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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 volo_d2_384.sail_in1k |
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A VOLO (Vision Outlooker) image classification model. Trained on ImageNet-1k with token labelling 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): 58.9 |
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- GMACs: 46.2 |
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- Activations (M): 184.5 |
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- Image size: 384 x 384 |
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- **Papers:** |
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- VOLO: Vision Outlooker for Visual Recognition: https://arxiv.org/abs/2106.13112 |
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- **Dataset:** ImageNet-1k |
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- **Original:** https://github.com/sail-sg/volo |
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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('volo_d2_384.sail_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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'volo_d2_384.sail_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, 512) 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{yuan2022volo, |
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title={Volo: Vision outlooker for visual recognition}, |
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author={Yuan, Li and Hou, Qibin and Jiang, Zihang and Feng, Jiashi and Yan, Shuicheng}, |
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journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, |
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year={2022}, |
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publisher={IEEE} |
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} |
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``` |
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