.875,bicubic,-51.879,-54.736,+2
deit_tiny_patch16_224.fb_in1k,20.142,79.858,37.541,62.459,5.72,224,0.900,bicubic,-52.030,-53.571,-1
tf_mobilenetv3_large_minimal_100.in1k,20.103,79.897,36.894,63.106,3.92,224,0.875,bilinear,-52.155,-53.744,-4
seresnext26d_32x4d.bt_in1k,20.063,79.937,35.233,64.766,16.81,288,0.950,bicubic,-58.751,-59.006,-203
vgg16_bn.tv_in1k,19.945,80.055,36.314,63.686,138.37,224,0.875,bilinear,-53.425,-55.200,-22
resnet26.bt_in1k,19.739,80.261,35.839,64.161,16.00,288,0.950,bicubic,-56.627,-57.341,-75
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,19.334,80.666,36.059,63.941,6.34,224,0.900,bicubic,-52.464,-54.765,-2
tinynet_c.in1k,19.260,80.740,35.982,64.018,2.46,184,0.875,bicubic,-51.980,-53.752,+1
edgenext_xx_small.in1k,18.863,81.137,35.159,64.841,1.33,288,1.000,bicubic,-53.015,-55.393,-5
resnet18.a3_in1k,18.450,81.550,33.485,66.515,11.69,224,0.950,bicubic,-49.804,-54.691,+13
mobilevit_xs.cvnets_in1k,18.312,81.688,33.192,66.808,2.32,256,0.900,bicubic,-56.324,-59.154,-43
lcnet_075.ra2_in1k,18.128,81.872,34.371,65.629,2.36,224,0.875,bicubic,-50.654,-53.989,+8
vgg19.tv_in1k,17.941,82.059,33.058,66.942,143.67,224,0.875,bilinear,-54.437,-57.816,-18
vgg13_bn.tv_in1k,17.798,82.202,34.043,65.957,133.05,224,0.875,bilinear,-53.790,-56.335,-6
vgg16.tv_in1k,17.536,82.464,32.779,67.221,138.36,224,0.875,bilinear,-54.056,-57.605,-8
regnety_002.pycls_in1k,17.460,82.540,32.433,67.567,3.16,224,0.875,bicubic,-52.818,-57.095,-3
vgg11_bn.tv_in1k,17.399,82.601,33.001,66.999,132.87,224,0.875,bilinear,-52.983,-56.807,-5
mobilevitv2_050.cvnets_in1k,17.308,82.692,33.003,66.997,1.37,256,0.888,bicubic,-52.830,-56.917,-4
regnetx_002.pycls_in1k,16.953,83.047,32.241,67.759,2.68,224,0.875,bicubic,-51.793,-56.295,+2
mobilenetv3_small_100.lamb_in1k,16.805,83.195,32.516,67.484,2.54,224,0.875,bicubic,-50.851,-55.120,+6
resnet10t.c3_in1k,16.699,83.301,32.123,67.877,5.44,224,0.950,bicubic,-51.659,-55.917,+1
mobilenetv2_050.lamb_in1k,16.666,83.334,31.962,68.038,1.97,224,0.875,bicubic,-49.284,-54.126,+8
tinynet_d.in1k,16.660,83.340,32.451,67.549,2.34,152,0.875,bicubic,-50.314,-54.611,+4
mnasnet_small.lamb_in1k,16.638,83.362,31.909,68.091,2.03,224,0.875,bicubic,-49.558,-54.595,+4
dla60x_c.in1k,16.330,83.670,31.747,68.252,1.32,224,0.875,bilinear,-51.568,-56.674,0
tf_mobilenetv3_small_100.in1k,16.216,83.784,31.205,68.795,2.54,224,0.875,bilinear,-51.702,-56.469,-2
vgg13.tv_in1k,16.096,83.904,30.985,69.015,133.05,224,0.875,bilinear,-53.836,-58.265,-11
resnet14t.c3_in1k,15.925,84.075,30.003,69.997,10.08,224,0.950,bicubic,-56.329,-60.299,-28
vgg11.tv_in1k,15.723,84.278,30.451,69.549,132.86,224,0.875,bilinear,-53.300,-58.173,-11
mobilenetv3_small_075.lamb_in1k,14.952,85.048,29.727,70.273,2.04,224,0.875,bicubic,-50.282,-55.719,+2
tf_mobilenetv3_small_075.in1k,14.932,85.067,29.566,70.434,2.04,224,0.875,bilinear,-50.795,-56.560,0
dla46_c.in1k,14.665,85.335,29.397,70.603,1.30,224,0.875,bilinear,-50.207,-56.901,+1
mobilevit_xxs.cvnets_in1k,14.492,85.508,28.659,71.341,1.27,256,0.900,bicubic,-54.440,-60.283,-14
dla46x_c.in1k,14.380,85.620,29.189,70.811,1.07,224,0.875,bilinear,-51.608,-57.791,-5
lcnet_050.ra2_in1k,14.304,85.696,28.653,71.347,1.88,224,0.875,bicubic,-48.816,-55.733,-1
tf_mobilenetv3_small_minimal_100.in1k,13.966,86.034,27.988,72.012,2.04,224,0.875,bilinear,-48.924,-56.252,-1
tinynet_e.in1k,12.671,87.329,26.381,73.619,2.04,106,0.875,bicubic,-47.195,-55.383,-1
mobilenetv3_small_050.lamb_in1k,11.040,88.960,23.477,76.523,1.59,224,0.875,bicubic,-46.870,-56.703,-1
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_layers.py | import torch
import torch.nn as nn
from timm.layers import create_act_layer, set_layer_config
class MLP(nn.Module):
def __init__(self, act_layer="relu", inplace=True):
super(MLP, self).__init__()
self.fc1 = nn.Linear(1000, 100)
self.act = create_act_layer(act_layer, inplace=inplace)
self.fc2 = nn.Linear(100, 10)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
return x
def _run_act_layer_grad(act_type, inplace=True):
x = torch.rand(10, 1000) * 10
m = MLP(act_layer=act_type, inplace=inplace)
def _run(x, act_layer=''):
if act_layer:
# replace act layer if set
m.act = create_act_layer(act_layer, inplace=inplace)
out = m(x)
l = (out - 0).pow(2).sum()
return l
out_me = _run(x)
with set_layer_config(scriptable=True):
out_jit = _run(x, act_type)
assert torch.isclose(out_jit, out_me)
with set_layer_config(no_jit=True):
out_basic = _run(x, act_type)
assert torch.isclose(out_basic, out_jit)
def test_swish_grad():
for _ in range(100):
_run_act_layer_grad('swish')
def test_mish_grad():
for _ in range(100):
_run_act_layer_grad('mish')
def test_hard_sigmoid_grad():
for _ in range(100):
_run_act_layer_grad('hard_sigmoid', inplace=None)
def test_hard_swish_grad():
for _ in range(100):
_run_act_layer_grad('hard_swish')
def test_hard_mish_grad():
for _ in range(100):
_run_act_layer_grad('hard_mish')
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_models.py | """Run tests for all models
Tests that run on CI should have a specific marker, e.g. @pytest.mark.base. This
marker is used to parallelize the CI runs, with one runner for each marker.
If new tests are added, ensure that they use one of the existing markers
(documented in pyproject.toml > pytest > markers) or that a new marker is added
for this set of tests. If using a new marker, adjust the test matrix in
.github/workflows/tests.yml to run tests with this new marker, otherwise the
tests will be skipped on CI.
"""
import pytest
import torch
import platform
import os
import fnmatch
_IS_MAC = platform.system() == 'Darwin'
try:
from torchvision.models.feature_extraction import create_feature_extractor, get_graph_node_names, NodePathTracer
has_fx_feature_extraction = True
except ImportError:
has_fx_feature_extraction = False
import timm
from timm import list_models, create_model, set_scriptable, get_pretrained_cfg_value
from timm.layers import Format, get_spatial_dim, get_channel_dim
from timm.models import get_notrace_modules, get_notrace_functions
if hasattr(torch._C, '_jit_set_profiling_executor'):
# legacy executor is too slow to compile large models for unit tests
# no need for the fusion performance here
torch._C._jit_set_profiling_executor(True)
torch._C._jit_set_profiling_mode(False)
# transformer models don't support many of the spatial / feature based model functionalities
NON_STD_FILTERS = [
'vit_*', 'tnt_*', 'pit_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit*',
'poolformer_*', 'volo_*', 'sequencer2d_*', 'pvt_v2*', 'mvitv2*', 'gcvit*', 'efficientformer*',
'eva_*', 'flexivit*', 'eva02*', 'samvit_*'
]
NUM_NON_STD = len(NON_STD_FILTERS)
# exclude models that cause specific test failures
if 'GITHUB_ACTIONS' in os.environ:
# GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
EXCLUDE_FILTERS = [
'*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*50x3_bitm',
'*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*',
'*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', '*huge*', '*giant*', '*gigantic*',
'*enormous*', 'maxvit_xlarge*', 'regnet*1280', 'regnet*2560']
NON_STD_EXCLUDE_FILTERS = ['*huge*', '*giant*', '*gigantic*', '*enormous*']
else:
EXCLUDE_FILTERS = ['*enormous*']
NON_STD_EXCLUDE_FILTERS = ['*gigantic*', '*enormous*']
EXCLUDE_JIT_FILTERS = []
TARGET_FWD_SIZE = MAX_FWD_SIZE = 384
TARGET_BWD_SIZE = 128
MAX_BWD_SIZE = 320
MAX_FWD_OUT_SIZE = 448
TARGET_JIT_SIZE = 128
MAX_JIT_SIZE = 320
TARGET_FFEAT_SIZE = 96
MAX_FFEAT_SIZE = 256
TARGET_FWD_FX_SIZE = 128
MAX_FWD_FX_SIZE = 256
TARGET_BWD_FX_SIZE = 128
MAX_BWD_FX_SIZE = 224
def _get_input_size(model=None, model_name='', target=None):
if model is None:
assert model_name, "One of model or model_name must be provided"
input_size = get_pretrained_cfg_value(model_name, 'input_size')
fixed_input_size = get_pretrained_cfg_value(model_name, 'fixed_input_size')
min_input_size = get_pretrained_cfg_value(model_name, 'min_input_size')
else:
default_cfg = model.default_cfg
input_size = default_cfg['input_size']
fixed_input_size = default_cfg.get('fixed_input_size', None)
min_input_size = default_cfg.get('min_input_size', None)
assert input_size is not None
if fixed_input_size:
return input_size
if min_input_size:
if target and max(input_size) > target:
input_size = min_input_size
else:
if target and max(input_size) > target:
input_size = tuple([min(x, target) for x in input_size])
return input_size
@pytest.mark.base
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False)
model.eval()
input_size = _get_input_size(model=model, target=TARGET_FWD_SIZE)
if max(input_size) > MAX_FWD_SIZE:
pytest.skip("Fixed input size model > limit.")
inputs = torch.randn((batch_size, *input_size))
outputs = model(inputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
@pytest.mark.base
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [2])
def test_model_backward(model_name, batch_size):
"""Run a single forward pass with each model"""
input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_SIZE)
if max(input_size) > MAX_BWD_SIZE:
pytest.skip("Fixed input size model > limit.")
model = create_model(model_name, pretrained=False, num_classes=42)
num_params = sum([x.numel() for x in model.parameters()])
model.train()
inputs = torch.randn((batch_size, *input_size))
outputs = model(inputs)
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
outputs.mean().backward()
for n, x in model.named_parameters():
assert x.grad is not None, f'No gradient for {n}'
num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
assert outputs.shape[-1] == 42
assert num_params == num_grad, 'Some parameters are missing gradients'
assert not torch.isnan(outputs).any(), 'Output included NaNs'
@pytest.mark.cfg
@pytest.mark.timeout(300)
@pytest.mark.parametrize('model_name', list_models(
exclude_filters=EXCLUDE_FILTERS + NON_STD_FILTERS, include_tags=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_default_cfgs(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False)
model.eval()
state_dict = model.state_dict()
cfg = model.default_cfg
pool_size = cfg['pool_size']
input_size = model.default_cfg['input_size']
output_fmt = getattr(model, 'output_fmt', 'NCHW')
spatial_axis = get_spatial_dim(output_fmt)
assert len(spatial_axis) == 2 # TODO add 1D sequence support
feat_axis = get_channel_dim(output_fmt)
if all([x <= MAX_FWD_OUT_SIZE for x in input_size]) and \
not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
# output sizes only checked if default res <= 448 * 448 to keep resource down
input_size = tuple([min(x, MAX_FWD_OUT_SIZE) for x in input_size])
input_tensor = torch.randn((batch_size, *input_size))
# test forward_features (always unpooled)
outputs = model.forward_features(input_tensor)
assert outputs.shape[spatial_axis[0]] == pool_size[0], 'unpooled feature shape != config'
assert outputs.shape[spatial_axis[1]] == pool_size[1], 'unpooled feature shape != config'
if not isinstance(model, (timm.models.MobileNetV3, timm.models.GhostNet, timm.models.VGG)):
assert outputs.shape[feat_axis] == model.num_features
# test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
model.reset_classifier(0)
outputs = model.forward(input_tensor)
assert len(outputs.shape) == 2
assert outputs.shape[1] == model.num_features
# test model forward without pooling and classifier
model.reset_classifier(0, '') # reset classifier and set global pooling to pass-through
outputs = model.forward(input_tensor)
assert len(outputs.shape) == 4
if not isinstance(model, (timm.models.MobileNetV3, timm.models.GhostNet, timm.models.VGG)):
# mobilenetv3/ghostnet/vgg forward_features vs removed pooling differ due to location or lack of GAP
assert outputs.shape[spatial_axis[0]] == pool_size[0] and outputs.shape[spatial_axis[1]] == pool_size[1]
if 'pruned' not in model_name: # FIXME better pruned model handling
# test classifier + global pool deletion via __init__
model = create_model(model_name, pretrained=False, num_classes=0, global_pool='').eval()
outputs = model.forward(input_tensor)
assert len(outputs.shape) == 4
if not isinstance(model, (timm.models.MobileNetV3, timm.models.GhostNet, timm.models.VGG)):
assert outputs.shape[spatial_axis[0]] == pool_size[0] and outputs.shape[spatial_axis[1]] == pool_size[1]
# check classifier name matches default_cfg
if cfg.get('num_classes', None):
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
# check first conv(s) names match default_cfg
first_conv = cfg['first_conv']
if isinstance(first_conv, str):
first_conv = (first_conv,)
assert isinstance(first_conv, (tuple, list))
for fc in first_conv:
assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
@pytest.mark.cfg
@pytest.mark.timeout(300)
@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS, exclude_filters=NON_STD_EXCLUDE_FILTERS, include_tags=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_default_cfgs_non_std(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False)
model.eval()
state_dict = model.state_dict()
cfg = model.default_cfg
input_size = _get_input_size(model=model)
if max(input_size) > 320: # FIXME const
pytest.skip("Fixed input size model > limit.")
input_tensor = torch.randn((batch_size, *input_size))
feat_dim = getattr(model, 'feature_dim', None)
outputs = model.forward_features(input_tensor)
if isinstance(outputs, (tuple, list)):
# cannot currently verify multi-tensor output.
pass
else:
if feat_dim is None:
feat_dim = -1 if outputs.ndim == 3 else 1
assert outputs.shape[feat_dim] == model.num_features
# test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
model.reset_classifier(0)
outputs = model.forward(input_tensor)
if isinstance(outputs, (tuple, list)):
outputs = outputs[0]
if feat_dim is None:
feat_dim = -1 if outputs.ndim == 3 else 1
assert outputs.shape[feat_dim] == model.num_features, 'pooled num_features != config'
model = create_model(model_name, pretrained=False, num_classes=0).eval()
outputs = model.forward(input_tensor)
if isinstance(outputs, (tuple, list)):
outputs = outputs[0]
if feat_dim is None:
feat_dim = -1 if outputs.ndim == 3 else 1
assert outputs.shape[feat_dim] == model.num_features
# check classifier name matches default_cfg
if cfg.get('num_classes', None):
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
# check first conv(s) names match default_cfg
first_conv = cfg['first_conv']
if isinstance(first_conv, str):
first_conv = (first_conv,)
assert isinstance(first_conv, (tuple, list))
for fc in first_conv:
assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
if 'GITHUB_ACTIONS' not in os.environ:
@pytest.mark.timeout(240)
@pytest.mark.parametrize('model_name', list_models(pretrained=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_load_pretrained(model_name, batch_size):
"""Create that pretrained weights load, verify support for in_chans != 3 while doing so."""
in_chans = 3 if 'pruned' in model_name else 1 # pruning not currently supported with in_chans change
create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5)
create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=0)
@pytest.mark.timeout(240)
@pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_features_pretrained(model_name, batch_size):
"""Create that pretrained weights load when features_only==True."""
create_model(model_name, pretrained=True, features_only=True)
@pytest.mark.torchscript
@pytest.mark.timeout(120)
@pytest.mark.parametrize(
'model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_torchscript(model_name, batch_size):
"""Run a single forward pass with each model"""
input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
if max(input_size) > MAX_JIT_SIZE:
pytest.skip("Fixed input size model > limit.")
with set_scriptable(True):
model = create_model(model_name, pretrained=False)
model.eval()
model = torch.jit.script(model)
outputs = model(torch.randn((batch_size, *input_size)))
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
EXCLUDE_FEAT_FILTERS = [
'*pruned*', # hopefully fix at some point
] + NON_STD_FILTERS
if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system():
# GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
EXCLUDE_FEAT_FILTERS += ['*resnext101_32x32d', '*resnext101_32x16d']
@pytest.mark.features
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS, include_tags=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_features(model_name, batch_size):
"""Run a single forward pass with each model in feature extraction mode"""
model = create_model(model_name, pretrained=False, features_only=True)
model.eval()
expected_channels = model.feature_info.channels()
expected_reduction = model.feature_info.reduction()
assert len(expected_channels) >= 4 # all models here should have at least 4 feature levels by default, some 5 or 6
input_size = _get_input_size(model=model, target=TARGET_FFEAT_SIZE)
if max(input_size) > MAX_FFEAT_SIZE:
pytest.skip("Fixed input size model > limit.")
output_fmt = getattr(model, 'output_fmt', 'NCHW')
feat_axis = get_channel_dim(output_fmt)
spatial_axis = get_spatial_dim(output_fmt)
import math
outputs = model(torch.randn((batch_size, *input_size)))
assert len(expected_channels) == len(outputs)
spatial_size = input_size[-2:]
for e, r, o in zip(expected_channels, expected_reduction, outputs):
assert e == o.shape[feat_axis]
assert o.shape[spatial_axis[0]] <= math.ceil(spatial_size[0] / r) + 1
assert o.shape[spatial_axis[1]] <= math.ceil(spatial_size[1] / r) + 1
assert o.shape[0] == batch_size
assert not torch.isnan(o).any()
def _create_fx_model(model, train=False):
# This block of code does a bit of juggling to handle any case where there are multiple outputs in train mode
# So we trace once and look at the graph, and get the indices of the nodes that lead into the original fx output
# node. Then we use those indices to select from train_nodes returned by torchvision get_graph_node_names
tracer_kwargs = dict(
leaf_modules=get_notrace_modules(),
autowrap_functions=get_notrace_functions(),
#enable_cpatching=True,
param_shapes_constant=True
)
train_nodes, eval_nodes = get_graph_node_names(model, tracer_kwargs=tracer_kwargs)
eval_return_nodes = [eval_nodes[-1]]
train_return_nodes = [train_nodes[-1]]
if train:
tracer = NodePathTracer(**tracer_kwargs)
graph = tracer.trace(model)
graph_nodes = list(reversed(graph.nodes))
output_node_names = [n.name for n in graph_nodes[0]._input_nodes.keys()]
graph_node_names = [n.name for n in graph_nodes]
output_node_indices = [-graph_node_names.index(node_name) for node_name in output_node_names]
train_return_nodes = [train_nodes[ix] for ix in output_node_indices]
fx_model = create_feature_extractor(
model,
train_return_nodes=train_return_nodes,
eval_return_nodes=eval_return_nodes,
tracer_kwargs=tracer_kwargs,
)
return fx_model
EXCLUDE_FX_FILTERS = ['vit_gi*']
# not enough memory to run fx on more models than other tests
if 'GITHUB_ACTIONS' in os.environ:
EXCLUDE_FX_FILTERS += [
'beit_large*',
'mixer_l*',
'*nfnet_f2*',
'*resnext101_32x32d',
'resnetv2_152x2*',
'resmlp_big*',
'resnetrs270',
'swin_large*',
'vgg*',
'vit_large*',
'vit_base_patch8*',
'xcit_large*',
]
@pytest.mark.fxforward
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_fx(model_name, batch_size):
"""
Symbolically trace each model and run single forward pass through the resulting GraphModule
Also check that the output of a forward pass through the GraphModule is the same as that from the original Module
"""
if not has_fx_feature_extraction:
pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
model = create_model(model_name, pretrained=False)
model.eval()
input_size = _get_input_size(model=model, target=TARGET_FWD_FX_SIZE)
if max(input_size) > MAX_FWD_FX_SIZE:
pytest.skip("Fixed input size model > limit.")
with torch.no_grad():
inputs = torch.randn((batch_size, *input_size))
outputs = model(inputs)
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
model = _create_fx_model(model)
fx_outputs = tuple(model(inputs).values())
if isinstance(fx_outputs, tuple):
fx_outputs = torch.cat(fx_outputs)
assert torch.all(fx_outputs == outputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
@pytest.mark.fxbackward
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(
exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [2])
def test_model_backward_fx(model_name, batch_size):
"""Symbolically trace each model and run single backward pass through the resulting GraphModule"""
if not has_fx_feature_extraction:
pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_FX_SIZE)
if max(input_size) > MAX_BWD_FX_SIZE:
pytest.skip("Fixed input size model > limit.")
model = create_model(model_name, pretrained=False, num_classes=42)
model.train()
num_params = sum([x.numel() for x in model.parameters()])
if 'GITHUB_ACTIONS' in os.environ and num_params > 100e6:
pytest.skip("Skipping FX backward test on model with more than 100M params.")
model = _create_fx_model(model, train=True)
outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
outputs.mean().backward()
for n, x in model.named_parameters():
assert x.grad is not None, f'No gradient for {n}'
num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
assert outputs.shape[-1] == 42
assert num_params == num_grad, 'Some parameters are missing gradients'
assert not torch.isnan(outputs).any(), 'Output included NaNs'
if 'GITHUB_ACTIONS' not in os.environ:
# FIXME this test is causing GitHub actions to run out of RAM and abruptly kill the test process
# reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
EXCLUDE_FX_JIT_FILTERS = [
'deit_*_distilled_patch16_224',
'levit*',
'pit_*_distilled_224',
] + EXCLUDE_FX_FILTERS
@pytest.mark.timeout(120)
@pytest.mark.parametrize(
'model_name', list_models(
exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward_fx_torchscript(model_name, batch_size):
"""Symbolically trace each model, script it, and run single forward pass"""
if not has_fx_feature_extraction:
pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
if max(input_size) > MAX_JIT_SIZE:
pytest.skip("Fixed input size model > limit.")
with set_scriptable(True):
model = create_model(model_name, pretrained=False)
model.eval()
model = torch.jit.script(_create_fx_model(model))
with torch.no_grad():
outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_optim.py | """ Optimzier Tests
These tests were adapted from PyTorch' optimizer tests.
"""
import math
import pytest
import functools
from copy import deepcopy
import torch
from torch.testing._internal.common_utils import TestCase
from torch.autograd import Variable
from timm.scheduler import PlateauLRScheduler
from timm.optim import create_optimizer_v2
# HACK relying on internal PyTorch test functionality for comparisons that I don't want to write
torch_tc = TestCase()
def _test_basic_cases_template(weight, bias, input, constructor, scheduler_constructors):
weight = Variable(weight, requires_grad=True)
bias = Variable(bias, requires_grad=True)
input = Variable(input)
optimizer = constructor(weight, bias)
schedulers = []
for scheduler_constructor in scheduler_constructors:
schedulers.append(scheduler_constructor(optimizer))
# to check if the optimizer can be printed as a string
optimizer.__repr__()
def fn():
optimizer.zero_grad()
y = weight.mv(input)
if y.is_cuda and bias.is_cuda and y.get_device() != bias.get_device():
y = y.cuda(bias.get_device())
loss = (y + bias).pow(2).sum()
loss.backward()
return loss
initial_value = fn().item()
for _i in range(200):
for scheduler in schedulers:
if isinstance(scheduler, PlateauLRScheduler):
val_loss = fn()
scheduler.step(val_loss)
else:
scheduler.step()
optimizer.step(fn)
assert fn().item() < initial_value
def _test_state_dict(weight, bias, input, constructor):
weight = Variable(weight, requires_grad=True)
bias = Variable(bias, requires_grad=True)
input = Variable(input)
def fn_base(optimizer, weight, bias):
optimizer.zero_grad()
i = input_cuda if weight.is_cuda else input
loss = (weight.mv(i) + bias).pow(2).sum()
loss.backward()
return loss
optimizer = constructor(weight, bias)
fn = functools.partial(fn_base, optimizer, weight, bias)
# Prime the optimizer
for _i in range(20):
optimizer.step(fn)
# Clone the weights and construct new optimizer for them
weight_c = Variable(weight.data.clone(), requires_grad=True)
bias_c = Variable(bias.data.clone(), requires_grad=True)
optimizer_c = constructor(weight_c, bias_c)
fn_c = functools.partial(fn_base, optimizer_c, weight_c, bias_c)
# Load state dict
state_dict = deepcopy(optimizer.state_dict())
state_dict_c = deepcopy(optimizer.state_dict())
optimizer_c.load_state_dict(state_dict_c)
# Run both optimizations in parallel
for _i in range(20):
optimizer.step(fn)
optimizer_c.step(fn_c)
#assert torch.equal(weight, weight_c)
#assert torch.equal(bias, bias_c)
torch_tc.assertEqual(weight, weight_c)
torch_tc.assertEqual(bias, bias_c)
# Make sure state dict wasn't modified
torch_tc.assertEqual(state_dict, state_dict_c)
# Make sure state dict is deterministic with equal but not identical parameters
torch_tc.assertEqual(optimizer.state_dict(), optimizer_c.state_dict())
# Make sure repeated parameters have identical representation in state dict
optimizer_c.param_groups.extend(optimizer_c.param_groups)
torch_tc.assertEqual(optimizer.state_dict()['param_groups'][-1], optimizer_c.state_dict()['param_groups'][-1])
# Check that state dict can be loaded even when we cast parameters
# to a different type and move to a different device.
if not torch.cuda.is_available():
return
input_cuda = Variable(input.data.float().cuda())
weight_cuda = Variable(weight.data.float().cuda(), requires_grad=True)
bias_cuda = Variable(bias.data.float().cuda(), requires_grad=True)
optimizer_cuda = constructor(weight_cuda, bias_cuda)
fn_cuda = functools.partial(fn_base, optimizer_cuda, weight_cuda, bias_cuda)
state_dict = deepcopy(optimizer.state_dict())
state_dict_c = deepcopy(optimizer.state_dict())
optimizer_cuda.load_state_dict(state_dict_c)
# Make sure state dict wasn't modified
torch_tc.assertEqual(state_dict, state_dict_c)
for _i in range(20):
optimizer.step(fn)
optimizer_cuda.step(fn_cuda)
torch_tc.assertEqual(weight, weight_cuda)
torch_tc.assertEqual(bias, bias_cuda)
# validate deepcopy() copies all public attributes
def getPublicAttr(obj):
return set(k for k in obj.__dict__ if not k.startswith('_'))
assert getPublicAttr(optimizer) == getPublicAttr(deepcopy(optimizer))
def _test_basic_cases(constructor, scheduler_constructors=None):
if scheduler_constructors is None:
scheduler_constructors = []
_test_state_dict(
torch.randn(10, 5),
torch.randn(10),
torch.randn(5),
constructor
)
_test_basic_cases_template(
torch.randn(10, 5),
torch.randn(10),
torch.randn(5),
constructor,
scheduler_constructors
)
# non-contiguous parameters
_test_basic_cases_template(
torch.randn(10, 5, 2)[..., 0],
torch.randn(10, 2)[..., 0],
torch.randn(5),
constructor,
scheduler_constructors
)
# CUDA
if not torch.cuda.is_available():
return
_test_basic_cases_template(
torch.randn(10, 5).cuda(),
torch.randn(10).cuda(),
torch.randn(5).cuda(),
constructor,
scheduler_constructors
)
def _test_model(optimizer, params, device=torch.device('cpu')):
weight = torch.tensor(
[[-0.2109, -0.4976], [-0.1413, -0.3420], [-0.2524, 0.6976]],
device=device, requires_grad=True)
bias = torch.tensor([-0.1085, -0.2979, 0.6892], device=device, requires_grad=True)
weight2 = torch.tensor([[-0.0508, -0.3941, -0.2843]], device=device, requires_grad=True)
bias2 = torch.tensor([-0.0711], device=device, requires_grad=True)
input = torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], device=device).reshape(3, 2)
model = torch.nn.Sequential(torch.nn.Linear(2, 3),
torch.nn.Sigmoid(),
torch.nn.Linear(3, 1),
torch.nn.Sigmoid())
model.to(device)
pretrained_dict = model.state_dict()
pretrained_dict['0.weight'] = weight
pretrained_dict['0.bias'] = bias
pretrained_dict['2.weight'] = weight2
pretrained_dict['2.bias'] = bias2
model.load_state_dict(pretrained_dict)
optimizer = create_optimizer_v2(model, opt=optimizer, **params)
prev_loss = float('inf')
for i in range(20):
optimizer.zero_grad()
output = model(input)
loss = output.sum()
loss.backward()
loss = loss.item()
assert loss < prev_loss
prev_loss = loss
optimizer.step()
def rosenbrock(tensor):
x, y = tensor
return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2
def drosenbrock(tensor):
x, y = tensor
return torch.tensor((-400 * x * (y - x ** 2) - 2 * (1 - x), 200 * (y - x ** 2)))
def _test_rosenbrock(constructor, scheduler_constructors=None):
if scheduler_constructors is None:
scheduler_constructors = []
params_t = torch.tensor([1.5, 1.5])
params = Variable(params_t, requires_grad=True)
optimizer = constructor([params])
schedulers = []
for scheduler_constructor in scheduler_constructors:
schedulers.append(scheduler_constructor(optimizer))
solution = torch.tensor([1, 1])
initial_dist = params.data.dist(solution)
def eval(params, w):
# Depending on w, provide only the x or y gradient
optimizer.zero_grad()
loss = rosenbrock(params)
loss.backward()
grad = drosenbrock(params.data)
# NB: We torture test the optimizer by returning an
# uncoalesced sparse tensor
if w:
i = torch.LongTensor([[0, 0]])
x = grad[0]
v = torch.tensor([x / 4., x - x / 4.])
else:
i = torch.LongTensor([[1, 1]])
y = grad[1]
v = torch.tensor([y - y / 4., y / 4.])
x = torch.sparse.DoubleTensor(i, v, torch.Size([2])).to(dtype=v.dtype)
with torch.no_grad():
params.grad = x.to_dense()
return loss
for i in range(2000):
# Do cyclic coordinate descent
w = i % 2
optimizer.step(functools.partial(eval, params, w))
for scheduler in schedulers:
if isinstance(scheduler, PlateauLRScheduler):
scheduler.step(rosenbrock(params))
else:
scheduler.step()
torch_tc.assertLessEqual(params.data.dist(solution), initial_dist)
def _build_params_dict(weight, bias, **kwargs):
return [{'params': [weight]}, dict(params=[bias], **kwargs)]
def _build_params_dict_single(weight, bias, **kwargs):
return [dict(params=bias, **kwargs)]
#@pytest.mark.parametrize('optimizer', ['sgd', 'momentum'])
# FIXME momentum variant frequently fails in GitHub runner, but never local after many attempts
@pytest.mark.parametrize('optimizer', ['sgd'])
def test_sgd(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=1e-2),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=1e-2),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=1e-2), optimizer)
)
# _test_basic_cases(
# lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3),
# [lambda opt: StepLR(opt, gamma=0.9, step_size=10)]
# )
# _test_basic_cases(
# lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3),
# [lambda opt: WarmUpLR(opt, warmup_factor=0.4, warmup_iters=4, warmup_method="linear")]
# )
# _test_basic_cases(
# lambda weight, bias: optimizer([weight, bias], lr=1e-3),
# [lambda opt: WarmUpLR(opt, warmup_factor=0.4, warmup_iters=4, warmup_method="constant")]
# )
# _test_basic_cases(
# lambda weight, bias: optimizer([weight, bias], lr=1e-3),
# [lambda opt: StepLR(opt, gamma=0.9, step_size=10),
# lambda opt: WarmUpLR(opt, warmup_factor=0.4, warmup_iters=4)]
# )
# _test_basic_cases(
# lambda weight, bias: optimizer([weight, bias], lr=1e-3),
# [lambda opt: StepLR(opt, gamma=0.9, step_size=10),
# lambda opt: ReduceLROnPlateau(opt)]
# )
# _test_basic_cases(
# lambda weight, bias: optimizer([weight, bias], lr=1e-3),
# [lambda opt: StepLR(opt, gamma=0.99, step_size=10),
# lambda opt: ExponentialLR(opt, gamma=0.99),
# lambda opt: ReduceLROnPlateau(opt)]
# )
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=3e-3, momentum=1)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=3e-3, momentum=1, weight_decay=.1)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
)
_test_model(optimizer, dict(lr=1e-3))
@pytest.mark.parametrize('optimizer', ['adamw', 'adam', 'nadam', 'adamax'])
def test_adam(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=5e-2)
)
_test_model(optimizer, dict(lr=5e-2))
@pytest.mark.parametrize('optimizer', ['adabelief'])
def test_adabelief(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3), optimizer)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3, weight_decay=1)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=5e-2)
)
_test_model(optimizer, dict(lr=5e-2))
@pytest.mark.parametrize('optimizer', ['radam', 'radabelief'])
def test_rectified(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
)
_test_model(optimizer, dict(lr=1e-3))
@pytest.mark.parametrize('optimizer', ['adadelta', 'adagrad'])
def test_adaother(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3), optimizer)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3, weight_decay=1)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=1e-1)
)
_test_model(optimizer, dict(lr=5e-2))
@pytest.mark.parametrize('optimizer', ['adafactor'])
def test_adafactor(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(_build_params_dict_single(weight, bias), optimizer)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3, weight_decay=1)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=5e-2)
)
_test_model(optimizer, dict(lr=5e-2))
@pytest.mark.parametrize('optimizer', ['lamb', 'lambc'])
def test_lamb(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=1e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=1e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=1e-3), optimizer)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
)
_test_model(optimizer, dict(lr=1e-3))
@pytest.mark.parametrize('optimizer', ['lars', 'larc', 'nlars', 'nlarc'])
def test_lars(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=1e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=1e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=1e-3), optimizer)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
)
_test_model(optimizer, dict(lr=1e-3))
@pytest.mark.parametrize('optimizer', ['madgrad', 'madgradw'])
def test_madgrad(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3), optimizer)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=1e-2)
)
_test_model(optimizer, dict(lr=1e-2))
@pytest.mark.parametrize('optimizer', ['novograd'])
def test_novograd(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3), optimizer)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
)
_test_model(optimizer, dict(lr=1e-3))
@pytest.mark.parametrize('optimizer', ['rmsprop', 'rmsproptf'])
def test_rmsprop(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3), optimizer)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=1e-2)
)
_test_model(optimizer, dict(lr=1e-2))
@pytest.mark.parametrize('optimizer', ['adamp'])
def test_adamp(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3), optimizer)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=5e-2)
)
_test_model(optimizer, dict(lr=5e-2))
@pytest.mark.parametrize('optimizer', ['sgdp'])
def test_sgdp(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3), optimizer)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
)
_test_model(optimizer, dict(lr=1e-3))
@pytest.mark.parametrize('optimizer', ['lookahead_sgd', 'lookahead_momentum'])
def test_lookahead_sgd(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3), optimizer)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
)
@pytest.mark.parametrize('optimizer', ['lookahead_adamw', 'lookahead_adam'])
def test_lookahead_adam(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3), optimizer)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=5e-2)
)
@pytest.mark.parametrize('optimizer', ['lookahead_radam'])
def test_lookahead_radam(optimizer):
_test_basic_cases(
lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3),
optimizer,
lr=1e-3)
)
_test_basic_cases(
lambda weight, bias: create_optimizer_v2(
_build_params_dict_single(weight, bias, lr=3e-3), optimizer)
)
_test_rosenbrock(
lambda params: create_optimizer_v2(params, optimizer, lr=1e-4)
)
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/tests/test_utils.py | from torch.nn.modules.batchnorm import BatchNorm2d
from torchvision.ops.misc import FrozenBatchNorm2d
import timm
from timm.utils.model import freeze, unfreeze
def test_freeze_unfreeze():
model = timm.create_model('resnet18')
# Freeze all
freeze(model)
# Check top level module
assert model.fc.weight.requires_grad == False
# Check submodule
assert model.layer1[0].conv1.weight.requires_grad == False
# Check BN
assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d)
# Unfreeze all
unfreeze(model)
# Check top level module
assert model.fc.weight.requires_grad == True
# Check submodule
assert model.layer1[0].conv1.weight.requires_grad == True
# Check BN
assert isinstance(model.layer1[0].bn1, BatchNorm2d)
# Freeze some
freeze(model, ['layer1', 'layer2.0'])
# Check frozen
assert model.layer1[0].conv1.weight.requires_grad == False
assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d)
assert model.layer2[0].conv1.weight.requires_grad == False
# Check not frozen
assert model.layer3[0].conv1.weight.requires_grad == True
assert isinstance(model.layer3[0].bn1, BatchNorm2d)
assert model.layer2[1].conv1.weight.requires_grad == True
# Unfreeze some
unfreeze(model, ['layer1', 'layer2.0'])
# Check not frozen
assert model.layer1[0].conv1.weight.requires_grad == True
assert isinstance(model.layer1[0].bn1, BatchNorm2d)
assert model.layer2[0].conv1.weight.requires_grad == True
# Freeze/unfreeze BN
# From root
freeze(model, ['layer1.0.bn1'])
assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d)
unfreeze(model, ['layer1.0.bn1'])
assert isinstance(model.layer1[0].bn1, BatchNorm2d)
# From direct parent
freeze(model.layer1[0], ['bn1'])
assert isinstance(model.layer1[0].bn1, FrozenBatchNorm2d)
unfreeze(model.layer1[0], ['bn1'])
assert isinstance(model.layer1[0].bn1, BatchNorm2d) | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/timm/__init__.py | from .version import __version__
from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable
from .models import create_model, list_models, list_pretrained, is_model, list_modules, model_entrypoint, \
is_model_pretrained, get_pretrained_cfg, get_pretrained_cfg_value
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/timm/version.py | __version__ = '0.9.5'
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/__init__.py | from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\
rand_augment_transform, auto_augment_transform
from .config import resolve_data_config, resolve_model_data_config
from .constants import *
from .dataset import ImageDataset, IterableImageDataset, AugMixDataset
from .dataset_factory import create_dataset
from .dataset_info import DatasetInfo, CustomDatasetInfo
from .imagenet_info import ImageNetInfo, infer_imagenet_subset
from .loader import create_loader
from .mixup import Mixup, FastCollateMixup
from .readers import create_reader
from .readers import get_img_extensions, is_img_extension, set_img_extensions, add_img_extensions, del_img_extensions
from .real_labels import RealLabelsImagenet
from .transforms import *
from .transforms_factory import create_transform
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/auto_augment.py | """ AutoAugment, RandAugment, AugMix, and 3-Augment for PyTorch
This code implements the searched ImageNet policies with various tweaks and improvements and
does not include any of the search code.
AA and RA Implementation adapted from:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py
AugMix adapted from:
https://github.com/google-research/augmix
3-Augment based on: https://github.com/facebookresearch/deit/blob/main/README_revenge.md
Papers:
AutoAugment: Learning Augmentation Policies from Data - https://arxiv.org/abs/1805.09501
Learning Data Augmentation Strategies for Object Detection - https://arxiv.org/abs/1906.11172
RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781
3-Augment: DeiT III: Revenge of the ViT - https://arxiv.org/abs/2204.07118
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import re
from functools import partial
from typing import Dict, List, Optional, Union
from PIL import Image, ImageOps, ImageEnhance, ImageChops, ImageFilter
import PIL
import numpy as np
_PIL_VER = tuple([int(x) for x in PIL.__version__.split('.')[:2]])
_FILL = (128, 128, 128)
_LEVEL_DENOM = 10. # denominator for conversion from 'Mx' magnitude scale to fractional aug level for op arguments
_HPARAMS_DEFAULT = dict(
translate_const=250,
img_mean=_FILL,
)
if hasattr(Image, "Resampling"):
_RANDOM_INTERPOLATION = (Image.Resampling.BILINEAR, Image.Resampling.BICUBIC)
_DEFAULT_INTERPOLATION = Image.Resampling.BICUBIC
else:
_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
_DEFAULT_INTERPOLATION = Image.BICUBIC
def _interpolation(kwargs):
interpolation = kwargs.pop('resample', _DEFAULT_INTERPOLATION)
if isinstance(interpolation, (list, tuple)):
return random.choice(interpolation)
return interpolation
def _check_args_tf(kwargs):
if 'fillcolor' in kwargs and _PIL_VER < (5, 0):
kwargs.pop('fillcolor')
kwargs['resample'] = _interpolation(kwargs)
def shear_x(img, factor, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, factor, 0, 0, 1, 0), **kwargs)
def shear_y(img, factor, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, factor, 1, 0), **kwargs)
def translate_x_rel(img, pct, **kwargs):
pixels = pct * img.size[0]
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
def translate_y_rel(img, pct, **kwargs):
pixels = pct * img.size[1]
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
def translate_x_abs(img, pixels, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
def translate_y_abs(img, pixels, **kwargs):
_check_args_tf(kwargs)
return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
def rotate(img, degrees, **kwargs):
_check_args_tf(kwargs)
if _PIL_VER >= (5, 2):
return img.rotate(degrees, **kwargs)
if _PIL_VER >= (5, 0):
w, h = img.size
post_trans = (0, 0)
rotn_center = (w / 2.0, h / 2.0)
angle = -math.radians(degrees)
matrix = [
round(math.cos(angle), 15),
round(math.sin(angle), 15),
0.0,
round(-math.sin(angle), 15),
round(math.cos(angle), 15),
0.0,
]
def transform(x, y, matrix):
(a, b, c, d, e, f) = matrix
return a * x + b * y + c, d * x + e * y + f
matrix[2], matrix[5] = transform(
-rotn_center[0] - post_trans[0], -rotn_center[1] - post_trans[1], matrix
)
matrix[2] += rotn_center[0]
matrix[5] += rotn_center[1]
return img.transform(img.size, Image.AFFINE, matrix, **kwargs)
return img.rotate(degrees, resample=kwargs['resample'])
def auto_contrast(img, **__):
return ImageOps.autocontrast(img)
def invert(img, **__):
return ImageOps.invert(img)
def equalize(img, **__):
return ImageOps.equalize(img)
def solarize(img, thresh, **__):
return ImageOps.solarize(img, thresh)
def solarize_add(img, add, thresh=128, **__):
lut = []
for i in range(256):
if i < thresh:
lut.append(min(255, i + add))
else:
lut.append(i)
if img.mode in ("L", "RGB"):
if img.mode == "RGB" and len(lut) == 256:
lut = lut + lut + lut
return img.point(lut)
return img
def posterize(img, bits_to_keep, **__):
if bits_to_keep >= 8:
return img
return ImageOps.posterize(img, bits_to_keep)
def contrast(img, factor, **__):
return ImageEnhance.Contrast(img).enhance(factor)
def color(img, factor, **__):
return ImageEnhance.Color(img).enhance(factor)
def brightness(img, factor, **__):
return ImageEnhance.Brightness(img).enhance(factor)
def sharpness(img, factor, **__):
return ImageEnhance.Sharpness(img).enhance(factor)
def gaussian_blur(img, factor, **__):
img = img.filter(ImageFilter.GaussianBlur(radius=factor))
return img
def gaussian_blur_rand(img, factor, **__):
radius_min = 0.1
radius_max = 2.0
img = img.filter(ImageFilter.GaussianBlur(radius=random.uniform(radius_min, radius_max * factor)))
return img
def desaturate(img, factor, **_):
factor = min(1., max(0., 1. - factor))
# enhance factor 0 = grayscale, 1.0 = no-change
return ImageEnhance.Color(img).enhance(factor)
def _randomly_negate(v):
"""With 50% prob, negate the value"""
return -v if random.random() > 0.5 else v
def _rotate_level_to_arg(level, _hparams):
# range [-30, 30]
level = (level / _LEVEL_DENOM) * 30.
level = _randomly_negate(level)
return level,
def _enhance_level_to_arg(level, _hparams):
# range [0.1, 1.9]
return (level / _LEVEL_DENOM) * 1.8 + 0.1,
def _enhance_increasing_level_to_arg(level, _hparams):
# the 'no change' level is 1.0, moving away from that towards 0. or 2.0 increases the enhancement blend
# range [0.1, 1.9] if level <= _LEVEL_DENOM
level = (level / _LEVEL_DENOM) * .9
level = max(0.1, 1.0 + _randomly_negate(level)) # keep it >= 0.1
return level,
def _minmax_level_to_arg(level, _hparams, min_val=0., max_val=1.0, clamp=True):
level = (level / _LEVEL_DENOM)
level = min_val + (max_val - min_val) * level
if clamp:
level = max(min_val, min(max_val, level))
return level,
def _shear_level_to_arg(level, _hparams):
# range [-0.3, 0.3]
level = (level / _LEVEL_DENOM) * 0.3
level = _randomly_negate(level)
return level,
def _translate_abs_level_to_arg(level, hparams):
translate_const = hparams['translate_const']
level = (level / _LEVEL_DENOM) * float(translate_const)
level = _randomly_negate(level)
return level,
def _translate_rel_level_to_arg(level, hparams):
# default range [-0.45, 0.45]
translate_pct = hparams.get('translate_pct', 0.45)
level = (level / _LEVEL_DENOM) * translate_pct
level = _randomly_negate(level)
return level,
def _posterize_level_to_arg(level, _hparams):
# As per Tensorflow TPU EfficientNet impl
# range [0, 4], 'keep 0 up to 4 MSB of original image'
# intensity/severity of augmentation decreases with level
return int((level / _LEVEL_DENOM) * 4),
def _posterize_increasing_level_to_arg(level, hparams):
# As per Tensorflow models research and UDA impl
# range [4, 0], 'keep 4 down to 0 MSB of original image',
# intensity/severity of augmentation increases with level
return 4 - _posterize_level_to_arg(level, hparams)[0],
def _posterize_original_level_to_arg(level, _hparams):
# As per original AutoAugment paper description
# range [4, 8], 'keep 4 up to 8 MSB of image'
# intensity/severity of augmentation decreases with level
return int((level / _LEVEL_DENOM) * 4) + 4,
def _solarize_level_to_arg(level, _hparams):
# range [0, 256]
# intensity/severity of augmentation decreases with level
return min(256, int((level / _LEVEL_DENOM) * 256)),
def _solarize_increasing_level_to_arg(level, _hparams):
# range [0, 256]
# intensity/severity of augmentation increases with level
return 256 - _solarize_level_to_arg(level, _hparams)[0],
def _solarize_add_level_to_arg(level, _hparams):
# range [0, 110]
return min(128, int((level / _LEVEL_DENOM) * 110)),
LEVEL_TO_ARG = {
'AutoContrast': None,
'Equalize': None,
'Invert': None,
'Rotate': _rotate_level_to_arg,
# There are several variations of the posterize level scaling in various Tensorflow/Google repositories/papers
'Posterize': _posterize_level_to_arg,
'PosterizeIncreasing': _posterize_increasing_level_to_arg,
'PosterizeOriginal': _posterize_original_level_to_arg,
'Solarize': _solarize_level_to_arg,
'SolarizeIncreasing': _solarize_increasing_level_to_arg,
'SolarizeAdd': _solarize_add_level_to_arg,
'Color': _enhance_level_to_arg,
'ColorIncreasing': _enhance_increasing_level_to_arg,
'Contrast': _enhance_level_to_arg,
'ContrastIncreasing': _enhance_increasing_level_to_arg,
'Brightness': _enhance_level_to_arg,
'BrightnessIncreasing': _enhance_increasing_level_to_arg,
'Sharpness': _enhance_level_to_arg,
'SharpnessIncreasing': _enhance_increasing_level_to_arg,
'ShearX': _shear_level_to_arg,
'ShearY': _shear_level_to_arg,
'TranslateX': _translate_abs_level_to_arg,
'TranslateY': _translate_abs_level_to_arg,
'TranslateXRel': _translate_rel_level_to_arg,
'TranslateYRel': _translate_rel_level_to_arg,
'Desaturate': partial(_minmax_level_to_arg, min_val=0.5, max_val=1.0),
'GaussianBlur': partial(_minmax_level_to_arg, min_val=0.1, max_val=2.0),
'GaussianBlurRand': _minmax_level_to_arg,
}
NAME_TO_OP = {
'AutoContrast': auto_contrast,
'Equalize': equalize,
'Invert': invert,
'Rotate': rotate,
'Posterize': posterize,
'PosterizeIncreasing': posterize,
'PosterizeOriginal': posterize,
'Solarize': solarize,
'SolarizeIncreasing': solarize,
'SolarizeAdd': solarize_add,
'Color': color,
'ColorIncreasing': color,
'Contrast': contrast,
'ContrastIncreasing': contrast,
'Brightness': brightness,
'BrightnessIncreasing': brightness,
'Sharpness': sharpness,
'SharpnessIncreasing': sharpness,
'ShearX': shear_x,
'ShearY': shear_y,
'TranslateX': translate_x_abs,
'TranslateY': translate_y_abs,
'TranslateXRel': translate_x_rel,
'TranslateYRel': translate_y_rel,
'Desaturate': desaturate,
'GaussianBlur': gaussian_blur,
'GaussianBlurRand': gaussian_blur_rand,
}
class AugmentOp:
def __init__(self, name, prob=0.5, magnitude=10, hparams=None):
hparams = hparams or _HPARAMS_DEFAULT
self.name = name
self.aug_fn = NAME_TO_OP[name]
self.level_fn = LEVEL_TO_ARG[name]
self.prob = prob
self.magnitude = magnitude
self.hparams = hparams.copy()
self.kwargs = dict(
fillcolor=hparams['img_mean'] if 'img_mean' in hparams else _FILL,
resample=hparams['interpolation'] if 'interpolation' in hparams else _RANDOM_INTERPOLATION,
)
# If magnitude_std is > 0, we introduce some randomness
# in the usually fixed policy and sample magnitude from a normal distribution
# with mean `magnitude` and std-dev of `magnitude_std`.
# NOTE This is my own hack, being tested, not in papers or reference impls.
# If magnitude_std is inf, we sample magnitude from a uniform distribution
self.magnitude_std = self.hparams.get('magnitude_std', 0)
self.magnitude_max = self.hparams.get('magnitude_max', None)
def __call__(self, img):
if self.prob < 1.0 and random.random() > self.prob:
return img
magnitude = self.magnitude
if self.magnitude_std > 0:
# magnitude randomization enabled
if self.magnitude_std == float('inf'):
# inf == uniform sampling
magnitude = random.uniform(0, magnitude)
elif self.magnitude_std > 0:
magnitude = random.gauss(magnitude, self.magnitude_std)
# default upper_bound for the timm RA impl is _LEVEL_DENOM (10)
# setting magnitude_max overrides this to allow M > 10 (behaviour closer to Google TF RA impl)
upper_bound = self.magnitude_max or _LEVEL_DENOM
magnitude = max(0., min(magnitude, upper_bound))
level_args = self.level_fn(magnitude, self.hparams) if self.level_fn is not None else tuple()
return self.aug_fn(img, *level_args, **self.kwargs)
def __repr__(self):
fs = self.__class__.__name__ + f'(name={self.name}, p={self.prob}'
fs += f', m={self.magnitude}, mstd={self.magnitude_std}'
if self.magnitude_max is not None:
fs += f', mmax={self.magnitude_max}'
fs += ')'
return fs
def auto_augment_policy_v0(hparams):
# ImageNet v0 policy from TPU EfficientNet impl, cannot find a paper reference.
policy = [
[('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
[('Color', 0.4, 9), ('Equalize', 0.6, 3)],
[('Color', 0.4, 1), ('Rotate', 0.6, 8)],
[('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
[('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
[('Color', 0.2, 0), ('Equalize', 0.8, 8)],
[('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
[('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
[('Color', 0.6, 1), ('Equalize', 1.0, 2)],
[('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
[('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
[('Color', 0.4, 7), ('Equalize', 0.6, 0)],
[('Posterize', 0.4, 6), ('AutoContrast', 0.4, 7)],
[('Solarize', 0.6, 8), ('Color', 0.6, 9)],
[('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
[('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
[('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
[('ShearY', 0.8, 0), ('Color', 0.6, 4)],
[('Color', 1.0, 0), ('Rotate', 0.6, 2)],
[('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
[('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
[('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
[('Posterize', 0.8, 2), ('Solarize', 0.6, 10)], # This results in black image with Tpu posterize
[('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
[('Color', 0.8, 6), ('Rotate', 0.4, 5)],
]
pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
return pc
def auto_augment_policy_v0r(hparams):
# ImageNet v0 policy from TPU EfficientNet impl, with variation of Posterize used
# in Google research implementation (number of bits discarded increases with magnitude)
policy = [
[('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
[('Color', 0.4, 9), ('Equalize', 0.6, 3)],
[('Color', 0.4, 1), ('Rotate', 0.6, 8)],
[('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
[('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
[('Color', 0.2, 0), ('Equalize', 0.8, 8)],
[('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
[('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
[('Color', 0.6, 1), ('Equalize', 1.0, 2)],
[('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
[('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
[('Color', 0.4, 7), ('Equalize', 0.6, 0)],
[('PosterizeIncreasing', 0.4, 6), ('AutoContrast', 0.4, 7)],
[('Solarize', 0.6, 8), ('Color', 0.6, 9)],
[('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
[('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
[('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
[('ShearY', 0.8, 0), ('Color', 0.6, 4)],
[('Color', 1.0, 0), ('Rotate', 0.6, 2)],
[('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
[('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
[('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
[('PosterizeIncreasing', 0.8, 2), ('Solarize', 0.6, 10)],
[('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
[('Color', 0.8, 6), ('Rotate', 0.4, 5)],
]
pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
return pc
def auto_augment_policy_original(hparams):
# ImageNet policy from https://arxiv.org/abs/1805.09501
policy = [
[('PosterizeOriginal', 0.4, 8), ('Rotate', 0.6, 9)],
[('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
[('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
[('PosterizeOriginal', 0.6, 7), ('PosterizeOriginal', 0.6, 6)],
[('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
[('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],
[('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],
[('PosterizeOriginal', 0.8, 5), ('Equalize', 1.0, 2)],
[('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
[('Equalize', 0.6, 8), ('PosterizeOriginal', 0.4, 6)],
[('Rotate', 0.8, 8), ('Color', 0.4, 0)],
[('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],
[('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],
[('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
[('Color', 0.6, 4), ('Contrast', 1.0, 8)],
[('Rotate', 0.8, 8), ('Color', 1.0, 2)],
[('Color', 0.8, 8), ('Solarize', 0.8, 7)],
[('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],
[('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],
[('Color', 0.4, 0), ('Equalize', 0.6, 3)],
[('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
[('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
[('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
[('Color', 0.6, 4), ('Contrast', 1.0, 8)],
[('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
]
pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
return pc
def auto_augment_policy_originalr(hparams):
# ImageNet policy from https://arxiv.org/abs/1805.09501 with research posterize variation
policy = [
[('PosterizeIncreasing', 0.4, 8), ('Rotate', 0.6, 9)],
[('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
[('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
[('PosterizeIncreasing', 0.6, 7), ('PosterizeIncreasing', 0.6, 6)],
[('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
[('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],
[('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],
[('PosterizeIncreasing', 0.8, 5), ('Equalize', 1.0, 2)],
[('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
[('Equalize', 0.6, 8), ('PosterizeIncreasing', 0.4, 6)],
[('Rotate', 0.8, 8), ('Color', 0.4, 0)],
[('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],
[('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],
[('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
[('Color', 0.6, 4), ('Contrast', 1.0, 8)],
[('Rotate', 0.8, 8), ('Color', 1.0, 2)],
[('Color', 0.8, 8), ('Solarize', 0.8, 7)],
[('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],
[('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],
[('Color', 0.4, 0), ('Equalize', 0.6, 3)],
[('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
[('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
[('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
[('Color', 0.6, 4), ('Contrast', 1.0, 8)],
[('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
]
pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
return pc
def auto_augment_policy_3a(hparams):
policy = [
[('Solarize', 1.0, 5)], # 128 solarize threshold @ 5 magnitude
[('Desaturate', 1.0, 10)], # grayscale at 10 magnitude
[('GaussianBlurRand', 1.0, 10)],
]
pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
return pc
def auto_augment_policy(name='v0', hparams=None):
hparams = hparams or _HPARAMS_DEFAULT
if name == 'original':
return auto_augment_policy_original(hparams)
if name == 'originalr':
return auto_augment_policy_originalr(hparams)
if name == 'v0':
return auto_augment_policy_v0(hparams)
if name == 'v0r':
return auto_augment_policy_v0r(hparams)
if name == '3a':
return auto_augment_policy_3a(hparams)
assert False, f'Unknown AA policy {name}'
class AutoAugment:
def __init__(self, policy):
self.policy = policy
def __call__(self, img):
sub_policy = random.choice(self.policy)
for op in sub_policy:
img = op(img)
return img
def __repr__(self):
fs = self.__class__.__name__ + '(policy='
for p in self.policy:
fs += '\n\t['
fs += ', '.join([str(op) for op in p])
fs += ']'
fs += ')'
return fs
def auto_augment_transform(config_str: str, hparams: Optional[Dict] = None):
"""
Create a AutoAugment transform
Args:
config_str: String defining configuration of auto augmentation. Consists of multiple sections separated by
dashes ('-').
The first section defines the AutoAugment policy (one of 'v0', 'v0r', 'original', 'originalr').
The remaining sections:
'mstd' - float std deviation of magnitude noise applied
Ex 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5
hparams: Other hparams (kwargs) for the AutoAugmentation scheme
Returns:
A PyTorch compatible Transform
"""
config = config_str.split('-')
policy_name = config[0]
config = config[1:]
for c in config:
cs = re.split(r'(\d.*)', c)
if len(cs) < 2:
continue
key, val = cs[:2]
if key == 'mstd':
# noise param injected via hparams for now
hparams.setdefault('magnitude_std', float(val))
else:
assert False, 'Unknown AutoAugment config section'
aa_policy = auto_augment_policy(policy_name, hparams=hparams)
return AutoAugment(aa_policy)
_RAND_TRANSFORMS = [
'AutoContrast',
'Equalize',
'Invert',
'Rotate',
'Posterize',
'Solarize',
'SolarizeAdd',
'Color',
'Contrast',
'Brightness',
'Sharpness',
'ShearX',
'ShearY',
'TranslateXRel',
'TranslateYRel',
# 'Cutout' # NOTE I've implement this as random erasing separately
]
_RAND_INCREASING_TRANSFORMS = [
'AutoContrast',
'Equalize',
'Invert',
'Rotate',
'PosterizeIncreasing',
'SolarizeIncreasing',
'SolarizeAdd',
'ColorIncreasing',
'ContrastIncreasing',
'BrightnessIncreasing',
'SharpnessIncreasing',
'ShearX',
'ShearY',
'TranslateXRel',
'TranslateYRel',
# 'Cutout' # NOTE I've implement this as random erasing separately
]
_RAND_3A = [
'SolarizeIncreasing',
'Desaturate',
'GaussianBlur',
]
_RAND_WEIGHTED_3A = {
'SolarizeIncreasing': 6,
'Desaturate': 6,
'GaussianBlur': 6,
'Rotate': 3,
'ShearX': 2,
'ShearY': 2,
'PosterizeIncreasing': 1,
'AutoContrast': 1,
'ColorIncreasing': 1,
'SharpnessIncreasing': 1,
'ContrastIncreasing': 1,
'BrightnessIncreasing': 1,
'Equalize': 1,
'Invert': 1,
}
# These experimental weights are based loosely on the relative improvements mentioned in paper.
# They may not result in increased performance, but could likely be tuned to so.
_RAND_WEIGHTED_0 = {
'Rotate': 3,
'ShearX': 2,
'ShearY': 2,
'TranslateXRel': 1,
'TranslateYRel': 1,
'ColorIncreasing': .25,
'SharpnessIncreasing': 0.25,
'AutoContrast': 0.25,
'SolarizeIncreasing': .05,
'SolarizeAdd': .05,
'ContrastIncreasing': .05,
'BrightnessIncreasing': .05,
'Equalize': .05,
'PosterizeIncreasing': 0.05,
'Invert': 0.05,
}
def _get_weighted_transforms(transforms: Dict):
transforms, probs = list(zip(*transforms.items()))
probs = np.array(probs)
probs = probs / np.sum(probs)
return transforms, probs
def rand_augment_choices(name: str, increasing=True):
if name == 'weights':
return _RAND_WEIGHTED_0
if name == '3aw':
return _RAND_WEIGHTED_3A
if name == '3a':
return _RAND_3A
return _RAND_INCREASING_TRANSFORMS if increasing else _RAND_TRANSFORMS
def rand_augment_ops(
magnitude: Union[int, float] = 10,
prob: float = 0.5,
hparams: Optional[Dict] = None,
transforms: Optional[Union[Dict, List]] = None,
):
hparams = hparams or _HPARAMS_DEFAULT
transforms = transforms or _RAND_TRANSFORMS
return [AugmentOp(
name, prob=prob, magnitude=magnitude, hparams=hparams) for name in transforms]
class RandAugment:
def __init__(self, ops, num_layers=2, choice_weights=None):
self.ops = ops
self.num_layers = num_layers
self.choice_weights = choice_weights
def __call__(self, img):
# no replacement when using weighted choice
ops = np.random.choice(
self.ops,
self.num_layers,
replace=self.choice_weights is None,
p=self.choice_weights,
)
for op in ops:
img = op(img)
return img
def __repr__(self):
fs = self.__class__.__name__ + f'(n={self.num_layers}, ops='
for op in self.ops:
fs += f'\n\t{op}'
fs += ')'
return fs
def rand_augment_transform(
config_str: str,
hparams: Optional[Dict] = None,
transforms: Optional[Union[str, Dict, List]] = None,
):
"""
Create a RandAugment transform
Args:
config_str (str): String defining configuration of random augmentation. Consists of multiple sections separated
by dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand').
The remaining sections, not order sepecific determine
'm' - integer magnitude of rand augment
'n' - integer num layers (number of transform ops selected per image)
'p' - float probability of applying each layer (default 0.5)
'mstd' - float std deviation of magnitude noise applied, or uniform sampling if infinity (or > 100)
'mmax' - set upper bound for magnitude to something other than default of _LEVEL_DENOM (10)
'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0)
't' - str name of transform set to use
Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5
'rand-mstd1-tweights' results in mag std 1.0, weighted transforms, default mag of 10 and num_layers 2
hparams (dict): Other hparams (kwargs) for the RandAugmentation scheme
Returns:
A PyTorch compatible Transform
"""
magnitude = _LEVEL_DENOM # default to _LEVEL_DENOM for magnitude (currently 10)
num_layers = 2 # default to 2 ops per image
increasing = False
prob = 0.5
config = config_str.split('-')
assert config[0] == 'rand'
config = config[1:]
for c in config:
if c.startswith('t'):
# NOTE old 'w' key was removed, 'w0' is not equivalent to 'tweights'
val = str(c[1:])
if transforms is None:
transforms = val
else:
# numeric options
cs = re.split(r'(\d.*)', c)
if len(cs) < 2:
continue
key, val = cs[:2]
if key == 'mstd':
# noise param / randomization of magnitude values
mstd = float(val)
if mstd > 100:
# use uniform sampling in 0 to magnitude if mstd is > 100
mstd = float('inf')
hparams.setdefault('magnitude_std', mstd)
elif key == 'mmax':
# clip magnitude between [0, mmax] instead of default [0, _LEVEL_DENOM]
hparams.setdefault('magnitude_max', int(val))
elif key == 'inc':
if bool(val):
increasing = True
elif key == 'm':
magnitude = int(val)
elif key == 'n':
num_layers = int(val)
elif key == 'p':
prob = float(val)
else:
assert False, 'Unknown RandAugment config section'
if isinstance(transforms, str):
transforms = rand_augment_choices(transforms, increasing=increasing)
elif transforms is None:
transforms = _RAND_INCREASING_TRANSFORMS if increasing else _RAND_TRANSFORMS
choice_weights = None
if isinstance(transforms, Dict):
transforms, choice_weights = _get_weighted_transforms(transforms)
ra_ops = rand_augment_ops(magnitude=magnitude, prob=prob, hparams=hparams, transforms=transforms)
return RandAugment(ra_ops, num_layers, choice_weights=choice_weights)
_AUGMIX_TRANSFORMS = [
'AutoContrast',
'ColorIncreasing', # not in paper
'ContrastIncreasing', # not in paper
'BrightnessIncreasing', # not in paper
'SharpnessIncreasing', # not in paper
'Equalize',
'Rotate',
'PosterizeIncreasing',
'SolarizeIncreasing',
'ShearX',
'ShearY',
'TranslateXRel',
'TranslateYRel',
]
def augmix_ops(
magnitude: Union[int, float] = 10,
hparams: Optional[Dict] = None,
transforms: Optional[Union[str, Dict, List]] = None,
):
hparams = hparams or _HPARAMS_DEFAULT
transforms = transforms or _AUGMIX_TRANSFORMS
return [AugmentOp(
name,
prob=1.0,
magnitude=magnitude,
hparams=hparams
) for name in transforms]
class AugMixAugment:
""" AugMix Transform
Adapted and improved from impl here: https://github.com/google-research/augmix/blob/master/imagenet.py
From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty -
https://arxiv.org/abs/1912.02781
"""
def __init__(self, ops, alpha=1., width=3, depth=-1, blended=False):
self.ops = ops
self.alpha = alpha
self.width = width
self.depth = depth
self.blended = blended # blended mode is faster but not well tested
def _calc_blended_weights(self, ws, m):
ws = ws * m
cump = 1.
rws = []
for w in ws[::-1]:
alpha = w / cump
cump *= (1 - alpha)
rws.append(alpha)
return np.array(rws[::-1], dtype=np.float32)
def _apply_blended(self, img, mixing_weights, m):
# This is my first crack and implementing a slightly faster mixed augmentation. Instead
# of accumulating the mix for each chain in a Numpy array and then blending with original,
# it recomputes the blending coefficients and applies one PIL image blend per chain.
# TODO the results appear in the right ballpark but they differ by more than rounding.
img_orig = img.copy()
ws = self._calc_blended_weights(mixing_weights, m)
for w in ws:
depth = self.depth if self.depth > 0 else np.random.randint(1, 4)
ops = np.random.choice(self.ops, depth, replace=True)
img_aug = img_orig # no ops are in-place, deep copy not necessary
for op in ops:
img_aug = op(img_aug)
img = Image.blend(img, img_aug, w)
return img
def _apply_basic(self, img, mixing_weights, m):
# This is a literal adaptation of the paper/official implementation without normalizations and
# PIL <-> Numpy conversions between every op. It is still quite CPU compute heavy compared to the
# typical augmentation transforms, could use a GPU / Kornia implementation.
img_shape = img.size[0], img.size[1], len(img.getbands())
mixed = np.zeros(img_shape, dtype=np.float32)
for mw in mixing_weights:
depth = self.depth if self.depth > 0 else np.random.randint(1, 4)
ops = np.random.choice(self.ops, depth, replace=True)
img_aug = img # no ops are in-place, deep copy not necessary
for op in ops:
img_aug = op(img_aug)
mixed += mw * np.asarray(img_aug, dtype=np.float32)
np.clip(mixed, 0, 255., out=mixed)
mixed = Image.fromarray(mixed.astype(np.uint8))
return Image.blend(img, mixed, m)
def __call__(self, img):
mixing_weights = np.float32(np.random.dirichlet([self.alpha] * self.width))
m = np.float32(np.random.beta(self.alpha, self.alpha))
if self.blended:
mixed = self._apply_blended(img, mixing_weights, m)
else:
mixed = self._apply_basic(img, mixing_weights, m)
return mixed
def __repr__(self):
fs = self.__class__.__name__ + f'(alpha={self.alpha}, width={self.width}, depth={self.depth}, ops='
for op in self.ops:
fs += f'\n\t{op}'
fs += ')'
return fs
def augment_and_mix_transform(config_str: str, hparams: Optional[Dict] = None):
""" Create AugMix PyTorch transform
Args:
config_str (str): String defining configuration of random augmentation. Consists of multiple sections separated
by dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand').
The remaining sections, not order sepecific determine
'm' - integer magnitude (severity) of augmentation mix (default: 3)
'w' - integer width of augmentation chain (default: 3)
'd' - integer depth of augmentation chain (-1 is random [1, 3], default: -1)
'b' - integer (bool), blend each branch of chain into end result without a final blend, less CPU (default: 0)
'mstd' - float std deviation of magnitude noise applied (default: 0)
Ex 'augmix-m5-w4-d2' results in AugMix with severity 5, chain width 4, chain depth 2
hparams: Other hparams (kwargs) for the Augmentation transforms
Returns:
A PyTorch compatible Transform
"""
magnitude = 3
width = 3
depth = -1
alpha = 1.
blended = False
config = config_str.split('-')
assert config[0] == 'augmix'
config = config[1:]
for c in config:
cs = re.split(r'(\d.*)', c)
if len(cs) < 2:
continue
key, val = cs[:2]
if key == 'mstd':
# noise param injected via hparams for now
hparams.setdefault('magnitude_std', float(val))
elif key == 'm':
magnitude = int(val)
elif key == 'w':
width = int(val)
elif key == 'd':
depth = int(val)
elif key == 'a':
alpha = float(val)
elif key == 'b':
blended = bool(val)
else:
assert False, 'Unknown AugMix config section'
hparams.setdefault('magnitude_std', float('inf')) # default to uniform sampling (if not set via mstd arg)
ops = augmix_ops(magnitude=magnitude, hparams=hparams)
return AugMixAugment(ops, alpha=alpha, width=width, depth=depth, blended=blended)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/config.py | import logging
from .constants import *
_logger = logging.getLogger(__name__)
def resolve_data_config(
args=None,
pretrained_cfg=None,
model=None,
use_test_size=False,
verbose=False
):
assert model or args or pretrained_cfg, "At least one of model, args, or pretrained_cfg required for data config."
args = args or {}
pretrained_cfg = pretrained_cfg or {}
if not pretrained_cfg and model is not None and hasattr(model, 'pretrained_cfg'):
pretrained_cfg = model.pretrained_cfg
data_config = {}
# Resolve input/image size
in_chans = 3
if args.get('chans', None) is not None:
in_chans = args['chans']
input_size = (in_chans, 224, 224)
if args.get('input_size', None) is not None:
assert isinstance(args['input_size'], (tuple, list))
assert len(args['input_size']) == 3
input_size = tuple(args['input_size'])
in_chans = input_size[0] # input_size overrides in_chans
elif args.get('img_size', None) is not None:
assert isinstance(args['img_size'], int)
input_size = (in_chans, args['img_size'], args['img_size'])
else:
if use_test_size and pretrained_cfg.get('test_input_size', None) is not None:
input_size = pretrained_cfg['test_input_size']
elif pretrained_cfg.get('input_size', None) is not None:
input_size = pretrained_cfg['input_size']
data_config['input_size'] = input_size
# resolve interpolation method
data_config['interpolation'] = 'bicubic'
if args.get('interpolation', None):
data_config['interpolation'] = args['interpolation']
elif pretrained_cfg.get('interpolation', None):
data_config['interpolation'] = pretrained_cfg['interpolation']
# resolve dataset + model mean for normalization
data_config['mean'] = IMAGENET_DEFAULT_MEAN
if args.get('mean', None) is not None:
mean = tuple(args['mean'])
if len(mean) == 1:
mean = tuple(list(mean) * in_chans)
else:
assert len(mean) == in_chans
data_config['mean'] = mean
elif pretrained_cfg.get('mean', None):
data_config['mean'] = pretrained_cfg['mean']
# resolve dataset + model std deviation for normalization
data_config['std'] = IMAGENET_DEFAULT_STD
if args.get('std', None) is not None:
std = tuple(args['std'])
if len(std) == 1:
std = tuple(list(std) * in_chans)
else:
assert len(std) == in_chans
data_config['std'] = std
elif pretrained_cfg.get('std', None):
data_config['std'] = pretrained_cfg['std']
# resolve default inference crop
crop_pct = DEFAULT_CROP_PCT
if args.get('crop_pct', None):
crop_pct = args['crop_pct']
else:
if use_test_size and pretrained_cfg.get('test_crop_pct', None):
crop_pct = pretrained_cfg['test_crop_pct']
elif pretrained_cfg.get('crop_pct', None):
crop_pct = pretrained_cfg['crop_pct']
data_config['crop_pct'] = crop_pct
# resolve default crop percentage
crop_mode = DEFAULT_CROP_MODE
if args.get('crop_mode', None):
crop_mode = args['crop_mode']
elif pretrained_cfg.get('crop_mode', None):
crop_mode = pretrained_cfg['crop_mode']
data_config['crop_mode'] = crop_mode
if verbose:
_logger.info('Data processing configuration for current model + dataset:')
for n, v in data_config.items():
_logger.info('\t%s: %s' % (n, str(v)))
return data_config
def resolve_model_data_config(
model,
args=None,
pretrained_cfg=None,
use_test_size=False,
verbose=False,
):
""" Resolve Model Data Config
This is equivalent to resolve_data_config() but with arguments re-ordered to put model first.
Args:
model (nn.Module): the model instance
args (dict): command line arguments / configuration in dict form (overrides pretrained_cfg)
pretrained_cfg (dict): pretrained model config (overrides pretrained_cfg attached to model)
use_test_size (bool): use the test time input resolution (if one exists) instead of default train resolution
verbose (bool): enable extra logging of resolved values
Returns:
dictionary of config
"""
return resolve_data_config(
args=args,
pretrained_cfg=pretrained_cfg,
model=model,
use_test_size=use_test_size,
verbose=verbose,
)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/constants.py | DEFAULT_CROP_PCT = 0.875
DEFAULT_CROP_MODE = 'center'
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
IMAGENET_DPN_MEAN = (124 / 255, 117 / 255, 104 / 255)
IMAGENET_DPN_STD = tuple([1 / (.0167 * 255)] * 3)
OPENAI_CLIP_MEAN = (0.48145466, 0.4578275, 0.40821073)
OPENAI_CLIP_STD = (0.26862954, 0.26130258, 0.27577711)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/dataset.py | """ Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2019, Ross Wightman
"""
import io
import logging
from typing import Optional
import torch
import torch.utils.data as data
from PIL import Image
from .readers import create_reader
_logger = logging.getLogger(__name__)
_ERROR_RETRY = 50
class ImageDataset(data.Dataset):
def __init__(
self,
root,
reader=None,
split='train',
class_map=None,
load_bytes=False,
img_mode='RGB',
transform=None,
target_transform=None,
):
if reader is None or isinstance(reader, str):
reader = create_reader(
reader or '',
root=root,
split=split,
class_map=class_map
)
self.reader = reader
self.load_bytes = load_bytes
self.img_mode = img_mode
self.transform = transform
self.target_transform = target_transform
self._consecutive_errors = 0
def __getitem__(self, index):
img, target = self.reader[index]
try:
img = img.read() if self.load_bytes else Image.open(img)
except Exception as e:
_logger.warning(f'Skipped sample (index {index}, file {self.reader.filename(index)}). {str(e)}')
self._consecutive_errors += 1
if self._consecutive_errors < _ERROR_RETRY:
return self.__getitem__((index + 1) % len(self.reader))
else:
raise e
self._consecutive_errors = 0
if self.img_mode and not self.load_bytes:
img = img.convert(self.img_mode)
if self.transform is not None:
img = self.transform(img)
if target is None:
target = -1
elif self.target_transform is not None:
target = self.target_transform(target)
return img, target
def __len__(self):
return len(self.reader)
def filename(self, index, basename=False, absolute=False):
return self.reader.filename(index, basename, absolute)
def filenames(self, basename=False, absolute=False):
return self.reader.filenames(basename, absolute)
class IterableImageDataset(data.IterableDataset):
def __init__(
self,
root,
reader=None,
split='train',
class_map=None,
is_training=False,
batch_size=None,
seed=42,
repeats=0,
download=False,
transform=None,
target_transform=None,
):
assert reader is not None
if isinstance(reader, str):
self.reader = create_reader(
reader,
root=root,
split=split,
class_map=class_map,
is_training=is_training,
batch_size=batch_size,
seed=seed,
repeats=repeats,
download=download,
)
else:
self.reader = reader
self.transform = transform
self.target_transform = target_transform
self._consecutive_errors = 0
def __iter__(self):
for img, target in self.reader:
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
yield img, target
def __len__(self):
if hasattr(self.reader, '__len__'):
return len(self.reader)
else:
return 0
def set_epoch(self, count):
# TFDS and WDS need external epoch count for deterministic cross process shuffle
if hasattr(self.reader, 'set_epoch'):
self.reader.set_epoch(count)
def set_loader_cfg(
self,
num_workers: Optional[int] = None,
):
# TFDS and WDS readers need # workers for correct # samples estimate before loader processes created
if hasattr(self.reader, 'set_loader_cfg'):
self.reader.set_loader_cfg(num_workers=num_workers)
def filename(self, index, basename=False, absolute=False):
assert False, 'Filename lookup by index not supported, use filenames().'
def filenames(self, basename=False, absolute=False):
return self.reader.filenames(basename, absolute)
class AugMixDataset(torch.utils.data.Dataset):
"""Dataset wrapper to perform AugMix or other clean/augmentation mixes"""
def __init__(self, dataset, num_splits=2):
self.augmentation = None
self.normalize = None
self.dataset = dataset
if self.dataset.transform is not None:
self._set_transforms(self.dataset.transform)
self.num_splits = num_splits
def _set_transforms(self, x):
assert isinstance(x, (list, tuple)) and len(x) == 3, 'Expecting a tuple/list of 3 transforms'
self.dataset.transform = x[0]
self.augmentation = x[1]
self.normalize = x[2]
@property
def transform(self):
return self.dataset.transform
@transform.setter
def transform(self, x):
self._set_transforms(x)
def _normalize(self, x):
return x if self.normalize is None else self.normalize(x)
def __getitem__(self, i):
x, y = self.dataset[i] # all splits share the same dataset base transform
x_list = [self._normalize(x)] # first split only normalizes (this is the 'clean' split)
# run the full augmentation on the remaining splits
for _ in range(self.num_splits - 1):
x_list.append(self._normalize(self.augmentation(x)))
return tuple(x_list), y
def __len__(self):
return len(self.dataset)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/dataset_factory.py | """ Dataset Factory
Hacked together by / Copyright 2021, Ross Wightman
"""
import os
from torchvision.datasets import CIFAR100, CIFAR10, MNIST, KMNIST, FashionMNIST, ImageFolder
try:
from torchvision.datasets import Places365
has_places365 = True
except ImportError:
has_places365 = False
try:
from torchvision.datasets import INaturalist
has_inaturalist = True
except ImportError:
has_inaturalist = False
try:
from torchvision.datasets import QMNIST
has_qmnist = True
except ImportError:
has_qmnist = False
try:
from torchvision.datasets import ImageNet
has_imagenet = True
except ImportError:
has_imagenet = False
from .dataset import IterableImageDataset, ImageDataset
_TORCH_BASIC_DS = dict(
cifar10=CIFAR10,
cifar100=CIFAR100,
mnist=MNIST,
kmnist=KMNIST,
fashion_mnist=FashionMNIST,
)
_TRAIN_SYNONYM = dict(train=None, training=None)
_EVAL_SYNONYM = dict(val=None, valid=None, validation=None, eval=None, evaluation=None)
def _search_split(root, split):
# look for sub-folder with name of split in root and use that if it exists
split_name = split.split('[')[0]
try_root = os.path.join(root, split_name)
if os.path.exists(try_root):
return try_root
def _try(syn):
for s in syn:
try_root = os.path.join(root, s)
if os.path.exists(try_root):
return try_root
return root
if split_name in _TRAIN_SYNONYM:
root = _try(_TRAIN_SYNONYM)
elif split_name in _EVAL_SYNONYM:
root = _try(_EVAL_SYNONYM)
return root
def create_dataset(
name,
root,
split='validation',
search_split=True,
class_map=None,
load_bytes=False,
is_training=False,
download=False,
batch_size=None,
seed=42,
repeats=0,
**kwargs
):
""" Dataset factory method
In parenthesis after each arg are the type of dataset supported for each arg, one of:
* folder - default, timm folder (or tar) based ImageDataset
* torch - torchvision based datasets
* HFDS - Hugging Face Datasets
* TFDS - Tensorflow-datasets wrapper in IterabeDataset interface via IterableImageDataset
* WDS - Webdataset
* all - any of the above
Args:
name: dataset name, empty is okay for folder based datasets
root: root folder of dataset (all)
split: dataset split (all)
search_split: search for split specific child fold from root so one can specify
`imagenet/` instead of `/imagenet/val`, etc on cmd line / config. (folder, torch/folder)
class_map: specify class -> index mapping via text file or dict (folder)
load_bytes: load data, return images as undecoded bytes (folder)
download: download dataset if not present and supported (HFDS, TFDS, torch)
is_training: create dataset in train mode, this is different from the split.
For Iterable / TDFS it enables shuffle, ignored for other datasets. (TFDS, WDS)
batch_size: batch size hint for (TFDS, WDS)
seed: seed for iterable datasets (TFDS, WDS)
repeats: dataset repeats per iteration i.e. epoch (TFDS, WDS)
**kwargs: other args to pass to dataset
Returns:
Dataset object
"""
name = name.lower()
if name.startswith('torch/'):
name = name.split('/', 2)[-1]
torch_kwargs = dict(root=root, download=download, **kwargs)
if name in _TORCH_BASIC_DS:
ds_class = _TORCH_BASIC_DS[name]
use_train = split in _TRAIN_SYNONYM
ds = ds_class(train=use_train, **torch_kwargs)
elif name == 'inaturalist' or name == 'inat':
assert has_inaturalist, 'Please update to PyTorch 1.10, torchvision 0.11+ for Inaturalist'
target_type = 'full'
split_split = split.split('/')
if len(split_split) > 1:
target_type = split_split[0].split('_')
if len(target_type) == 1:
target_type = target_type[0]
split = split_split[-1]
if split in _TRAIN_SYNONYM:
split = '2021_train'
elif split in _EVAL_SYNONYM:
split = '2021_valid'
ds = INaturalist(version=split, target_type=target_type, **torch_kwargs)
elif name == 'places365':
assert has_places365, 'Please update to a newer PyTorch and torchvision for Places365 dataset.'
if split in _TRAIN_SYNONYM:
split = 'train-standard'
elif split in _EVAL_SYNONYM:
split = 'val'
ds = Places365(split=split, **torch_kwargs)
elif name == 'qmnist':
assert has_qmnist, 'Please update to a newer PyTorch and torchvision for QMNIST dataset.'
use_train = split in _TRAIN_SYNONYM
ds = QMNIST(train=use_train, **torch_kwargs)
elif name == 'imagenet':
assert has_imagenet, 'Please update to a newer PyTorch and torchvision for ImageNet dataset.'
if split in _EVAL_SYNONYM:
split = 'val'
ds = ImageNet(split=split, **torch_kwargs)
elif name == 'image_folder' or name == 'folder':
# in case torchvision ImageFolder is preferred over timm ImageDataset for some reason
if search_split and os.path.isdir(root):
# look for split specific sub-folder in root
root = _search_split(root, split)
ds = ImageFolder(root, **kwargs)
else:
assert False, f"Unknown torchvision dataset {name}"
elif name.startswith('hfds/'):
# NOTE right now, HF datasets default arrow format is a random-access Dataset,
# There will be a IterableDataset variant too, TBD
ds = ImageDataset(root, reader=name, split=split, class_map=class_map, **kwargs)
elif name.startswith('tfds/'):
ds = IterableImageDataset(
root,
reader=name,
split=split,
class_map=class_map,
is_training=is_training,
download=download,
batch_size=batch_size,
repeats=repeats,
seed=seed,
**kwargs
)
elif name.startswith('wds/'):
ds = IterableImageDataset(
root,
reader=name,
split=split,
class_map=class_map,
is_training=is_training,
batch_size=batch_size,
repeats=repeats,
seed=seed,
**kwargs
)
else:
# FIXME support more advance split cfg for ImageFolder/Tar datasets in the future
if search_split and os.path.isdir(root):
# look for split specific sub-folder in root
root = _search_split(root, split)
ds = ImageDataset(root, reader=name, class_map=class_map, load_bytes=load_bytes, **kwargs)
return ds
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/dataset_info.py | from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Union
class DatasetInfo(ABC):
def __init__(self):
pass
@abstractmethod
def num_classes(self):
pass
@abstractmethod
def label_names(self):
pass
@abstractmethod
def label_descriptions(self, detailed: bool = False, as_dict: bool = False) -> Union[List[str], Dict[str, str]]:
pass
@abstractmethod
def index_to_label_name(self, index) -> str:
pass
@abstractmethod
def index_to_description(self, index: int, detailed: bool = False) -> str:
pass
@abstractmethod
def label_name_to_description(self, label: str, detailed: bool = False) -> str:
pass
class CustomDatasetInfo(DatasetInfo):
""" DatasetInfo that wraps passed values for custom datasets."""
def __init__(
self,
label_names: Union[List[str], Dict[int, str]],
label_descriptions: Optional[Dict[str, str]] = None
):
super().__init__()
assert len(label_names) > 0
self._label_names = label_names # label index => label name mapping
self._label_descriptions = label_descriptions # label name => label description mapping
if self._label_descriptions is not None:
# validate descriptions (label names required)
assert isinstance(self._label_descriptions, dict)
for n in self._label_names:
assert n in self._label_descriptions
def num_classes(self):
return len(self._label_names)
def label_names(self):
return self._label_names
def label_descriptions(self, detailed: bool = False, as_dict: bool = False) -> Union[List[str], Dict[str, str]]:
return self._label_descriptions
def label_name_to_description(self, label: str, detailed: bool = False) -> str:
if self._label_descriptions:
return self._label_descriptions[label]
return label # return label name itself if a descriptions is not present
def index_to_label_name(self, index) -> str:
assert 0 <= index < len(self._label_names)
return self._label_names[index]
def index_to_description(self, index: int, detailed: bool = False) -> str:
label = self.index_to_label_name(index)
return self.label_name_to_description(label, detailed=detailed)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/distributed_sampler.py | import math
import torch
from torch.utils.data import Sampler
import torch.distributed as dist
class OrderedDistributedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with
:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
process can pass a DistributedSampler instance as a DataLoader sampler,
and load a subset of the original dataset that is exclusive to it.
.. note::
Dataset is assumed to be of constant size.
Arguments:
dataset: Dataset used for sampling.
num_replicas (optional): Number of processes participating in
distributed training.
rank (optional): Rank of the current process within num_replicas.
"""
def __init__(self, dataset, num_replicas=None, rank=None):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
def __iter__(self):
indices = list(range(len(self.dataset)))
# add extra samples to make it evenly divisible
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self):
return self.num_samples
class RepeatAugSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset for distributed,
with repeated augmentation.
It ensures that different each augmented version of a sample will be visible to a
different process (GPU). Heavily based on torch.utils.data.DistributedSampler
This sampler was taken from https://github.com/facebookresearch/deit/blob/0c4b8f60/samplers.py
Used in
Copyright (c) 2015-present, Facebook, Inc.
"""
def __init__(
self,
dataset,
num_replicas=None,
rank=None,
shuffle=True,
num_repeats=3,
selected_round=256,
selected_ratio=0,
):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.shuffle = shuffle
self.num_repeats = num_repeats
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) * num_repeats / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
# Determine the number of samples to select per epoch for each rank.
# num_selected logic defaults to be the same as original RASampler impl, but this one can be tweaked
# via selected_ratio and selected_round args.
selected_ratio = selected_ratio or num_replicas # ratio to reduce selected samples by, num_replicas if 0
if selected_round:
self.num_selected_samples = int(math.floor(
len(self.dataset) // selected_round * selected_round / selected_ratio))
else:
self.num_selected_samples = int(math.ceil(len(self.dataset) / selected_ratio))
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
if self.shuffle:
indices = torch.randperm(len(self.dataset), generator=g)
else:
indices = torch.arange(start=0, end=len(self.dataset))
# produce repeats e.g. [0, 0, 0, 1, 1, 1, 2, 2, 2....]
if isinstance(self.num_repeats, float) and not self.num_repeats.is_integer():
# resample for repeats w/ non-integer ratio
repeat_size = math.ceil(self.num_repeats * len(self.dataset))
indices = indices[torch.tensor([int(i // self.num_repeats) for i in range(repeat_size)])]
else:
indices = torch.repeat_interleave(indices, repeats=int(self.num_repeats), dim=0)
indices = indices.tolist() # leaving as tensor thrashes dataloader memory
# add extra samples to make it evenly divisible
padding_size = self.total_size - len(indices)
if padding_size > 0:
indices += indices[:padding_size]
assert len(indices) == self.total_size
# subsample per rank
indices = indices[self.rank:self.total_size:self.num_replicas]
assert len(indices) == self.num_samples
# return up to num selected samples
return iter(indices[:self.num_selected_samples])
def __len__(self):
return self.num_selected_samples
def set_epoch(self, epoch):
self.epoch = epoch
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/imagenet_info.py | import csv
import os
import pkgutil
import re
from typing import Dict, List, Optional, Union
from .dataset_info import DatasetInfo
# NOTE no ambiguity wrt to mapping from # classes to ImageNet subset so far, but likely to change
_NUM_CLASSES_TO_SUBSET = {
1000: 'imagenet-1k',
11221: 'imagenet-21k-miil', # miil subset of fall11
11821: 'imagenet-12k', # timm specific 12k subset of fall11
21841: 'imagenet-22k', # as in fall11.tar
21842: 'imagenet-22k-ms', # a Microsoft (for FocalNet) remapping of 22k w/ moves ImageNet-1k classes to first 1000
21843: 'imagenet-21k-goog', # Google's ImageNet full has two classes not in fall11
}
_SUBSETS = {
'imagenet1k': 'imagenet_synsets.txt',
'imagenet12k': 'imagenet12k_synsets.txt',
'imagenet22k': 'imagenet22k_synsets.txt',
'imagenet21k': 'imagenet21k_goog_synsets.txt',
'imagenet21kgoog': 'imagenet21k_goog_synsets.txt',
'imagenet21kmiil': 'imagenet21k_miil_synsets.txt',
'imagenet22kms': 'imagenet22k_ms_synsets.txt',
}
_LEMMA_FILE = 'imagenet_synset_to_lemma.txt'
_DEFINITION_FILE = 'imagenet_synset_to_definition.txt'
def infer_imagenet_subset(model_or_cfg) -> Optional[str]:
if isinstance(model_or_cfg, dict):
num_classes = model_or_cfg.get('num_classes', None)
else:
num_classes = getattr(model_or_cfg, 'num_classes', None)
if not num_classes:
pretrained_cfg = getattr(model_or_cfg, 'pretrained_cfg', {})
# FIXME at some point pretrained_cfg should include dataset-tag,
# which will be more robust than a guess based on num_classes
num_classes = pretrained_cfg.get('num_classes', None)
if not num_classes or num_classes not in _NUM_CLASSES_TO_SUBSET:
return None
return _NUM_CLASSES_TO_SUBSET[num_classes]
class ImageNetInfo(DatasetInfo):
def __init__(self, subset: str = 'imagenet-1k'):
super().__init__()
subset = re.sub(r'[-_\s]', '', subset.lower())
assert subset in _SUBSETS, f'Unknown imagenet subset {subset}.'
# WordNet synsets (part-of-speach + offset) are the unique class label names for ImageNet classifiers
synset_file = _SUBSETS[subset]
synset_data = pkgutil.get_data(__name__, os.path.join('_info', synset_file))
self._synsets = synset_data.decode('utf-8').splitlines()
# WordNet lemmas (canonical dictionary form of word) and definitions are used to build
# the class descriptions. If detailed=True both are used, otherwise just the lemmas.
lemma_data = pkgutil.get_data(__name__, os.path.join('_info', _LEMMA_FILE))
reader = csv.reader(lemma_data.decode('utf-8').splitlines(), delimiter='\t')
self._lemmas = dict(reader)
definition_data = pkgutil.get_data(__name__, os.path.join('_info', _DEFINITION_FILE))
reader = csv.reader(definition_data.decode('utf-8').splitlines(), delimiter='\t')
self._definitions = dict(reader)
def num_classes(self):
return len(self._synsets)
def label_names(self):
return self._synsets
def label_descriptions(self, detailed: bool = False, as_dict: bool = False) -> Union[List[str], Dict[str, str]]:
if as_dict:
return {label: self.label_name_to_description(label, detailed=detailed) for label in self._synsets}
else:
return [self.label_name_to_description(label, detailed=detailed) for label in self._synsets]
def index_to_label_name(self, index) -> str:
assert 0 <= index < len(self._synsets), \
f'Index ({index}) out of range for dataset with {len(self._synsets)} classes.'
return self._synsets[index]
def index_to_description(self, index: int, detailed: bool = False) -> str:
label = self.index_to_label_name(index)
return self.label_name_to_description(label, detailed=detailed)
def label_name_to_description(self, label: str, detailed: bool = False) -> str:
if detailed:
description = f'{self._lemmas[label]}: {self._definitions[label]}'
else:
description = f'{self._lemmas[label]}'
return description
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/loader.py | """ Loader Factory, Fast Collate, CUDA Prefetcher
Prefetcher and Fast Collate inspired by NVIDIA APEX example at
https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import random
from contextlib import suppress
from functools import partial
from itertools import repeat
from typing import Callable
import torch
import torch.utils.data
import numpy as np
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .dataset import IterableImageDataset
from .distributed_sampler import OrderedDistributedSampler, RepeatAugSampler
from .random_erasing import RandomErasing
from .mixup import FastCollateMixup
from .transforms_factory import create_transform
_logger = logging.getLogger(__name__)
def fast_collate(batch):
""" A fast collation function optimized for uint8 images (np array or torch) and int64 targets (labels)"""
assert isinstance(batch[0], tuple)
batch_size = len(batch)
if isinstance(batch[0][0], tuple):
# This branch 'deinterleaves' and flattens tuples of input tensors into one tensor ordered by position
# such that all tuple of position n will end up in a torch.split(tensor, batch_size) in nth position
inner_tuple_size = len(batch[0][0])
flattened_batch_size = batch_size * inner_tuple_size
targets = torch.zeros(flattened_batch_size, dtype=torch.int64)
tensor = torch.zeros((flattened_batch_size, *batch[0][0][0].shape), dtype=torch.uint8)
for i in range(batch_size):
assert len(batch[i][0]) == inner_tuple_size # all input tensor tuples must be same length
for j in range(inner_tuple_size):
targets[i + j * batch_size] = batch[i][1]
tensor[i + j * batch_size] += torch.from_numpy(batch[i][0][j])
return tensor, targets
elif isinstance(batch[0][0], np.ndarray):
targets = torch.tensor([b[1] for b in batch], dtype=torch.int64)
assert len(targets) == batch_size
tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
for i in range(batch_size):
tensor[i] += torch.from_numpy(batch[i][0])
return tensor, targets
elif isinstance(batch[0][0], torch.Tensor):
targets = torch.tensor([b[1] for b in batch], dtype=torch.int64)
assert len(targets) == batch_size
tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
for i in range(batch_size):
tensor[i].copy_(batch[i][0])
return tensor, targets
else:
assert False
def adapt_to_chs(x, n):
if not isinstance(x, (tuple, list)):
x = tuple(repeat(x, n))
elif len(x) != n:
x_mean = np.mean(x).item()
x = (x_mean,) * n
_logger.warning(f'Pretrained mean/std different shape than model, using avg value {x}.')
else:
assert len(x) == n, 'normalization stats must match image channels'
return x
class PrefetchLoader:
def __init__(
self,
loader,
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD,
channels=3,
device=torch.device('cuda'),
img_dtype=torch.float32,
fp16=False,
re_prob=0.,
re_mode='const',
re_count=1,
re_num_splits=0):
mean = adapt_to_chs(mean, channels)
std = adapt_to_chs(std, channels)
normalization_shape = (1, channels, 1, 1)
self.loader = loader
self.device = device
if fp16:
# fp16 arg is deprecated, but will override dtype arg if set for bwd compat
img_dtype = torch.float16
self.img_dtype = img_dtype
self.mean = torch.tensor(
[x * 255 for x in mean], device=device, dtype=img_dtype).view(normalization_shape)
self.std = torch.tensor(
[x * 255 for x in std], device=device, dtype=img_dtype).view(normalization_shape)
if re_prob > 0.:
self.random_erasing = RandomErasing(
probability=re_prob,
mode=re_mode,
max_count=re_count,
num_splits=re_num_splits,
device=device,
)
else:
self.random_erasing = None
self.is_cuda = torch.cuda.is_available() and device.type == 'cuda'
def __iter__(self):
first = True
if self.is_cuda:
stream = torch.cuda.Stream()
stream_context = partial(torch.cuda.stream, stream=stream)
else:
stream = None
stream_context = suppress
for next_input, next_target in self.loader:
with stream_context():
next_input = next_input.to(device=self.device, non_blocking=True)
next_target = next_target.to(device=self.device, non_blocking=True)
next_input = next_input.to(self.img_dtype).sub_(self.mean).div_(self.std)
if self.random_erasing is not None:
next_input = self.random_erasing(next_input)
if not first:
yield input, target
else:
first = False
if stream is not None:
torch.cuda.current_stream().wait_stream(stream)
input = next_input
target = next_target
yield input, target
def __len__(self):
return len(self.loader)
@property
def sampler(self):
return self.loader.sampler
@property
def dataset(self):
return self.loader.dataset
@property
def mixup_enabled(self):
if isinstance(self.loader.collate_fn, FastCollateMixup):
return self.loader.collate_fn.mixup_enabled
else:
return False
@mixup_enabled.setter
def mixup_enabled(self, x):
if isinstance(self.loader.collate_fn, FastCollateMixup):
self.loader.collate_fn.mixup_enabled = x
def _worker_init(worker_id, worker_seeding='all'):
worker_info = torch.utils.data.get_worker_info()
assert worker_info.id == worker_id
if isinstance(worker_seeding, Callable):
seed = worker_seeding(worker_info)
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed % (2 ** 32 - 1))
else:
assert worker_seeding in ('all', 'part')
# random / torch seed already called in dataloader iter class w/ worker_info.seed
# to reproduce some old results (same seed + hparam combo), partial seeding is required (skip numpy re-seed)
if worker_seeding == 'all':
np.random.seed(worker_info.seed % (2 ** 32 - 1))
def create_loader(
dataset,
input_size,
batch_size,
is_training=False,
use_prefetcher=True,
no_aug=False,
re_prob=0.,
re_mode='const',
re_count=1,
re_split=False,
scale=None,
ratio=None,
hflip=0.5,
vflip=0.,
color_jitter=0.4,
auto_augment=None,
num_aug_repeats=0,
num_aug_splits=0,
interpolation='bilinear',
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD,
num_workers=1,
distributed=False,
crop_pct=None,
crop_mode=None,
collate_fn=None,
pin_memory=False,
fp16=False, # deprecated, use img_dtype
img_dtype=torch.float32,
device=torch.device('cuda'),
tf_preprocessing=False,
use_multi_epochs_loader=False,
persistent_workers=True,
worker_seeding='all',
):
re_num_splits = 0
if re_split:
# apply RE to second half of batch if no aug split otherwise line up with aug split
re_num_splits = num_aug_splits or 2
dataset.transform = create_transform(
input_size,
is_training=is_training,
use_prefetcher=use_prefetcher,
no_aug=no_aug,
scale=scale,
ratio=ratio,
hflip=hflip,
vflip=vflip,
color_jitter=color_jitter,
auto_augment=auto_augment,
interpolation=interpolation,
mean=mean,
std=std,
crop_pct=crop_pct,
crop_mode=crop_mode,
tf_preprocessing=tf_preprocessing,
re_prob=re_prob,
re_mode=re_mode,
re_count=re_count,
re_num_splits=re_num_splits,
separate=num_aug_splits > 0,
)
if isinstance(dataset, IterableImageDataset):
# give Iterable datasets early knowledge of num_workers so that sample estimates
# are correct before worker processes are launched
dataset.set_loader_cfg(num_workers=num_workers)
sampler = None
if distributed and not isinstance(dataset, torch.utils.data.IterableDataset):
if is_training:
if num_aug_repeats:
sampler = RepeatAugSampler(dataset, num_repeats=num_aug_repeats)
else:
sampler = torch.utils.data.distributed.DistributedSampler(dataset)
else:
# This will add extra duplicate entries to result in equal num
# of samples per-process, will slightly alter validation results
sampler = OrderedDistributedSampler(dataset)
else:
assert num_aug_repeats == 0, "RepeatAugment not currently supported in non-distributed or IterableDataset use"
if collate_fn is None:
collate_fn = fast_collate if use_prefetcher else torch.utils.data.dataloader.default_collate
loader_class = torch.utils.data.DataLoader
if use_multi_epochs_loader:
loader_class = MultiEpochsDataLoader
loader_args = dict(
batch_size=batch_size,
shuffle=not isinstance(dataset, torch.utils.data.IterableDataset) and sampler is None and is_training,
num_workers=num_workers,
sampler=sampler,
collate_fn=collate_fn,
pin_memory=pin_memory,
drop_last=is_training,
worker_init_fn=partial(_worker_init, worker_seeding=worker_seeding),
persistent_workers=persistent_workers
)
try:
loader = loader_class(dataset, **loader_args)
except TypeError as e:
loader_args.pop('persistent_workers') # only in Pytorch 1.7+
loader = loader_class(dataset, **loader_args)
if use_prefetcher:
prefetch_re_prob = re_prob if is_training and not no_aug else 0.
loader = PrefetchLoader(
loader,
mean=mean,
std=std,
channels=input_size[0],
device=device,
fp16=fp16, # deprecated, use img_dtype
img_dtype=img_dtype,
re_prob=prefetch_re_prob,
re_mode=re_mode,
re_count=re_count,
re_num_splits=re_num_splits
)
return loader
class MultiEpochsDataLoader(torch.utils.data.DataLoader):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._DataLoader__initialized = False
if self.batch_sampler is None:
self.sampler = _RepeatSampler(self.sampler)
else:
self.batch_sampler = _RepeatSampler(self.batch_sampler)
self._DataLoader__initialized = True
self.iterator = super().__iter__()
def __len__(self):
return len(self.sampler) if self.batch_sampler is None else len(self.batch_sampler.sampler)
def __iter__(self):
for i in range(len(self)):
yield next(self.iterator)
class _RepeatSampler(object):
""" Sampler that repeats forever.
Args:
sampler (Sampler)
"""
def __init__(self, sampler):
self.sampler = sampler
def __iter__(self):
while True:
yield from iter(self.sampler)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/mixup.py | """ Mixup and Cutmix
Papers:
mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899)
Code Reference:
CutMix: https://github.com/clovaai/CutMix-PyTorch
Hacked together by / Copyright 2019, Ross Wightman
"""
import numpy as np
import torch
def one_hot(x, num_classes, on_value=1., off_value=0.):
x = x.long().view(-1, 1)
return torch.full((x.size()[0], num_classes), off_value, device=x.device).scatter_(1, x, on_value)
def mixup_target(target, num_classes, lam=1., smoothing=0.0):
off_value = smoothing / num_classes
on_value = 1. - smoothing + off_value
y1 = one_hot(target, num_classes, on_value=on_value, off_value=off_value)
y2 = one_hot(target.flip(0), num_classes, on_value=on_value, off_value=off_value)
return y1 * lam + y2 * (1. - lam)
def rand_bbox(img_shape, lam, margin=0., count=None):
""" Standard CutMix bounding-box
Generates a random square bbox based on lambda value. This impl includes
support for enforcing a border margin as percent of bbox dimensions.
Args:
img_shape (tuple): Image shape as tuple
lam (float): Cutmix lambda value
margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image)
count (int): Number of bbox to generate
"""
ratio = np.sqrt(1 - lam)
img_h, img_w = img_shape[-2:]
cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
margin_y, margin_x = int(margin * cut_h), int(margin * cut_w)
cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)
yl = np.clip(cy - cut_h // 2, 0, img_h)
yh = np.clip(cy + cut_h // 2, 0, img_h)
xl = np.clip(cx - cut_w // 2, 0, img_w)
xh = np.clip(cx + cut_w // 2, 0, img_w)
return yl, yh, xl, xh
def rand_bbox_minmax(img_shape, minmax, count=None):
""" Min-Max CutMix bounding-box
Inspired by Darknet cutmix impl, generates a random rectangular bbox
based on min/max percent values applied to each dimension of the input image.
Typical defaults for minmax are usually in the .2-.3 for min and .8-.9 range for max.
Args:
img_shape (tuple): Image shape as tuple
minmax (tuple or list): Min and max bbox ratios (as percent of image size)
count (int): Number of bbox to generate
"""
assert len(minmax) == 2
img_h, img_w = img_shape[-2:]
cut_h = np.random.randint(int(img_h * minmax[0]), int(img_h * minmax[1]), size=count)
cut_w = np.random.randint(int(img_w * minmax[0]), int(img_w * minmax[1]), size=count)
yl = np.random.randint(0, img_h - cut_h, size=count)
xl = np.random.randint(0, img_w - cut_w, size=count)
yu = yl + cut_h
xu = xl + cut_w
return yl, yu, xl, xu
def cutmix_bbox_and_lam(img_shape, lam, ratio_minmax=None, correct_lam=True, count=None):
""" Generate bbox and apply lambda correction.
"""
if ratio_minmax is not None:
yl, yu, xl, xu = rand_bbox_minmax(img_shape, ratio_minmax, count=count)
else:
yl, yu, xl, xu = rand_bbox(img_shape, lam, count=count)
if correct_lam or ratio_minmax is not None:
bbox_area = (yu - yl) * (xu - xl)
lam = 1. - bbox_area / float(img_shape[-2] * img_shape[-1])
return (yl, yu, xl, xu), lam
class Mixup:
""" Mixup/Cutmix that applies different params to each element or whole batch
Args:
mixup_alpha (float): mixup alpha value, mixup is active if > 0.
cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0.
cutmix_minmax (List[float]): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None.
prob (float): probability of applying mixup or cutmix per batch or element
switch_prob (float): probability of switching to cutmix instead of mixup when both are active
mode (str): how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element)
correct_lam (bool): apply lambda correction when cutmix bbox clipped by image borders
label_smoothing (float): apply label smoothing to the mixed target tensor
num_classes (int): number of classes for target
"""
def __init__(self, mixup_alpha=1., cutmix_alpha=0., cutmix_minmax=None, prob=1.0, switch_prob=0.5,
mode='batch', correct_lam=True, label_smoothing=0.1, num_classes=1000):
self.mixup_alpha = mixup_alpha
self.cutmix_alpha = cutmix_alpha
self.cutmix_minmax = cutmix_minmax
if self.cutmix_minmax is not None:
assert len(self.cutmix_minmax) == 2
# force cutmix alpha == 1.0 when minmax active to keep logic simple & safe
self.cutmix_alpha = 1.0
self.mix_prob = prob
self.switch_prob = switch_prob
self.label_smoothing = label_smoothing
self.num_classes = num_classes
self.mode = mode
self.correct_lam = correct_lam # correct lambda based on clipped area for cutmix
self.mixup_enabled = True # set to false to disable mixing (intended tp be set by train loop)
def _params_per_elem(self, batch_size):
lam = np.ones(batch_size, dtype=np.float32)
use_cutmix = np.zeros(batch_size, dtype=bool)
if self.mixup_enabled:
if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
use_cutmix = np.random.rand(batch_size) < self.switch_prob
lam_mix = np.where(
use_cutmix,
np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size),
np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size))
elif self.mixup_alpha > 0.:
lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size)
elif self.cutmix_alpha > 0.:
use_cutmix = np.ones(batch_size, dtype=bool)
lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha, size=batch_size)
else:
assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
lam = np.where(np.random.rand(batch_size) < self.mix_prob, lam_mix.astype(np.float32), lam)
return lam, use_cutmix
def _params_per_batch(self):
lam = 1.
use_cutmix = False
if self.mixup_enabled and np.random.rand() < self.mix_prob:
if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
use_cutmix = np.random.rand() < self.switch_prob
lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \
np.random.beta(self.mixup_alpha, self.mixup_alpha)
elif self.mixup_alpha > 0.:
lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha)
elif self.cutmix_alpha > 0.:
use_cutmix = True
lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
else:
assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
lam = float(lam_mix)
return lam, use_cutmix
def _mix_elem(self, x):
batch_size = len(x)
lam_batch, use_cutmix = self._params_per_elem(batch_size)
x_orig = x.clone() # need to keep an unmodified original for mixing source
for i in range(batch_size):
j = batch_size - i - 1
lam = lam_batch[i]
if lam != 1.:
if use_cutmix[i]:
(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
lam_batch[i] = lam
else:
x[i] = x[i] * lam + x_orig[j] * (1 - lam)
return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)
def _mix_pair(self, x):
batch_size = len(x)
lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
x_orig = x.clone() # need to keep an unmodified original for mixing source
for i in range(batch_size // 2):
j = batch_size - i - 1
lam = lam_batch[i]
if lam != 1.:
if use_cutmix[i]:
(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
x[i].shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
x[j][:, yl:yh, xl:xh] = x_orig[i][:, yl:yh, xl:xh]
lam_batch[i] = lam
else:
x[i] = x[i] * lam + x_orig[j] * (1 - lam)
x[j] = x[j] * lam + x_orig[i] * (1 - lam)
lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)
def _mix_batch(self, x):
lam, use_cutmix = self._params_per_batch()
if lam == 1.:
return 1.
if use_cutmix:
(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
x.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh]
else:
x_flipped = x.flip(0).mul_(1. - lam)
x.mul_(lam).add_(x_flipped)
return lam
def __call__(self, x, target):
assert len(x) % 2 == 0, 'Batch size should be even when using this'
if self.mode == 'elem':
lam = self._mix_elem(x)
elif self.mode == 'pair':
lam = self._mix_pair(x)
else:
lam = self._mix_batch(x)
target = mixup_target(target, self.num_classes, lam, self.label_smoothing)
return x, target
class FastCollateMixup(Mixup):
""" Fast Collate w/ Mixup/Cutmix that applies different params to each element or whole batch
A Mixup impl that's performed while collating the batches.
"""
def _mix_elem_collate(self, output, batch, half=False):
batch_size = len(batch)
num_elem = batch_size // 2 if half else batch_size
assert len(output) == num_elem
lam_batch, use_cutmix = self._params_per_elem(num_elem)
for i in range(num_elem):
j = batch_size - i - 1
lam = lam_batch[i]
mixed = batch[i][0]
if lam != 1.:
if use_cutmix[i]:
if not half:
mixed = mixed.copy()
(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
lam_batch[i] = lam
else:
mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
np.rint(mixed, out=mixed)
output[i] += torch.from_numpy(mixed.astype(np.uint8))
if half:
lam_batch = np.concatenate((lam_batch, np.ones(num_elem)))
return torch.tensor(lam_batch).unsqueeze(1)
def _mix_pair_collate(self, output, batch):
batch_size = len(batch)
lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
for i in range(batch_size // 2):
j = batch_size - i - 1
lam = lam_batch[i]
mixed_i = batch[i][0]
mixed_j = batch[j][0]
assert 0 <= lam <= 1.0
if lam < 1.:
if use_cutmix[i]:
(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
patch_i = mixed_i[:, yl:yh, xl:xh].copy()
mixed_i[:, yl:yh, xl:xh] = mixed_j[:, yl:yh, xl:xh]
mixed_j[:, yl:yh, xl:xh] = patch_i
lam_batch[i] = lam
else:
mixed_temp = mixed_i.astype(np.float32) * lam + mixed_j.astype(np.float32) * (1 - lam)
mixed_j = mixed_j.astype(np.float32) * lam + mixed_i.astype(np.float32) * (1 - lam)
mixed_i = mixed_temp
np.rint(mixed_j, out=mixed_j)
np.rint(mixed_i, out=mixed_i)
output[i] += torch.from_numpy(mixed_i.astype(np.uint8))
output[j] += torch.from_numpy(mixed_j.astype(np.uint8))
lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
return torch.tensor(lam_batch).unsqueeze(1)
def _mix_batch_collate(self, output, batch):
batch_size = len(batch)
lam, use_cutmix = self._params_per_batch()
if use_cutmix:
(yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)
for i in range(batch_size):
j = batch_size - i - 1
mixed = batch[i][0]
if lam != 1.:
if use_cutmix:
mixed = mixed.copy() # don't want to modify the original while iterating
mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]
else:
mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)
np.rint(mixed, out=mixed)
output[i] += torch.from_numpy(mixed.astype(np.uint8))
return lam
def __call__(self, batch, _=None):
batch_size = len(batch)
assert batch_size % 2 == 0, 'Batch size should be even when using this'
half = 'half' in self.mode
if half:
batch_size //= 2
output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
if self.mode == 'elem' or self.mode == 'half':
lam = self._mix_elem_collate(output, batch, half=half)
elif self.mode == 'pair':
lam = self._mix_pair_collate(output, batch)
else:
lam = self._mix_batch_collate(output, batch)
target = torch.tensor([b[1] for b in batch], dtype=torch.int64)
target = mixup_target(target, self.num_classes, lam, self.label_smoothing)
target = target[:batch_size]
return output, target
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/random_erasing.py | """ Random Erasing (Cutout)
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2019, Ross Wightman
"""
import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float32, device='cuda'):
# NOTE I've seen CUDA illegal memory access errors being caused by the normal_()
# paths, flip the order so normal is run on CPU if this becomes a problem
# Issue has been fixed in master https://github.com/pytorch/pytorch/issues/19508
if per_pixel:
return torch.empty(patch_size, dtype=dtype, device=device).normal_()
elif rand_color:
return torch.empty((patch_size[0], 1, 1), dtype=dtype, device=device).normal_()
else:
return torch.zeros((patch_size[0], 1, 1), dtype=dtype, device=device)
class RandomErasing:
""" Randomly selects a rectangle region in an image and erases its pixels.
'Random Erasing Data Augmentation' by Zhong et al.
See https://arxiv.org/pdf/1708.04896.pdf
This variant of RandomErasing is intended to be applied to either a batch
or single image tensor after it has been normalized by dataset mean and std.
Args:
probability: Probability that the Random Erasing operation will be performed.
min_area: Minimum percentage of erased area wrt input image area.
max_area: Maximum percentage of erased area wrt input image area.
min_aspect: Minimum aspect ratio of erased area.
mode: pixel color mode, one of 'const', 'rand', or 'pixel'
'const' - erase block is constant color of 0 for all channels
'rand' - erase block is same per-channel random (normal) color
'pixel' - erase block is per-pixel random (normal) color
max_count: maximum number of erasing blocks per image, area per box is scaled by count.
per-image count is randomly chosen between 1 and this value.
"""
def __init__(
self,
probability=0.5,
min_area=0.02,
max_area=1/3,
min_aspect=0.3,
max_aspect=None,
mode='const',
min_count=1,
max_count=None,
num_splits=0,
device='cuda',
):
self.probability = probability
self.min_area = min_area
self.max_area = max_area
max_aspect = max_aspect or 1 / min_aspect
self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))
self.min_count = min_count
self.max_count = max_count or min_count
self.num_splits = num_splits
self.mode = mode.lower()
self.rand_color = False
self.per_pixel = False
if self.mode == 'rand':
self.rand_color = True # per block random normal
elif self.mode == 'pixel':
self.per_pixel = True # per pixel random normal
else:
assert not self.mode or self.mode == 'const'
self.device = device
def _erase(self, img, chan, img_h, img_w, dtype):
if random.random() > self.probability:
return
area = img_h * img_w
count = self.min_count if self.min_count == self.max_count else \
random.randint(self.min_count, self.max_count)
for _ in range(count):
for attempt in range(10):
target_area = random.uniform(self.min_area, self.max_area) * area / count
aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if w < img_w and h < img_h:
top = random.randint(0, img_h - h)
left = random.randint(0, img_w - w)
img[:, top:top + h, left:left + w] = _get_pixels(
self.per_pixel,
self.rand_color,
(chan, h, w),
dtype=dtype,
device=self.device,
)
break
def __call__(self, input):
if len(input.size()) == 3:
self._erase(input, *input.size(), input.dtype)
else:
batch_size, chan, img_h, img_w = input.size()
# skip first slice of batch if num_splits is set (for clean portion of samples)
batch_start = batch_size // self.num_splits if self.num_splits > 1 else 0
for i in range(batch_start, batch_size):
self._erase(input[i], chan, img_h, img_w, input.dtype)
return input
def __repr__(self):
# NOTE simplified state for repr
fs = self.__class__.__name__ + f'(p={self.probability}, mode={self.mode}'
fs += f', count=({self.min_count}, {self.max_count}))'
return fs
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/real_labels.py | """ Real labels evaluator for ImageNet
Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159
Based on Numpy example at https://github.com/google-research/reassessed-imagenet
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import json
import numpy as np
import pkgutil
class RealLabelsImagenet:
def __init__(self, filenames, real_json=None, topk=(1, 5)):
if real_json is not None:
with open(real_json) as real_labels:
real_labels = json.load(real_labels)
else:
real_labels = json.loads(
pkgutil.get_data(__name__, os.path.join('_info', 'imagenet_real_labels.json')).decode('utf-8'))
real_labels = {f'ILSVRC2012_val_{i + 1:08d}.JPEG': labels for i, labels in enumerate(real_labels)}
self.real_labels = real_labels
self.filenames = filenames
assert len(self.filenames) == len(self.real_labels)
self.topk = topk
self.is_correct = {k: [] for k in topk}
self.sample_idx = 0
def add_result(self, output):
maxk = max(self.topk)
_, pred_batch = output.topk(maxk, 1, True, True)
pred_batch = pred_batch.cpu().numpy()
for pred in pred_batch:
filename = self.filenames[self.sample_idx]
filename = os.path.basename(filename)
if self.real_labels[filename]:
for k in self.topk:
self.is_correct[k].append(
any([p in self.real_labels[filename] for p in pred[:k]]))
self.sample_idx += 1
def get_accuracy(self, k=None):
if k is None:
return {k: float(np.mean(self.is_correct[k])) * 100 for k in self.topk}
else:
return float(np.mean(self.is_correct[k])) * 100
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/tf_preprocessing.py | """ Tensorflow Preprocessing Adapter
Allows use of Tensorflow preprocessing pipeline in PyTorch Transform
Copyright of original Tensorflow code below.
Hacked together by / Copyright 2020 Ross Wightman
"""
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ImageNet preprocessing for MnasNet."""
import tensorflow.compat.v1 as tf
import numpy as np
IMAGE_SIZE = 224
CROP_PADDING = 32
tf.compat.v1.disable_eager_execution()
def distorted_bounding_box_crop(image_bytes,
bbox,
min_object_covered=0.1,
aspect_ratio_range=(0.75, 1.33),
area_range=(0.05, 1.0),
max_attempts=100,
scope=None):
"""Generates cropped_image using one of the bboxes randomly distorted.
See `tf.image.sample_distorted_bounding_box` for more documentation.
Args:
image_bytes: `Tensor` of binary image data.
bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]`
where each coordinate is [0, 1) and the coordinates are arranged
as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole
image.
min_object_covered: An optional `float`. Defaults to `0.1`. The cropped
area of the image must contain at least this fraction of any bounding
box supplied.
aspect_ratio_range: An optional list of `float`s. The cropped area of the
image must have an aspect ratio = width / height within this range.
area_range: An optional list of `float`s. The cropped area of the image
must contain a fraction of the supplied image within in this range.
max_attempts: An optional `int`. Number of attempts at generating a cropped
region of the image of the specified constraints. After `max_attempts`
failures, return the entire image.
scope: Optional `str` for name scope.
Returns:
cropped image `Tensor`
"""
with tf.name_scope(scope, 'distorted_bounding_box_crop', [image_bytes, bbox]):
shape = tf.image.extract_jpeg_shape(image_bytes)
sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
shape,
bounding_boxes=bbox,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
max_attempts=max_attempts,
use_image_if_no_bounding_boxes=True)
bbox_begin, bbox_size, _ = sample_distorted_bounding_box
# Crop the image to the specified bounding box.
offset_y, offset_x, _ = tf.unstack(bbox_begin)
target_height, target_width, _ = tf.unstack(bbox_size)
crop_window = tf.stack([offset_y, offset_x, target_height, target_width])
image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3)
return image
def _at_least_x_are_equal(a, b, x):
"""At least `x` of `a` and `b` `Tensors` are equal."""
match = tf.equal(a, b)
match = tf.cast(match, tf.int32)
return tf.greater_equal(tf.reduce_sum(match), x)
def _decode_and_random_crop(image_bytes, image_size, resize_method):
"""Make a random crop of image_size."""
bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
image = distorted_bounding_box_crop(
image_bytes,
bbox,
min_object_covered=0.1,
aspect_ratio_range=(3. / 4, 4. / 3.),
area_range=(0.08, 1.0),
max_attempts=10,
scope=None)
original_shape = tf.image.extract_jpeg_shape(image_bytes)
bad = _at_least_x_are_equal(original_shape, tf.shape(image), 3)
image = tf.cond(
bad,
lambda: _decode_and_center_crop(image_bytes, image_size),
lambda: tf.image.resize([image], [image_size, image_size], resize_method)[0])
return image
def _decode_and_center_crop(image_bytes, image_size, resize_method):
"""Crops to center of image with padding then scales image_size."""
shape = tf.image.extract_jpeg_shape(image_bytes)
image_height = shape[0]
image_width = shape[1]
padded_center_crop_size = tf.cast(
((image_size / (image_size + CROP_PADDING)) *
tf.cast(tf.minimum(image_height, image_width), tf.float32)),
tf.int32)
offset_height = ((image_height - padded_center_crop_size) + 1) // 2
offset_width = ((image_width - padded_center_crop_size) + 1) // 2
crop_window = tf.stack([offset_height, offset_width,
padded_center_crop_size, padded_center_crop_size])
image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3)
image = tf.image.resize([image], [image_size, image_size], resize_method)[0]
return image
def _flip(image):
"""Random horizontal image flip."""
image = tf.image.random_flip_left_right(image)
return image
def preprocess_for_train(image_bytes, use_bfloat16, image_size=IMAGE_SIZE, interpolation='bicubic'):
"""Preprocesses the given image for evaluation.
Args:
image_bytes: `Tensor` representing an image binary of arbitrary size.
use_bfloat16: `bool` for whether to use bfloat16.
image_size: image size.
interpolation: image interpolation method
Returns:
A preprocessed image `Tensor`.
"""
resize_method = tf.image.ResizeMethod.BICUBIC if interpolation == 'bicubic' else tf.image.ResizeMethod.BILINEAR
image = _decode_and_random_crop(image_bytes, image_size, resize_method)
image = _flip(image)
image = tf.reshape(image, [image_size, image_size, 3])
image = tf.image.convert_image_dtype(
image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32)
return image
def preprocess_for_eval(image_bytes, use_bfloat16, image_size=IMAGE_SIZE, interpolation='bicubic'):
"""Preprocesses the given image for evaluation.
Args:
image_bytes: `Tensor` representing an image binary of arbitrary size.
use_bfloat16: `bool` for whether to use bfloat16.
image_size: image size.
interpolation: image interpolation method
Returns:
A preprocessed image `Tensor`.
"""
resize_method = tf.image.ResizeMethod.BICUBIC if interpolation == 'bicubic' else tf.image.ResizeMethod.BILINEAR
image = _decode_and_center_crop(image_bytes, image_size, resize_method)
image = tf.reshape(image, [image_size, image_size, 3])
image = tf.image.convert_image_dtype(
image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32)
return image
def preprocess_image(image_bytes,
is_training=False,
use_bfloat16=False,
image_size=IMAGE_SIZE,
interpolation='bicubic'):
"""Preprocesses the given image.
Args:
image_bytes: `Tensor` representing an image binary of arbitrary size.
is_training: `bool` for whether the preprocessing is for training.
use_bfloat16: `bool` for whether to use bfloat16.
image_size: image size.
interpolation: image interpolation method
Returns:
A preprocessed image `Tensor` with value range of [0, 255].
"""
if is_training:
return preprocess_for_train(image_bytes, use_bfloat16, image_size, interpolation)
else:
return preprocess_for_eval(image_bytes, use_bfloat16, image_size, interpolation)
class TfPreprocessTransform:
def __init__(self, is_training=False, size=224, interpolation='bicubic'):
self.is_training = is_training
self.size = size[0] if isinstance(size, tuple) else size
self.interpolation = interpolation
self._image_bytes = None
self.process_image = self._build_tf_graph()
self.sess = None
def _build_tf_graph(self):
with tf.device('/cpu:0'):
self._image_bytes = tf.placeholder(
shape=[],
dtype=tf.string,
)
img = preprocess_image(
self._image_bytes, self.is_training, False, self.size, self.interpolation)
return img
def __call__(self, image_bytes):
if self.sess is None:
self.sess = tf.Session()
img = self.sess.run(self.process_image, feed_dict={self._image_bytes: image_bytes})
img = img.round().clip(0, 255).astype(np.uint8)
if img.ndim < 3:
img = np.expand_dims(img, axis=-1)
img = np.rollaxis(img, 2) # HWC to CHW
return img
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/transforms.py | import math
import numbers
import random
import warnings
from typing import List, Sequence
import torch
import torchvision.transforms.functional as F
try:
from torchvision.transforms.functional import InterpolationMode
has_interpolation_mode = True
except ImportError:
has_interpolation_mode = False
from PIL import Image
import numpy as np
class ToNumpy:
def __call__(self, pil_img):
np_img = np.array(pil_img, dtype=np.uint8)
if np_img.ndim < 3:
np_img = np.expand_dims(np_img, axis=-1)
np_img = np.rollaxis(np_img, 2) # HWC to CHW
return np_img
class ToTensor:
def __init__(self, dtype=torch.float32):
self.dtype = dtype
def __call__(self, pil_img):
np_img = np.array(pil_img, dtype=np.uint8)
if np_img.ndim < 3:
np_img = np.expand_dims(np_img, axis=-1)
np_img = np.rollaxis(np_img, 2) # HWC to CHW
return torch.from_numpy(np_img).to(dtype=self.dtype)
# Pillow is deprecating the top-level resampling attributes (e.g., Image.BILINEAR) in
# favor of the Image.Resampling enum. The top-level resampling attributes will be
# removed in Pillow 10.
if hasattr(Image, "Resampling"):
_pil_interpolation_to_str = {
Image.Resampling.NEAREST: 'nearest',
Image.Resampling.BILINEAR: 'bilinear',
Image.Resampling.BICUBIC: 'bicubic',
Image.Resampling.BOX: 'box',
Image.Resampling.HAMMING: 'hamming',
Image.Resampling.LANCZOS: 'lanczos',
}
else:
_pil_interpolation_to_str = {
Image.NEAREST: 'nearest',
Image.BILINEAR: 'bilinear',
Image.BICUBIC: 'bicubic',
Image.BOX: 'box',
Image.HAMMING: 'hamming',
Image.LANCZOS: 'lanczos',
}
_str_to_pil_interpolation = {b: a for a, b in _pil_interpolation_to_str.items()}
if has_interpolation_mode:
_torch_interpolation_to_str = {
InterpolationMode.NEAREST: 'nearest',
InterpolationMode.BILINEAR: 'bilinear',
InterpolationMode.BICUBIC: 'bicubic',
InterpolationMode.BOX: 'box',
InterpolationMode.HAMMING: 'hamming',
InterpolationMode.LANCZOS: 'lanczos',
}
_str_to_torch_interpolation = {b: a for a, b in _torch_interpolation_to_str.items()}
else:
_pil_interpolation_to_torch = {}
_torch_interpolation_to_str = {}
def str_to_pil_interp(mode_str):
return _str_to_pil_interpolation[mode_str]
def str_to_interp_mode(mode_str):
if has_interpolation_mode:
return _str_to_torch_interpolation[mode_str]
else:
return _str_to_pil_interpolation[mode_str]
def interp_mode_to_str(mode):
if has_interpolation_mode:
return _torch_interpolation_to_str[mode]
else:
return _pil_interpolation_to_str[mode]
_RANDOM_INTERPOLATION = (str_to_interp_mode('bilinear'), str_to_interp_mode('bicubic'))
def _setup_size(size, error_msg):
if isinstance(size, numbers.Number):
return int(size), int(size)
if isinstance(size, Sequence) and len(size) == 1:
return size[0], size[0]
if len(size) != 2:
raise ValueError(error_msg)
return size
class RandomResizedCropAndInterpolation:
"""Crop the given PIL Image to random size and aspect ratio with random interpolation.
A crop of random size (default: of 0.08 to 1.0) of the original size and a random
aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop
is finally resized to given size.
This is popularly used to train the Inception networks.
Args:
size: expected output size of each edge
scale: range of size of the origin size cropped
ratio: range of aspect ratio of the origin aspect ratio cropped
interpolation: Default: PIL.Image.BILINEAR
"""
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation='bilinear'):
if isinstance(size, (list, tuple)):
self.size = tuple(size)
else:
self.size = (size, size)
if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
warnings.warn("range should be of kind (min, max)")
if interpolation == 'random':
self.interpolation = _RANDOM_INTERPOLATION
else:
self.interpolation = str_to_interp_mode(interpolation)
self.scale = scale
self.ratio = ratio
@staticmethod
def get_params(img, scale, ratio):
"""Get parameters for ``crop`` for a random sized crop.
Args:
img (PIL Image): Image to be cropped.
scale (tuple): range of size of the origin size cropped
ratio (tuple): range of aspect ratio of the origin aspect ratio cropped
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for a random
sized crop.
"""
area = img.size[0] * img.size[1]
for attempt in range(10):
target_area = random.uniform(*scale) * area
log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
aspect_ratio = math.exp(random.uniform(*log_ratio))
w = int(round(math.sqrt(target_area * aspect_ratio)))
h = int(round(math.sqrt(target_area / aspect_ratio)))
if w <= img.size[0] and h <= img.size[1]:
i = random.randint(0, img.size[1] - h)
j = random.randint(0, img.size[0] - w)
return i, j, h, w
# Fallback to central crop
in_ratio = img.size[0] / img.size[1]
if in_ratio < min(ratio):
w = img.size[0]
h = int(round(w / min(ratio)))
elif in_ratio > max(ratio):
h = img.size[1]
w = int(round(h * max(ratio)))
else: # whole image
w = img.size[0]
h = img.size[1]
i = (img.size[1] - h) // 2
j = (img.size[0] - w) // 2
return i, j, h, w
def __call__(self, img):
"""
Args:
img (PIL Image): Image to be cropped and resized.
Returns:
PIL Image: Randomly cropped and resized image.
"""
i, j, h, w = self.get_params(img, self.scale, self.ratio)
if isinstance(self.interpolation, (tuple, list)):
interpolation = random.choice(self.interpolation)
else:
interpolation = self.interpolation
return F.resized_crop(img, i, j, h, w, self.size, interpolation)
def __repr__(self):
if isinstance(self.interpolation, (tuple, list)):
interpolate_str = ' '.join([interp_mode_to_str(x) for x in self.interpolation])
else:
interpolate_str = interp_mode_to_str(self.interpolation)
format_string = self.__class__.__name__ + '(size={0}'.format(self.size)
format_string += ', scale={0}'.format(tuple(round(s, 4) for s in self.scale))
format_string += ', ratio={0}'.format(tuple(round(r, 4) for r in self.ratio))
format_string += ', interpolation={0})'.format(interpolate_str)
return format_string
def center_crop_or_pad(img: torch.Tensor, output_size: List[int], fill=0) -> torch.Tensor:
"""Center crops and/or pads the given image.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
Args:
img (PIL Image or Tensor): Image to be cropped.
output_size (sequence or int): (height, width) of the crop box. If int or sequence with single int,
it is used for both directions.
fill (int, Tuple[int]): Padding color
Returns:
PIL Image or Tensor: Cropped image.
"""
if isinstance(output_size, numbers.Number):
output_size = (int(output_size), int(output_size))
elif isinstance(output_size, (tuple, list)) and len(output_size) == 1:
output_size = (output_size[0], output_size[0])
_, image_height, image_width = F.get_dimensions(img)
crop_height, crop_width = output_size
if crop_width > image_width or crop_height > image_height:
padding_ltrb = [
(crop_width - image_width) // 2 if crop_width > image_width else 0,
(crop_height - image_height) // 2 if crop_height > image_height else 0,
(crop_width - image_width + 1) // 2 if crop_width > image_width else 0,
(crop_height - image_height + 1) // 2 if crop_height > image_height else 0,
]
img = F.pad(img, padding_ltrb, fill=fill)
_, image_height, image_width = F.get_dimensions(img)
if crop_width == image_width and crop_height == image_height:
return img
crop_top = int(round((image_height - crop_height) / 2.0))
crop_left = int(round((image_width - crop_width) / 2.0))
return F.crop(img, crop_top, crop_left, crop_height, crop_width)
class CenterCropOrPad(torch.nn.Module):
"""Crops the given image at the center.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
"""
def __init__(self, size, fill=0):
super().__init__()
self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
self.fill = fill
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be cropped.
Returns:
PIL Image or Tensor: Cropped image.
"""
return center_crop_or_pad(img, self.size, fill=self.fill)
def __repr__(self) -> str:
return f"{self.__class__.__name__}(size={self.size})"
class ResizeKeepRatio:
""" Resize and Keep Ratio
"""
def __init__(
self,
size,
longest=0.,
interpolation='bilinear',
fill=0,
):
if isinstance(size, (list, tuple)):
self.size = tuple(size)
else:
self.size = (size, size)
self.interpolation = str_to_interp_mode(interpolation)
self.longest = float(longest)
self.fill = fill
@staticmethod
def get_params(img, target_size, longest):
"""Get parameters
Args:
img (PIL Image): Image to be cropped.
target_size (Tuple[int, int]): Size of output
Returns:
tuple: params (h, w) and (l, r, t, b) to be passed to ``resize`` and ``pad`` respectively
"""
source_size = img.size[::-1] # h, w
h, w = source_size
target_h, target_w = target_size
ratio_h = h / target_h
ratio_w = w / target_w
ratio = max(ratio_h, ratio_w) * longest + min(ratio_h, ratio_w) * (1. - longest)
size = [round(x / ratio) for x in source_size]
return size
def __call__(self, img):
"""
Args:
img (PIL Image): Image to be cropped and resized.
Returns:
PIL Image: Resized, padded to at least target size, possibly cropped to exactly target size
"""
size = self.get_params(img, self.size, self.longest)
img = F.resize(img, size, self.interpolation)
return img
def __repr__(self):
interpolate_str = interp_mode_to_str(self.interpolation)
format_string = self.__class__.__name__ + '(size={0}'.format(self.size)
format_string += f', interpolation={interpolate_str})'
format_string += f', longest={self.longest:.3f})'
return format_string
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/data/transforms_factory.py | """ Transforms Factory
Factory methods for building image transforms for use with TIMM (PyTorch Image Models)
Hacked together by / Copyright 2019, Ross Wightman
"""
import math
import torch
from torchvision import transforms
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
from timm.data.auto_augment import rand_augment_transform, augment_and_mix_transform, auto_augment_transform
from timm.data.transforms import str_to_interp_mode, str_to_pil_interp, RandomResizedCropAndInterpolation,\
ResizeKeepRatio, CenterCropOrPad, ToNumpy
from timm.data.random_erasing import RandomErasing
def transforms_noaug_train(
img_size=224,
interpolation='bilinear',
use_prefetcher=False,
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD,
):
if interpolation == 'random':
# random interpolation not supported with no-aug
interpolation = 'bilinear'
tfl = [
transforms.Resize(img_size, interpolation=str_to_interp_mode(interpolation)),
transforms.CenterCrop(img_size)
]
if use_prefetcher:
# prefetcher and collate will handle tensor conversion and norm
tfl += [ToNumpy()]
else:
tfl += [
transforms.ToTensor(),
transforms.Normalize(
mean=torch.tensor(mean),
std=torch.tensor(std))
]
return transforms.Compose(tfl)
def transforms_imagenet_train(
img_size=224,
scale=None,
ratio=None,
hflip=0.5,
vflip=0.,
color_jitter=0.4,
auto_augment=None,
interpolation='random',
use_prefetcher=False,
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD,
re_prob=0.,
re_mode='const',
re_count=1,
re_num_splits=0,
separate=False,
force_color_jitter=False,
):
"""
If separate==True, the transforms are returned as a tuple of 3 separate transforms
for use in a mixing dataset that passes
* all data through the first (primary) transform, called the 'clean' data
* a portion of the data through the secondary transform
* normalizes and converts the branches above with the third, final transform
"""
scale = tuple(scale or (0.08, 1.0)) # default imagenet scale range
ratio = tuple(ratio or (3./4., 4./3.)) # default imagenet ratio range
primary_tfl = [
RandomResizedCropAndInterpolation(img_size, scale=scale, ratio=ratio, interpolation=interpolation)]
if hflip > 0.:
primary_tfl += [transforms.RandomHorizontalFlip(p=hflip)]
if vflip > 0.:
primary_tfl += [transforms.RandomVerticalFlip(p=vflip)]
secondary_tfl = []
disable_color_jitter = False
if auto_augment:
assert isinstance(auto_augment, str)
# color jitter is typically disabled if AA/RA on,
# this allows override without breaking old hparm cfgs
disable_color_jitter = not (force_color_jitter or '3a' in auto_augment)
if isinstance(img_size, (tuple, list)):
img_size_min = min(img_size)
else:
img_size_min = img_size
aa_params = dict(
translate_const=int(img_size_min * 0.45),
img_mean=tuple([min(255, round(255 * x)) for x in mean]),
)
if interpolation and interpolation != 'random':
aa_params['interpolation'] = str_to_pil_interp(interpolation)
if auto_augment.startswith('rand'):
secondary_tfl += [rand_augment_transform(auto_augment, aa_params)]
elif auto_augment.startswith('augmix'):
aa_params['translate_pct'] = 0.3
secondary_tfl += [augment_and_mix_transform(auto_augment, aa_params)]
else:
secondary_tfl += [auto_augment_transform(auto_augment, aa_params)]
if color_jitter is not None and not disable_color_jitter:
# color jitter is enabled when not using AA or when forced
if isinstance(color_jitter, (list, tuple)):
# color jitter should be a 3-tuple/list if spec brightness/contrast/saturation
# or 4 if also augmenting hue
assert len(color_jitter) in (3, 4)
else:
# if it's a scalar, duplicate for brightness, contrast, and saturation, no hue
color_jitter = (float(color_jitter),) * 3
secondary_tfl += [transforms.ColorJitter(*color_jitter)]
final_tfl = []
if use_prefetcher:
# prefetcher and collate will handle tensor conversion and norm
final_tfl += [ToNumpy()]
else:
final_tfl += [
transforms.ToTensor(),
transforms.Normalize(
mean=torch.tensor(mean),
std=torch.tensor(std))
]
if re_prob > 0.:
final_tfl.append(
RandomErasing(re_prob, mode=re_mode, max_count=re_count, num_splits=re_num_splits, device='cpu'))
if separate:
return transforms.Compose(primary_tfl), transforms.Compose(secondary_tfl), transforms.Compose(final_tfl)
else:
return transforms.Compose(primary_tfl + secondary_tfl + final_tfl)
def transforms_imagenet_eval(
img_size=224,
crop_pct=None,
crop_mode=None,
interpolation='bilinear',
use_prefetcher=False,
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD
):
crop_pct = crop_pct or DEFAULT_CROP_PCT
if isinstance(img_size, (tuple, list)):
assert len(img_size) == 2
scale_size = tuple([math.floor(x / crop_pct) for x in img_size])
else:
scale_size = math.floor(img_size / crop_pct)
scale_size = (scale_size, scale_size)
if crop_mode == 'squash':
# squash mode scales each edge to 1/pct of target, then crops
# aspect ratio is not preserved, no img lost if crop_pct == 1.0
tfl = [
transforms.Resize(scale_size, interpolation=str_to_interp_mode(interpolation)),
transforms.CenterCrop(img_size),
]
elif crop_mode == 'border':
# scale the longest edge of image to 1/pct of target edge, add borders to pad, then crop
# no image lost if crop_pct == 1.0
fill = [round(255 * v) for v in mean]
tfl = [
ResizeKeepRatio(scale_size, interpolation=interpolation, longest=1.0),
CenterCropOrPad(img_size, fill=fill),
]
else:
# default crop model is center
# aspect ratio is preserved, crops center within image, no borders are added, image is lost
if scale_size[0] == scale_size[1]:
# simple case, use torchvision built-in Resize w/ shortest edge mode (scalar size arg)
tfl = [
transforms.Resize(scale_size[0], interpolation=str_to_interp_mode(interpolation))
]
else:
# resize shortest edge to matching target dim for non-square target
tfl = [ResizeKeepRatio(scale_size)]
tfl += [transforms.CenterCrop(img_size)]
if use_prefetcher:
# prefetcher and collate will handle tensor conversion and norm
tfl += [ToNumpy()]
else:
tfl += [
transforms.ToTensor(),
transforms.Normalize(
mean=torch.tensor(mean),
std=torch.tensor(std),
)
]
return transforms.Compose(tfl)
def create_transform(
input_size,
is_training=False,
use_prefetcher=False,
no_aug=False,
scale=None,
ratio=None,
hflip=0.5,
vflip=0.,
color_jitter=0.4,
auto_augment=None,
interpolation='bilinear',
mean=IMAGENET_DEFAULT_MEAN,
std=IMAGENET_DEFAULT_STD,
re_prob=0.,
re_mode='const',
re_count=1,
re_num_splits=0,
crop_pct=None,
crop_mode=None,
tf_preprocessing=False,
separate=False):
if isinstance(input_size, (tuple, list)):
img_size = input_size[-2:]
else:
img_size = input_size
if tf_preprocessing and use_prefetcher:
assert not separate, "Separate transforms not supported for TF preprocessing"
from timm.data.tf_preprocessing import TfPreprocessTransform
transform = TfPreprocessTransform(
is_training=is_training, size=img_size, interpolation=interpolation)
else:
if is_training and no_aug:
assert not separate, "Cannot perform split augmentation with no_aug"
transform = transforms_noaug_train(
img_size,
interpolation=interpolation,
use_prefetcher=use_prefetcher,
mean=mean,
std=std,
)
elif is_training:
transform = transforms_imagenet_train(
img_size,
scale=scale,
ratio=ratio,
hflip=hflip,
vflip=vflip,
color_jitter=color_jitter,
auto_augment=auto_augment,
interpolation=interpolation,
use_prefetcher=use_prefetcher,
mean=mean,
std=std,
re_prob=re_prob,
re_mode=re_mode,
re_count=re_count,
re_num_splits=re_num_splits,
separate=separate,
)
else:
assert not separate, "Separate transforms not supported for validation preprocessing"
transform = transforms_imagenet_eval(
img_size,
interpolation=interpolation,
use_prefetcher=use_prefetcher,
mean=mean,
std=std,
crop_pct=crop_pct,
crop_mode=crop_mode,
)
return transform
| 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet12k_synsets.txt | n00005787
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| 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet21k_goog_to_12k_indices.txt | 1
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| 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet21k_goog_to_22k_indices.txt | 0
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hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet22k_ms_synsets.txt | n01440764
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| 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet22k_ms_to_12k_indices.txt | 1001
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95
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100
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104
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105
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142
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2916
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2920
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144
2923
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2925
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2930
2931
2932
2933
2934
2935
145
2936
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146
2941
2943
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2947
2948
2949
2953
2954
2955
2956
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2960
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147
2965
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2970
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148
2980
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149
2987
2988
2989
2990
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2992
2993
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150
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3012
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3021
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151
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154
155
3024
156
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157
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159
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160
161
162
163
164
3030
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3033
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167
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3038
169
170
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171
172
173
174
176
177
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3041
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179
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181
182
183
184
185
186
187
3043
3044
3045
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188
3048
189
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190
191
192
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194
195
3050
196
197
198
199
200
201
3051
202
203
204
3053
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3055
3056
205
206
207
208
209
3057
210
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211
212
213
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3065
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222
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223
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224
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231
232
233
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235
3071
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236
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238
239
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241
242
3074
243
244
3075
245
246
247
3080
248
249
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251
252
253
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255
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257
3082
258
259
260
261
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263
264
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265
266
267
3087
269
270
271
272
3089
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273
274
275
276
3093
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277
3102
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278
279
280
3105
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3110
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281
282
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283
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284
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285
3121
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286
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287
3134
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288
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290
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3142
292
3144
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293
3150
294
3152
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3154
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296
297
3158
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298
299
3170
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3199
3200
3205
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3212
3213
3215
3219
3220
3222
3223
3225
3226
3228
3230
3231
300
301
3232
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3235
302
3237
3239
303
3241
304
3243
3244
305
3252
3255
3256
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306
3260
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3262
3263
3264
3265
3266
307
3279
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3287
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3301
308
3304
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3316
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3321
3322
3326
3329
3330
3332
3333
3335
3336
3340
3345
3346
3347
309
3349
3350
3353
3354
3355
3356
3358
3359
3360
3362
3364
3365
3367
3369
3370
3371
3372
3373
3374
3376
3379
3381
3382
3383
3384
3386
3389
310
3394
3395
3396
3397
3401
3404
311
3411
3412
3413
3415
3416
3418
312
3419
3421
3424
313
3425
314
3427
3428
3430
315
3432
3433
3434
3436
3444
3445
3446
3448
3451
3454
3458
3462
3464
3475
3479
3480
3489
316
3495
3497
3498
317
3502
3510
3511
3514
318
3516
3518
3521
3524
319
320
3531
3538
3539
3540
3541
3542
3543
3544
3545
321
3546
3547
3548
3549
3550
322
3552
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323
324
3560
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325
326
3563
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3567
3568
3570
3572
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3576
3577
3580
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3596
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3605
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3613
3617
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3622
3623
3626
3628
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3632
3633
3636
3637
3640
3643
3647
3649
3652
3653
3655
3657
3658
3662
3663
3664
3665
3666
3667
3669
3672
3673
3675
3678
3680
327
3681
3682
328
3684
3685
3689
329
3690
3692
3694
3695
3696
3697
3698
330
3699
331
3703
3704
3707
3708
332
3710
3713
3714
3715
3718
3720
3722
3723
3726
3727
3734
3736
3741
3746
3753
3755
333
3759
3760
334
3776
3782
3790
3794
3799
3800
3801
3802
335
3803
3804
3805
3806
3807
3808
3809
3810
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18454
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18459
18460
18461
18464
18465
18466
18468
18469
18472
18473
18474
18475
18476
18479
18482
18483
18486
18491
18492
18493
18495
18499
18500
18501
18502
18504
18505
18507
18508
18509
18510
18511
18512
18514
18519
18520
18521
18522
18523
18524
18525
18527
18528
18529
18530
18531
18535
18536
18537
18538
18542
18544
18545
18546
986
18548
18549
18551
18552
18553
18554
18555
18558
18559
18561
18562
18563
18565
18566
18567
18568
18570
18571
18572
18574
18577
18582
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18588
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18590
18593
18595
18597
18598
18599
18600
18601
18602
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18610
18613
18615
18616
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18622
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18624
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18643
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18669
18671
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18681
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18692
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18699
18703
18706
18708
18709
18710
18715
18717
18727
18735
18737
18740
18742
18743
18747
18750
18752
18753
18756
18765
18766
18767
18771
18775
18778
18779
18787
18792
18793
18794
18796
18803
987
18806
18807
18809
18812
18813
18816
18819
18820
18827
18828
18833
18836
18837
18839
18840
18845
18846
18847
18848
18852
18853
18854
18858
18859
18860
18861
18863
18864
18865
18866
18868
18869
18870
18871
18874
18875
18876
18877
18878
18879
18880
18881
18882
18883
18884
18885
18896
18897
18899
18901
18911
18913
18917
18919
18922
18924
18925
18926
18932
18935
18938
18939
18940
18944
18948
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18953
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18960
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18964
18965
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18968
18973
18990
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19000
19007
19023
19024
19027
19032
19038
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19044
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19050
19063
19067
19080
19081
19084
19086
19088
19089
19090
19091
19093
19094
19097
19098
19099
19103
19104
19107
19111
988
19114
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19250
19254
19262
19264
19267
19268
19269
19270
19271
19272
19274
19275
19276
19277
19280
19281
19285
19286
19287
19294
19299
19300
19302
19303
19305
19306
19307
19310
19312
19314
19315
19316
19317
19320
19321
19326
19327
19328
19330
19331
19334
19335
19340
19341
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19352
19353
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19355
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19359
19360
19361
19362
19364
19366
19370
19373
19374
19375
19376
19377
19379
19381
19383
19384
19387
19389
19391
19392
19395
19396
19398
19412
19413
19414
19415
19417
19424
19427
19429
19431
19433
19444
19445
19446
19447
19450
19451
19452
19456
19457
19458
19460
19461
19462
19463
19466
19467
19469
19471
19481
19482
19483
19487
19488
19491
19493
19494
19495
19496
19500
19502
19504
19505
19506
19507
19508
19511
19512
19513
19516
19518
19519
19521
19523
19524
19526
19527
19528
19529
19530
19531
19532
19534
19535
19536
19537
19538
19540
19542
19548
19549
19551
19552
19553
19556
19557
19558
19559
19560
19561
19563
19565
19566
19567
19573
19576
19577
19578
19585
19587
19588
19592
19593
19594
19597
19598
19600
19602
19607
19616
19617
19618
19621
19623
19624
19625
19626
19628
19632
19634
19637
19638
19639
19640
19642
19643
19644
19645
19648
19650
19651
19652
19653
19654
19658
19659
19665
19666
19673
19677
19678
19679
19680
19682
19683
19685
19686
19687
19688
19689
19691
19692
19693
19694
19697
19698
19699
19700
19702
19703
19704
19706
19707
19708
19709
19715
19717
19719
19720
19724
19727
19728
19730
19731
19732
19734
19738
19742
19743
19744
19747
19748
19751
19755
19756
19766
19767
19768
19769
19773
19774
19777
19779
19780
19783
19784
19787
19792
19802
19805
19807
19808
19811
19812
19815
19823
19827
19829
19841
19843
19845
19847
19848
19849
19850
19851
19855
19862
19868
19871
19872
19873
19874
19875
19876
19877
19881
19882
19883
19884
19889
19892
19897
19899
19904
19905
19906
19907
19912
19915
19917
19918
19919
19920
19924
19926
19927
19928
19932
19934
19936
19937
19938
19939
19940
19943
19945
19948
19953
19955
19956
19957
19962
19963
19966
19967
19974
19977
19978
19979
19980
19981
19982
19983
19984
19985
19986
19988
19995
19998
20000
20002
20007
20008
20009
20011
20018
20020
20028
989
20029
20030
20033
20034
20035
20038
20039
20041
20046
20051
20052
20053
20054
20056
20058
20059
20062
20063
20064
20065
20066
20067
20070
20071
20072
20073
20075
20077
20079
20080
20083
20090
20091
20093
20095
20096
20101
20102
20106
20107
20109
20111
20112
20113
20117
20119
20120
20124
20125
20126
20127
20131
20132
20135
20136
20137
20138
20149
20150
20152
20154
20156
20157
20158
20162
20163
20164
20171
20172
20174
20180
20181
20183
20187
20194
20196
20201
20202
20204
20207
20208
20216
20218
20220
20223
20224
20225
20227
20228
20229
20230
20231
20232
20233
20234
20235
20237
20239
20240
20241
20242
20245
20248
20249
20257
20259
20260
20266
20271
20274
20275
20276
20278
20280
20287
20289
20290
20291
20292
20293
20295
20296
20297
20298
20299
20300
20304
20305
20306
20309
20312
20314
20320
20321
20326
20327
20328
20334
20335
20336
20337
20339
20342
20344
20346
20356
20357
20358
20364
20365
20366
20367
20369
20371
20372
20373
20374
20375
20376
20377
20382
20383
20385
20388
20390
20394
20395
20399
20401
20403
20406
20407
20408
20409
20414
20415
20416
20417
20418
20419
20422
20423
20424
20425
20426
20427
20429
20431
20433
20434
20436
20446
20450
20453
20455
20458
20461
20465
20466
20469
990
20479
20480
20481
20483
20484
20486
20488
20489
20493
20497
20500
20501
20502
20503
20506
20507
20508
20512
20513
20514
20519
20520
20523
20524
20525
20526
20527
20528
20531
20532
20534
20536
20537
20538
20542
20546
20550
20551
20554
20557
20558
20560
20561
20562
20563
20565
20566
20570
20571
20574
20576
20577
20580
20583
20585
20586
20589
20591
20594
20597
20598
20599
20600
20602
20603
20604
20605
20606
20609
20611
20612
20614
20615
20617
20621
20622
20625
20626
20627
20629
20630
20632
20634
20636
20637
20638
20639
20643
20647
20649
20650
20651
20652
20659
20660
20662
20664
20668
20669
20670
20671
20672
20673
20674
20675
20676
20677
20678
20680
20687
20688
20690
20692
20695
20696
20697
20698
20700
20701
20702
20703
20704
20709
20710
20712
20713
20714
20715
20718
20719
20720
20721
20722
20723
20724
20725
20727
20728
20732
20733
20735
20736
20740
20741
20742
20746
20750
20757
20762
20763
20764
20765
20771
20772
20775
20776
20785
20791
20794
20795
20796
20797
20798
20799
20800
20801
20802
20805
20806
20808
20814
20816
20817
20820
20822
20827
20831
20832
20833
20838
20839
20841
20844
20845
20847
20848
20851
20855
20856
20858
20859
20860
20861
20862
20863
20867
20868
20869
20871
20874
20878
20881
20884
20885
20888
20889
20890
20892
20894
20898
20903
20905
20906
20908
20910
20911
20912
20914
20916
20919
20921
20925
20927
20928
20929
20934
20935
20944
20945
20946
20948
20949
20950
20951
20954
20955
20957
20958
20963
20964
20967
20971
20976
20987
20988
20989
20990
20993
20996
20997
21006
21009
21010
21015
21017
21020
21022
21025
21032
21033
991
21034
21035
21038
21042
21044
21045
21046
21047
21049
21050
21051
992
21052
21053
21054
21055
21056
21057
21059
21060
21061
21062
21063
21064
21065
21069
21070
21071
21072
21073
21074
21075
21078
21082
21084
21089
21090
21092
21093
21094
21098
21100
21106
21107
21118
21119
21121
21125
21127
21128
21130
21131
21134
21139
21142
21144
21151
21152
21153
21154
21155
21157
21159
21162
21164
21165
993
21171
21174
21175
994
21176
21178
21183
21184
995
21189
21192
21193
996
21199
21200
21201
997
21202
21203
21205
21220
21222
21225
21230
21233
21239
21253
21258
21269
21270
21272
21280
21282
21283
21285
21287
21288
21298
21304
21306
21308
21312
21313
21314
21316
21317
21318
21319
21320
21322
21328
21337
21338
21340
21344
21346
21350
21353
21357
21358
21359
21362
21364
998
21370
21374
21376
21377
21378
21382
21386
21388
21389
21397
21398
21402
21407
21408
21411
21414
21415
21419
21425
21428
21429
21431
21432
21433
21438
21441
21451
21459
21464
21467
21469
21476
21479
21484
21485
21486
21489
21494
21495
21497
21501
21502
21507
21511
21515
21516
21517
21519
21522
21524
21528
21529
21532
21533
21534
21537
21541
21545
21547
21548
21549
21550
21554
21560
21563
21569
21573
21576
21578
21579
21580
21581
21585
21589
21590
21591
21598
21601
21604
21606
21611
21615
21618
21620
21623
21625
21627
21635
21637
21638
21641
21644
21645
21648
21649
21650
21659
21661
21662
21663
21665
21666
21668
21669
21672
21673
21675
21678
21679
21680
21686
21688
21689
21690
21692
21702
21711
21712
21713
21716
21717
21721
21722
21723
21724
21727
21728
21729
21734
21739
21740
21741
21743
21744
21747
21748
21749
21754
21757
21758
21760
21761
21762
21763
21765
21772
21773
21774
21777
21778
21781
21782
21784
21786
21791
21792
21795
21796
21800
21801
21803
21804
21806
21811
21815
21816
999
21817
21818
21822
21825
21826
21827
21828
21829
21830
21831
21833
21835
21837
21838
21840
21841
| 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet22k_ms_to_22k_indices.txt | 1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
0
1359
1360
1361
1362
1363
1364
1365
1366
1
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
2
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
3
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
4
1482
1483
1484
1485
1486
5
1487
1488
1489
6
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
7
1507
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9
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40
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41
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42
43
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62
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63
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65
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66
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67
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68
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69
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79
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80
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84
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87
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90
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91
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92
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95
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97
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98
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99
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100
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101
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104
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2500
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105
106
2510
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107
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108
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109
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110
2606
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111
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2641
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112
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113
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114
2650
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115
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116
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117
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118
119
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120
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121
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122
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123
124
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125
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126
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127
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129
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132
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133
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135
2800
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136
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137
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139
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140
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141
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142
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143
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144
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145
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147
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148
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149
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2990
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150
2997
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2999
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151
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3023
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159
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160
161
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164
3029
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165
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166
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277
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286
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287
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3210
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300
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307
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309
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| 0 |
hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet22k_to_12k_indices.txt | 1
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hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet_a_indices.txt | 6
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hf_public_repos/pytorch-image-models/timm/data | hf_public_repos/pytorch-image-models/timm/data/_info/imagenet_a_synsets.txt | n01498041
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