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--- |
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license: apache-2.0 |
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library_name: timm |
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tags: |
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- image-classification |
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- timm |
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datasets: |
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- imagenet-1k |
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--- |
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# Model card for efficientformerv2_s0.snap_dist_in1k |
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A EfficientFormer-V2 image classification model. Pretrained with distillation on ImageNet-1k. |
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## Model Details |
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- **Model Type:** Image classification / feature backbone |
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- **Model Stats:** |
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- Params (M): 3.6 |
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- GMACs: 0.4 |
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- Activations (M): 5.3 |
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- Image size: 224 x 224 |
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- **Original:** https://github.com/snap-research/EfficientFormer |
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- **Papers:** |
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- Rethinking Vision Transformers for MobileNet Size and Speed: https://arxiv.org/abs/2212.08059 |
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- **Dataset:** ImageNet-1k |
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## Model Usage |
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### Image Classification |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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img = Image.open( |
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urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) |
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model = timm.create_model('efficientformerv2_s0.snap_dist_in1k', pretrained=True) |
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model = model.eval() |
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 |
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top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) |
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``` |
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### Image Embeddings |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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img = Image.open( |
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urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) |
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model = timm.create_model( |
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'efficientformerv2_s0.snap_dist_in1k', |
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pretrained=True, |
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num_classes=0, # remove classifier nn.Linear |
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) |
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model = model.eval() |
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor |
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# or equivalently (without needing to set num_classes=0) |
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output = model.forward_features(transforms(img).unsqueeze(0)) |
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# output is unpooled (ie.e a (batch_size, num_features, H, W) tensor |
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output = model.forward_head(output, pre_logits=True) |
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# output is (batch_size, num_features) tensor |
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``` |
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### Feature Map Extraction |
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```python |
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from urllib.request import urlopen |
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from PIL import Image |
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import timm |
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img = Image.open( |
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urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) |
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model = timm.create_model( |
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'efficientformerv2_s0.snap_dist_in1k', |
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pretrained=True, |
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features_only=True, |
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) |
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model = model.eval() |
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# get model specific transforms (normalization, resize) |
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data_config = timm.data.resolve_model_data_config(model) |
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transforms = timm.data.create_transform(**data_config, is_training=False) |
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output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 |
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for o in output: |
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# print shape of each feature map in output |
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# e.g. for efficientformerv2_l: |
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# torch.Size([2, 40, 56, 56]) |
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# torch.Size([2, 80, 28, 28]) |
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# torch.Size([2, 192, 14, 14]) |
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# torch.Size([2, 384, 7, 7]) |
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print(o.shape) |
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``` |
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## Model Comparison |
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|model |top1 |top5 |param_count|img_size| |
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|-----------------------------------|------|------|-----------|--------| |
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|efficientformerv2_l.snap_dist_in1k |83.628|96.54 |26.32 |224 | |
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|efficientformer_l7.snap_dist_in1k |83.368|96.534|82.23 |224 | |
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|efficientformer_l3.snap_dist_in1k |82.572|96.24 |31.41 |224 | |
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|efficientformerv2_s2.snap_dist_in1k|82.128|95.902|12.71 |224 | |
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|efficientformer_l1.snap_dist_in1k |80.496|94.984|12.29 |224 | |
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|efficientformerv2_s1.snap_dist_in1k|79.698|94.698|6.19 |224 | |
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|efficientformerv2_s0.snap_dist_in1k|76.026|92.77 |3.6 |224 | |
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## Citation |
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```bibtex |
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@article{li2022rethinking, |
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title={Rethinking Vision Transformers for MobileNet Size and Speed}, |
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author={Li, Yanyu and Hu, Ju and Wen, Yang and Evangelidis, Georgios and Salahi, Kamyar and Wang, Yanzhi and Tulyakov, Sergey and Ren, Jian}, |
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journal={arXiv preprint arXiv:2212.08059}, |
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year={2022} |
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} |
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``` |
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```bibtex |
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@misc{rw2019timm, |
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author = {Ross Wightman}, |
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title = {PyTorch Image Models}, |
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year = {2019}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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doi = {10.5281/zenodo.4414861}, |
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howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} |
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} |
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``` |
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