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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 halonet50ts.a1h_in1k |
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A HaloNet image classification model (, based on ResNet architecture). Trained on ImageNet-1k in `timm` by Ross Wightman. |
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NOTE: this model did not adhere to any specific paper configuration, it was tuned for reasonable training times and reduced frequency of self-attention blocks. |
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Recipe details: |
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* Based on [ResNet Strikes Back](https://arxiv.org/abs/2110.00476) `A1` recipe |
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* LAMB optimizer |
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* Stronger dropout, stochastic depth, and RandAugment than paper `A1` recipe |
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* Cosine LR schedule with warmup |
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This model architecture is implemented using `timm`'s flexible [BYOBNet (Bring-Your-Own-Blocks Network)](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/byobnet.py). |
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BYOB (with BYOANet attention specific blocks) allows configuration of: |
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* block / stage layout |
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* block-type interleaving |
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* stem layout |
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* output stride (dilation) |
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* activation and norm layers |
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* channel and spatial / self-attention layers |
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...and also includes `timm` features common to many other architectures, including: |
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* stochastic depth |
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* gradient checkpointing |
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* layer-wise LR decay |
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* per-stage feature extraction |
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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): 22.7 |
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- GMACs: 5.3 |
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- Activations (M): 19.2 |
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- Image size: 256 x 256 |
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- **Papers:** |
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- Scaling Local Self-Attention for Parameter Efficient Visual Backbones: https://arxiv.org/abs/2103.12731 |
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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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## 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('halonet50ts.a1h_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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'halonet50ts.a1h_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, 256, 64, 64]) |
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# torch.Size([1, 512, 32, 32]) |
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# torch.Size([1, 1024, 16, 16]) |
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# torch.Size([1, 2048, 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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'halonet50ts.a1h_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, 2048, 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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@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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```bibtex |
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@article{Vaswani2021ScalingLS, |
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title={Scaling Local Self-Attention for Parameter Efficient Visual Backbones}, |
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author={Ashish Vaswani and Prajit Ramachandran and A. Srinivas and Niki Parmar and Blake A. Hechtman and Jonathon Shlens}, |
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journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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year={2021}, |
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pages={12889-12899} |
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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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