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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: mit |
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datasets: |
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- imagenet-1k |
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--- |
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# Model card for repvgg_b1g4.rvgg_in1k |
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A RepVGG image classification model. Trained on ImageNet-1k by paper authors. |
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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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BYOBNet allows configuration of: |
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* block / stage layout |
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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): 40.0 |
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- GMACs: 8.1 |
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- Activations (M): 10.6 |
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- Image size: 224 x 224 |
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- **Papers:** |
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- RepVGG: Making VGG-style ConvNets Great Again: https://arxiv.org/abs/2101.03697 |
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- **Dataset:** ImageNet-1k |
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- **Original:** https://github.com/DingXiaoH/RepVGG |
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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('repvgg_b1g4.rvgg_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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'repvgg_b1g4.rvgg_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, 128, 56, 56]) |
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# torch.Size([1, 256, 28, 28]) |
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# torch.Size([1, 512, 14, 14]) |
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# torch.Size([1, 2048, 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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'repvgg_b1g4.rvgg_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, 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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## 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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@inproceedings{ding2021repvgg, |
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title={Repvgg: Making vgg-style convnets great again}, |
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author={Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian}, |
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
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pages={13733--13742}, |
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year={2021} |
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} |
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``` |
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