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@ -0,0 +1,106 @@ |
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<h1 align="left">ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation<a href="https://arxiv.org/abs/2204.12484"><img src="https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg" ></a> </h1> |
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[](https://paperswithcode.com/sota/pose-estimation-on-coco-test-dev?p=vitpose-simple-vision-transformer-baselines) |
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<p align="center"> |
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<a href="#Results">Results</a> | |
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<a href="#Updates">Updates</a> | |
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<a href="#Usage">Usage</a> | |
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<a href='#Todo'>Todo</a> | |
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<a href="#Acknowledge">Acknowledge</a> |
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</p> |
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<p align="center"> |
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<a href="https://giphy.com/gifs/UfPQB1qKir7Vqem6sL/fullscreen"><img src="https://media.giphy.com/media/ZewXwZuixYKS2lZmNL/giphy.gif"></a> <a href="https://giphy.com/gifs/DCvf1DrWZgbwPa8bWZ/fullscreen"><img src="https://media.giphy.com/media/2AEeuicbIjwqp2mbug/giphy.gif"></a> |
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</p> |
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<p align="center"> |
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<a href="https://giphy.com/gifs/r3GaZz7H1H6zpuIvPI/fullscreen"><img src="https://media.giphy.com/media/13oe6zo6b2B7CdsOac/giphy.gif"></a> <a href="https://giphy.com/gifs/FjzrGJxsOzZAXaW7Vi/fullscreen"><img src="https://media.giphy.com/media/4JLERHxOEgH0tt5DZO/giphy.gif"></a> |
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</p> |
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This branch contains the pytorch implementation of <a href="https://arxiv.org/abs/2204.12484">ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation</a>. It obtains 81.1 AP on MS COCO Keypoint test-dev set. |
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## Results from this repo on MS COCO val set |
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Using detection results from a detector that obtains 56 mAP on person. |
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| Model | Pretrain | Resolution | AP | AR | config | log | weight | |
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| :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: | |
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| ViT-Base | MAE | 256x192 | 75.8 | 81.1 | config | log | | |
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| ViT-Large | MAE | 256x192 | 78.3 | 83.5 | config | log | | |
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| ViT-Huge | MAE | 256x192 | 79.1 | 84.1 | config | log | | |
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## Updates |
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> [2022-04-27] Our ViTPose with ViTAE-G obtains 81.1 AP on COCO test-dev set! |
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> Applications of ViTAE Transformer include: [image classification](https://github.com/ViTAE-Transformer/ViTAE-Transformer/tree/main/Image-Classification) | [object detection](https://github.com/ViTAE-Transformer/ViTAE-Transformer/tree/main/Object-Detection) | [semantic segmentation](https://github.com/ViTAE-Transformer/ViTAE-Transformer/tree/main/Semantic-Segmentation) | [animal pose segmentation](https://github.com/ViTAE-Transformer/ViTAE-Transformer/tree/main/Animal-Pose-Estimation) | [remote sensing](https://github.com/ViTAE-Transformer/ViTAE-Transformer-Remote-Sensing) | [matting](https://github.com/ViTAE-Transformer/ViTAE-Transformer-Matting) | [VSA](https://github.com/ViTAE-Transformer/ViTAE-VSA) | [ViTDet](https://github.com/ViTAE-Transformer/ViTDet) |
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## Usage |
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We use PyTorch 1.9.0 or NGC docker 21.06, and mmcv 1.3.9 for the experiments. |
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```bash |
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git clone https://github.com/open-mmlab/mmcv.git |
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cd mmcv |
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git checkout v1.3.9 |
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MMCV_WITH_OPS=1 pip install -e . |
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cd .. |
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git clone https://github.com/ViTAE-Transformer/ViTPose.git |
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cd ViTPose |
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pip install -v -e . |
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``` |
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After install the two repos, install timm and einops, i.e., |
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```bash |
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pip install timm==0.4.9 einops |
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``` |
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Download the pretrained models from [MAE](https://github.com/facebookresearch/mae) or [ViTAE](https://github.com/ViTAE-Transformer/ViTAE-Transformer), and then conduct the experiments by |
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```bash |
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# for single machine |
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bash tools/dist_train.sh <Config PATH> <NUM GPUs> --cfg-options model.pretrained=<Pretrained PATH> --seed 0 |
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# for multiple machines |
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python -m torch.distributed.launch --nnodes <Num Machines> --node_rank <Rank of Machine> --nproc_per_node <GPUs Per Machine> --master_addr <Master Addr> --master_port <Master Port> tools/train.py <Config PATH> --cfg-options model.pretrained=<Pretrained PATH> --launcher pytorch --seed 0 |
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``` |
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## Todo |
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This repo current contains modifications including: |
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- [ ] Upload configs and pretrained models |
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- [ ] More models with SOTA results |
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## Acknowledge |
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We acknowledge the excellent implementation from [mmpose](https://github.com/open-mmlab/mmdetection) and [MAE](https://github.com/facebookresearch/mae). |
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## Citing ViTPose |
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``` |
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@misc{xu2022vitpose, |
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title={ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation}, |
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author={Yufei Xu and Jing Zhang and Qiming Zhang and Dacheng Tao}, |
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year={2022}, |
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eprint={2204.12484}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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For ViTAE and ViTAEv2, please refer to: |
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``` |
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@article{xu2021vitae, |
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title={Vitae: Vision transformer advanced by exploring intrinsic inductive bias}, |
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author={Xu, Yufei and Zhang, Qiming and Zhang, Jing and Tao, Dacheng}, |
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journal={Advances in Neural Information Processing Systems}, |
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volume={34}, |
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year={2021} |
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} |
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@article{zhang2022vitaev2, |
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title={ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond}, |
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author={Zhang, Qiming and Xu, Yufei and Zhang, Jing and Tao, Dacheng}, |
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journal={arXiv preprint arXiv:2202.10108}, |
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year={2022} |
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} |
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``` |
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