# 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% -->