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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 davit_small.msft_in1k |
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A DaViT image classification model. Trained on ImageNet-1k by paper authors. |
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Thanks to [Fredo Guan](https://github.com/fffffgggg54) for bringing the classification backbone to `timm`. |
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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): 49.7 |
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- GMACs: 8.8 |
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- Activations (M): 30.5 |
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- Image size: 224 x 224 |
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- **Papers:** |
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- DaViT: Dual Attention Vision Transformers: https://arxiv.org/abs/2204.03645 |
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- **Original:** https://github.com/dingmyu/davit |
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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('davit_small.msft_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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### 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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'davit_small.msft_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.: |
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# torch.Size([1, 96, 56, 56]) |
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# torch.Size([1, 192, 28, 28]) |
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# torch.Size([1, 384, 14, 14]) |
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# torch.Size([1, 768, 7, 7] |
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print(o.shape) |
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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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'davit_small.msft_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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## Model Comparison |
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### By Top-1 |
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|model |top1 |top1_err|top5 |top5_err|param_count|img_size|crop_pct|interpolation| |
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|---------------------|------|--------|------|--------|-----------|--------|--------|-------------| |
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|davit_base.msft_in1k |84.634|15.366 |97.014|2.986 |87.95 |224 |0.95 |bicubic | |
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|davit_small.msft_in1k|84.25 |15.75 |96.94 |3.06 |49.75 |224 |0.95 |bicubic | |
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|davit_tiny.msft_in1k |82.676|17.324 |96.276|3.724 |28.36 |224 |0.95 |bicubic | |
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## Citation |
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```bibtex |
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@inproceedings{ding2022davit, |
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title={DaViT: Dual Attention Vision Transformer}, |
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author={Ding, Mingyu and Xiao, Bin and Codella, Noel and Luo, Ping and Wang, Jingdong and Yuan, Lu}, |
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booktitle={ECCV}, |
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year={2022}, |
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} |
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``` |
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