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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_name: 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 test_vit2.r160_in1k |
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A very small test Vision Transformer image classification model for testing and sanity checks. Trained on ImageNet-1k by Ross Wightman. |
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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): 0.5 |
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- GMACs: 0.0 |
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- Activations (M): 0.4 |
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- Image size: 160 x 160 |
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- **Dataset:** ImageNet-1k |
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- **Papers:** |
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- PyTorch Image Models: https://github.com/huggingface/pytorch-image-models |
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- **Original:** https://github.com/huggingface/pytorch-image-models |
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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('test_vit2.r160_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(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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'test_vit2.r160_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, 64, 10, 10]) |
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# torch.Size([1, 64, 10, 10]) |
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# torch.Size([1, 64, 10, 10]) |
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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(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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'test_vit2.r160_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, 101, 64) 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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## Model Comparison |
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### By Top-1 |
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|model |img_size|top1 |top5 |param_count| |
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|--------------------------------|--------|------|------|-----------| |
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|test_convnext3.r160_in1k |192 |54.558|79.356|0.47 | |
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|test_convnext2.r160_in1k |192 |53.62 |78.636|0.48 | |
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|test_convnext2.r160_in1k |160 |53.51 |78.526|0.48 | |
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|test_convnext3.r160_in1k |160 |53.328|78.318|0.47 | |
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|test_convnext.r160_in1k |192 |48.532|74.944|0.27 | |
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|test_nfnet.r160_in1k |192 |48.298|73.446|0.38 | |
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|test_convnext.r160_in1k |160 |47.764|74.152|0.27 | |
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|test_nfnet.r160_in1k |160 |47.616|72.898|0.38 | |
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|test_efficientnet.r160_in1k |192 |47.164|71.706|0.36 | |
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|test_efficientnet_evos.r160_in1k|192 |46.924|71.53 |0.36 | |
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|test_byobnet.r160_in1k |192 |46.688|71.668|0.46 | |
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|test_efficientnet_evos.r160_in1k|160 |46.498|71.006|0.36 | |
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|test_efficientnet.r160_in1k |160 |46.454|71.014|0.36 | |
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|test_byobnet.r160_in1k |160 |45.852|70.996|0.46 | |
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|test_efficientnet_ln.r160_in1k |192 |44.538|69.974|0.36 | |
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|test_efficientnet_gn.r160_in1k |192 |44.448|69.75 |0.36 | |
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|test_efficientnet_ln.r160_in1k |160 |43.916|69.404|0.36 | |
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|test_efficientnet_gn.r160_in1k |160 |43.88 |69.162|0.36 | |
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|test_vit2.r160_in1k |192 |43.454|69.798|0.46 | |
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|test_resnet.r160_in1k |192 |42.376|68.744|0.47 | |
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|test_vit2.r160_in1k |160 |42.232|68.982|0.46 | |
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|test_vit.r160_in1k |192 |41.984|68.64 |0.37 | |
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|test_resnet.r160_in1k |160 |41.578|67.956|0.47 | |
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|test_vit.r160_in1k |160 |40.946|67.362|0.37 | |
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## Citation |
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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/huggingface/pytorch-image-models}} |
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} |
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``` |
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