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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 cait_m36_384.fb_dist_in1k |
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A CaiT (Class-Attention in Image Transformers) image classification model. Pretrained on ImageNet-1k with distillation by paper authors. |
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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): 271.2 |
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- GMACs: 173.1 |
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- Activations (M): 734.8 |
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- Image size: 384 x 384 |
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- **Papers:** |
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- Going deeper with Image Transformers: https://arxiv.org/abs/2103.17239 |
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- **Dataset:** ImageNet-1k |
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- **Original:** https://github.com/facebookresearch/deit |
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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(urlopen( |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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)) |
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model = timm.create_model('cait_m36_384.fb_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(urlopen( |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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)) |
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model = timm.create_model( |
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'cait_m36_384.fb_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, a (1, 577, 768) shaped tensor |
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output = model.forward_head(output, pre_logits=True) |
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# output is a (1, num_features) shaped tensor |
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``` |
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## Citation |
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```bibtex |
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@InProceedings{Touvron_2021_ICCV, |
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author = {Touvron, Hugo and Cord, Matthieu and Sablayrolles, Alexandre and Synnaeve, Gabriel and J'egou, Herv'e}, |
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title = {Going Deeper With Image Transformers}, |
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booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, |
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month = {October}, |
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year = {2021}, |
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pages = {32-42} |
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} |
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``` |
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