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Showing content from https://github.com/ViTAE-Transformer/ViTPose below:

ViTAE-Transformer/ViTPose: The official repo for [NeurIPS'22] "ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation" and [TPAMI'23] "ViTPose++: Vision Transformer for Generic Body Pose Estimation"

Results | Updates | Usage | Todo | Acknowledge

This branch contains the pytorch implementation of ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation and ViTPose+: Vision Transformer Foundation Model for Generic Body Pose Estimation. It obtains 81.1 AP on MS COCO Keypoint test-dev set.

• The small size MAE pre-trained model can be found in Onedrive.
• The base, large, and huge pre-trained models using MAE can be found in the MAE official repo.

Using detection results from a detector that obtains 56 mAP on person. The configs here are for both training and test.

With classic decoder

With simple decoder

Note * There may exist duplicate images in the crowdpose training set and the validation images in other datasets, as discussed in issue #24. Please be careful when using these models for evaluation. We provide the results without the crowpose dataset for reference.

Results on MS COCO val set

Using detection results from a detector that obtains 56 mAP on person. Note the configs here are only for evaluation.

Results on OCHuman test set

Using groundtruth bounding boxes. Note the configs here are only for evaluation.

Results on MPII val set

Using groundtruth bounding boxes. Note the configs here are only for evaluation. The metric is PCKh.

Results on AI Challenger test set

Using groundtruth bounding boxes. Note the configs here are only for evaluation.

Results on CrowdPose test set

Using YOLOv3 human detector. Note the configs here are only for evaluation.

Results on AP-10K test set

Results on APT-36K val set

[2023-01-10] Update ViTPose+! It uses MoE strategies to jointly deal with human, animal, and wholebody pose estimation tasks.

[2022-05-24] Upload the single-task training code, single-task pre-trained models, and multi-task pretrained models.

[2022-05-06] Upload the logs for the base, large, and huge models!

[2022-04-27] Our ViTPose with ViTAE-G obtains 81.1 AP on COCO test-dev set!

Applications of ViTAE Transformer include: image classification | object detection | semantic segmentation | animal pose segmentation | remote sensing | matting | VSA | ViTDet

We use PyTorch 1.9.0 or NGC docker 21.06, and mmcv 1.3.9 for the experiments.

git clone https://github.com/open-mmlab/mmcv.git
cd mmcv
git checkout v1.3.9
MMCV_WITH_OPS=1 pip install -e .
cd ..
git clone https://github.com/ViTAE-Transformer/ViTPose.git
cd ViTPose
pip install -v -e .

After install the two repos, install timm and einops, i.e.,

pip install timm==0.4.9 einops

After downloading the pretrained models, please conduct the experiments by running

# for single machine
bash tools/dist_train.sh <Config PATH> <NUM GPUs> --cfg-options model.pretrained=<Pretrained PATH> --seed 0

# for multiple machines
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

To test the pretrained models performance, please run

bash tools/dist_test.sh <Config PATH> <Checkpoint PATH> <NUM GPUs>

For ViTPose+ pre-trained models, please first re-organize the pre-trained weights using

python tools/model_split.py --source <Pretrained PATH>

This repo current contains modifications including:

• Upload configs and pretrained models

• More models with SOTA results

• Upload multi-task training config

We acknowledge the excellent implementation from mmpose and MAE.

For ViTPose

@inproceedings{
  xu2022vitpose,
  title={Vi{TP}ose: Simple Vision Transformer Baselines for Human Pose Estimation},
  author={Yufei Xu and Jing Zhang and Qiming Zhang and Dacheng Tao},
  booktitle={Advances in Neural Information Processing Systems},
  year={2022},
}

For ViTPose+

@article{xu2022vitpose+,
  title={ViTPose+: Vision Transformer Foundation Model for Generic Body Pose Estimation},
  author={Xu, Yufei and Zhang, Jing and Zhang, Qiming and Tao, Dacheng},
  journal={arXiv preprint arXiv:2212.04246},
  year={2022}
}

For ViTAE and ViTAEv2, please refer to:

@article{xu2021vitae,
  title={Vitae: Vision transformer advanced by exploring intrinsic inductive bias},
  author={Xu, Yufei and Zhang, Qiming and Zhang, Jing and Tao, Dacheng},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

@article{zhang2022vitaev2,
  title={ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond},
  author={Zhang, Qiming and Xu, Yufei and Zhang, Jing and Tao, Dacheng},
  journal={arXiv preprint arXiv:2202.10108},
  year={2022}
}

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