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  1. README.md +151 -0
  2. config.json +35 -0
  3. model.safetensors +3 -0
  4. pytorch_model.bin +3 -0
README.md ADDED
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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_tag: timm
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+ license: apache-2.0
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+ datasets:
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+ - imagenet-21k
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+ ---
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+ # Model card for resnetv2_101x3_bit.goog_in21k
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+
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+ A ResNet-V2-BiT (Big Transfer w/ pre-activation ResNet) image classification model. Trained on ImageNet-21k by paper authors.
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+
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+ This model uses:
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+ * Group Normalization (GN) in combination with Weight Standardization (WS) instead of Batch Normalization (BN)..
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+
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+
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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): 516.0
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+ - GMACs: 71.4
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+ - Activations (M): 48.7
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+ - Image size: 224 x 224
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+ - **Papers:**
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+ - Big Transfer (BiT): General Visual Representation Learning: https://arxiv.org/abs/1912.11370
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+ - Identity Mappings in Deep Residual Networks: https://arxiv.org/abs/1603.05027
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+ - **Dataset:** ImageNet-21k
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+ - **Original:** https://github.com/google-research/big_transfer
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+
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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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+
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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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+
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+ model = timm.create_model('resnetv2_101x3_bit.goog_in21k', pretrained=True)
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+ model = model.eval()
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+
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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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+
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+ output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
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+
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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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+
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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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+
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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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+
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+ model = timm.create_model(
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+ 'resnetv2_101x3_bit.goog_in21k',
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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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+
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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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+
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+ output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
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+
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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, 192, 112, 112])
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+ # torch.Size([1, 768, 56, 56])
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+ # torch.Size([1, 1536, 28, 28])
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+ # torch.Size([1, 3072, 14, 14])
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+ # torch.Size([1, 6144, 7, 7])
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+
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+ print(o.shape)
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+ ```
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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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+
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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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+
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+ model = timm.create_model(
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+ 'resnetv2_101x3_bit.goog_in21k',
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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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+
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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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+
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+ output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
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+
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+ # or equivalently (without needing to set num_classes=0)
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+
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+ output = model.forward_features(transforms(img).unsqueeze(0))
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+ # output is unpooled, a (1, 6144, 7, 7) shaped tensor
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+
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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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+
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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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+
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+ ## Citation
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+ ```bibtex
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+ @inproceedings{Kolesnikov2019BigT,
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+ title={Big Transfer (BiT): General Visual Representation Learning},
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+ author={Alexander Kolesnikov and Lucas Beyer and Xiaohua Zhai and Joan Puigcerver and Jessica Yung and Sylvain Gelly and Neil Houlsby},
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+ booktitle={European Conference on Computer Vision},
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+ year={2019}
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+ }
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+ ```
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+ ```bibtex
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+ @article{He2016,
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+ author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
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+ title = {Identity Mappings in Deep Residual Networks},
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+ journal = {arXiv preprint arXiv:1603.05027},
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+ year = {2016}
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+ }
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+ ```
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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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+ ```
config.json ADDED
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+ {
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+ "architecture": "resnetv2_101x3_bit",
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+ "num_classes": 21843,
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+ "num_features": 6144,
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+ "pretrained_cfg": {
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+ "tag": "goog_in21k",
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+ "custom_load": true,
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+ "input_size": [
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+ 3,
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+ 224,
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+ 224
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+ ],
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+ "fixed_input_size": false,
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+ "interpolation": "bilinear",
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+ "crop_pct": 0.875,
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+ "crop_mode": "center",
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+ "mean": [
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+ 0.5,
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+ 0.5,
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+ 0.5
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+ ],
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+ "std": [
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+ 0.5,
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+ 0.5,
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+ 0.5
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+ ],
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+ "num_classes": 21843,
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+ "pool_size": [
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+ 7,
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+ 7
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+ ],
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+ "first_conv": "stem.conv",
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+ "classifier": "head.fc"
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+ }
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+ }
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