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--- |
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tags: |
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- image-classification |
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- timm |
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library_tag: timm |
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license: apache-2.0 |
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datasets: |
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- imagenet-1k |
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--- |
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# Model card for xcit_tiny_12_p8_224.fb_dist_in1k |
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A XCiT (Cross-Covariance Image Transformer) 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): 6.7 |
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- GMACs: 4.8 |
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- Activations (M): 23.6 |
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- Image size: 224 x 224 |
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- **Papers:** |
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- XCiT: Cross-Covariance Image Transformers: https://arxiv.org/abs/2106.09681 |
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- **Dataset:** ImageNet-1k |
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- **Original:** https://github.com/facebookresearch/xcit |
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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('xcit_tiny_12_p8_224.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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'xcit_tiny_12_p8_224.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, 785, 192) 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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@article{el2021xcit, |
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title={XCiT: Cross-Covariance Image Transformers}, |
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author={El-Nouby, Alaaeldin and Touvron, Hugo and Caron, Mathilde and Bojanowski, Piotr and Douze, Matthijs and Joulin, Armand and Laptev, Ivan and Neverova, Natalia and Synnaeve, Gabriel and Verbeek, Jakob and others}, |
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journal={arXiv preprint arXiv:2106.09681}, |
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year={2021} |
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} |
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``` |
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