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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 ese_vovnet19b_dw.ra_in1k |
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A VoVNet-v2 image classification model. Pretrained on ImageNet-1k in `timm` by Ross Wightman using RandAugment `RA` recipe. Related to `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476). |
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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): 6.5 |
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- GMACs: 1.3 |
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- Activations (M): 8.2 |
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- Image size: train = 224 x 224, test = 288 x 288 |
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- **Papers:** |
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- An Energy and GPU-Computation Efficient Backbone Network: https://arxiv.org/abs/1904.09730 |
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- CenterMask : Real-Time Anchor-Free Instance Segmentation: https://arxiv.org/abs/1911.06667 |
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- ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 |
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- **Dataset:** ImageNet-1k |
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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('ese_vovnet19b_dw.ra_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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'ese_vovnet19b_dw.ra_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, 112, 112]) |
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# torch.Size([1, 256, 56, 56]) |
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# torch.Size([1, 512, 28, 28]) |
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# torch.Size([1, 768, 14, 14]) |
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# torch.Size([1, 1024, 7, 7]) |
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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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'ese_vovnet19b_dw.ra_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, 1024, 7, 7) 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{lee2019energy, |
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title = {An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection}, |
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author = {Lee, Youngwan and Hwang, Joong-won and Lee, Sangrok and Bae, Yuseok and Park, Jongyoul}, |
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booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops}, |
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year = {2019} |
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} |
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``` |
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```bibtex |
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@article{lee2019centermask, |
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title={CenterMask: Real-Time Anchor-Free Instance Segmentation}, |
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author={Lee, Youngwan and Park, Jongyoul}, |
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booktitle={CVPR}, |
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year={2020} |
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} |
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``` |
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```bibtex |
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@inproceedings{wightman2021resnet, |
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title={ResNet strikes back: An improved training procedure in timm}, |
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author={Wightman, Ross and Touvron, Hugo and Jegou, Herve}, |
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booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future} |
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} |
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``` |
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