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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: other |
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datasets: |
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- imagenet-1k |
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--- |
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# Model card for mobilevitv2_100.cvnets_in1k |
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A MobileViT-v2 image classification model. Trained on ImageNet-1k by paper authors. |
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See license details at https://github.com/apple/ml-cvnets/blob/main/LICENSE |
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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): 4.9 |
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- GMACs: 1.8 |
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- Activations (M): 16.1 |
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- Image size: 256 x 256 |
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- **Papers:** |
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- Separable Self-attention for Mobile Vision Transformers: https://arxiv.org/abs/2206.02680 |
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- **Original:** https://github.com/apple/ml-cvnets |
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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(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('mobilevitv2_100.cvnets_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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'mobilevitv2_100.cvnets_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, 128, 128]) |
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# torch.Size([1, 128, 64, 64]) |
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# torch.Size([1, 256, 32, 32]) |
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# torch.Size([1, 384, 16, 16]) |
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# torch.Size([1, 512, 8, 8]) |
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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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'mobilevitv2_100.cvnets_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, 512, 8, 8) 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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Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). |
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## Citation |
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```bibtex |
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@article{Mehta2022SeparableSF, |
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title={Separable Self-attention for Mobile Vision Transformers}, |
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author={Sachin Mehta and Mohammad Rastegari}, |
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journal={ArXiv}, |
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year={2022}, |
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volume={abs/2206.02680} |
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} |
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``` |
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