--- license: apache-2.0 library_name: timm tags: - image-classification - timm datasets: - imagenet-1k - imagenet-21k --- # Model card for resnetv2_152x4_bit.goog_in21k_ft_in1k A ResNet-V2-BiT (Big Transfer w/ pre-activation ResNet) image classification model. Pretrained on ImageNet-21k and fine-tuned on ImageNet-1k by paper authors. This model uses: * Group Normalization (GN) in combination with Weight Standardization (WS) instead of Batch Normalization (BN).. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 936.5 - GMACs: 844.8 - Activations (M): 414.3 - Image size: 480 x 480 - **Papers:** - Big Transfer (BiT): General Visual Representation Learning: https://arxiv.org/abs/1912.11370 - Identity Mappings in Deep Residual Networks: https://arxiv.org/abs/1603.05027 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-21k - **Original:** https://github.com/google-research/big_transfer ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('resnetv2_152x4_bit.goog_in21k_ft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'resnetv2_152x4_bit.goog_in21k_ft_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 256, 240, 240]) # torch.Size([1, 1024, 120, 120]) # torch.Size([1, 2048, 60, 60]) # torch.Size([1, 4096, 30, 30]) # torch.Size([1, 8192, 15, 15]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'resnetv2_152x4_bit.goog_in21k_ft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 8192, 15, 15) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{Kolesnikov2019BigT, title={Big Transfer (BiT): General Visual Representation Learning}, author={Alexander Kolesnikov and Lucas Beyer and Xiaohua Zhai and Joan Puigcerver and Jessica Yung and Sylvain Gelly and Neil Houlsby}, booktitle={European Conference on Computer Vision}, year={2019} } ``` ```bibtex @article{He2016, author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun}, title = {Identity Mappings in Deep Residual Networks}, journal = {arXiv preprint arXiv:1603.05027}, year = {2016} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```