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---
tags:
- image-classification
- timm
library_name: timm
license: mit
datasets:
- imagenet-1k
---
# Model card for edgenext_base.usi_in1k
An EdgeNeXt image classification model. Trained on ImageNet-1k by paper authors using distillation (`USI` as per `Solving ImageNet`).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 18.5
- GMACs: 3.8
- Activations (M): 15.6
- Image size: train = 256 x 256, test = 320 x 320
- **Papers:**
- EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications: https://arxiv.org/abs/2206.10589
- Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results: https://arxiv.org/abs/2204.03475
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/mmaaz60/EdgeNeXt
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('edgenext_base.usi_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'edgenext_base.usi_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 80, 64, 64])
# torch.Size([1, 160, 32, 32])
# torch.Size([1, 288, 16, 16])
# torch.Size([1, 584, 8, 8])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'edgenext_base.usi_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 584, 8, 8) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Citation
```bibtex
@inproceedings{Maaz2022EdgeNeXt,
title={EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications},
author={Muhammad Maaz and Abdelrahman Shaker and Hisham Cholakkal and Salman Khan and Syed Waqas Zamir and Rao Muhammad Anwer and Fahad Shahbaz Khan},
booktitle={International Workshop on Computational Aspects of Deep Learning at 17th European Conference on Computer Vision (CADL2022)},
year={2022},
organization={Springer}
}
```
```bibtex
@misc{https://doi.org/10.48550/arxiv.2204.03475,
doi = {10.48550/ARXIV.2204.03475},
url = {https://arxiv.org/abs/2204.03475},
author = {Ridnik, Tal and Lawen, Hussam and Ben-Baruch, Emanuel and Noy, Asaf},
keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results},
publisher = {arXiv},
year = {2022},
}
```
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