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README
Apache-2.0

News!

  • Dec 2019: v0.3.0 version of AlphaPose is released! Smaller model, higher accuracy!
  • Apr 2019: MXNet version of AlphaPose is released! It runs at 23 fps on COCO validation set.
  • Feb 2019: CrowdPose is integrated into AlphaPose Now!
  • Dec 2018: General version of PoseFlow is released! 3X Faster and support pose tracking results visualization!
  • Sep 2018: v0.2.0 version of AlphaPose is released! It runs at 20 fps on COCO validation set (4.6 people per image on average) and achieves 71 mAP!

AlphaPose

AlphaPose is an accurate multi-person pose estimator, which is the first open-source system that achieves 70+ mAP (75 mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset. To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. It is the first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset.

AlphaPose supports both Linux and Windows!

Results

Pose Estimation

Results on COCO test-dev 2015:

Method AP @0.5:0.95 AP @0.5 AP @0.75 AP medium AP large
OpenPose (CMU-Pose) 61.8 84.9 67.5 57.1 68.2
Detectron (Mask R-CNN) 67.0 88.0 73.1 62.2 75.6
AlphaPose 72.3 89.2 79.1 69.0 78.6

Results on MPII full test set:

Method Head Shoulder Elbow Wrist Hip Knee Ankle Ave
OpenPose (CMU-Pose) 91.2 87.6 77.7 66.8 75.4 68.9 61.7 75.6
Newell & Deng 92.1 89.3 78.9 69.8 76.2 71.6 64.7 77.5
AlphaPose 91.3 90.5 84.0 76.4 80.3 79.9 72.4 82.1

More results and models are available in the docs/MODEL_ZOO.md.

Pose Tracking

Please read PoseFlow/README.md for details.

CrowdPose

Please read docs/CrowdPose.md for details.

Installation

Please check out docs/INSTALL.md

Model Zoo

Please check out docs/MODEL_ZOO.md

Quick Start

  • Inference: Inference demo
./scripts/inference.sh ${CONFIG} ${CHECKPOINT} ${VIDEO_NAME} # ${OUTPUT_DIR}, optional
  • Training: Train from scratch
./scripts/train.sh ${CONFIG} ${EXP_ID}
  • Validation: Validate your model on MSCOCO val2017
./scripts/validate.sh ${CONFIG} ${CHECKPOINT}

Examples:

Train FastPose on mscoco dataset.

./scripts/train.sh ./configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml exp_fastpose

More detailed inference options, please refer to GETTING_STARTED.md

Common issue & FAQ

Check out faq.md for faq. If it can not solve your problems or if you find any bugs, don't hesitate to comment on GitHub or make a pull request!

Contributors

AlphaPose is based on RMPE(ICCV'17), authored by Hao-Shu Fang, Shuqin Xie, Yu-Wing Tai and Cewu Lu, Cewu Lu is the corresponding author. Currently, it is maintained by Jiefeng Li*, Hao-shu Fang*, Yuliang Xiu and Chao Xu.

The main contributors are listed in doc/contributors.md.

TODO

  • Multi-GPU/CPU inference
  • 3D pose
  • add tracking flag
  • PyTorch C++ version
  • Add MPII and AIC data
  • dense support
  • small box easy filter
  • Crowdpose support
  • Speed up PoseFlow
  • Add stronger/light detectors and the mobile pose
  • High level API

We would really appreciate if you can offer any help and be the contributor of AlphaPose.

Citation

Please cite these papers in your publications if it helps your research:

@inproceedings{fang2017rmpe,
  title={{RMPE}: Regional Multi-person Pose Estimation},
  author={Fang, Hao-Shu and Xie, Shuqin and Tai, Yu-Wing and Lu, Cewu},
  booktitle={ICCV},
  year={2017}
}

@article{li2018crowdpose,
  title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
  author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
  journal={arXiv preprint arXiv:1812.00324},
  year={2018}
}

@inproceedings{xiu2018poseflow,
  author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu},
  title = {{Pose Flow}: Efficient Online Pose Tracking},
  booktitle={BMVC},
  year = {2018}
}

License

AlphaPose is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, please drop an e-mail at mvig.alphapose[at]gmail[dot]com and cc lucewu[[at]sjtu[dot]edu[dot]cn. We will send the detail agreement to you.

ALPHAPOSE: MULTIPERSON KEYPOINT DETECTION SOFTWARE LICENSE AGREEMENT ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT. IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE. This is a license agreement ("Agreement") between your academic institution or non-profit organization or self (called "Licensee" or "You" in this Agreement) and Shanghai Jiao Tong University (called "Licensor" in this Agreement). All rights not specifically granted to you in this Agreement are reserved for Licensor. 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CONFIDENTIALITY: Licensee acknowledges that the Software is proprietary to Licensor, and as such, Licensee agrees to receive all such materials in confidence and use the Software only in accordance with the terms of this Agreement. Licensee agrees to use reasonable effort to protect the Software from unauthorized use, reproduction, distribution, or publication. PERMITTED USES: The Software may be used for your own noncommercial internal research purposes. You understand and agree that Licensor is not obligated to implement any suggestions and/or feedback you might provide regarding the Software, but to the extent Licensor does so, you are not entitled to any compensation related thereto. DERIVATIVES: You may create derivatives of or make modifications to the Software, however, You agree that all and any such derivatives and modifications will be owned by Licensor and become a part of the Software licensed to You under this Agreement. You may only use such derivatives and modifications for your own noncommercial internal research purposes, and you may not otherwise use, distribute or copy such derivatives and modifications in violation of this Agreement. BACKUPS: If Licensee is an organization, it may make that number of copies of the Software necessary for internal noncommercial use at a single site within its organization provided that all information appearing in or on the original labels, including the copyright and trademark notices are copied onto the labels of the copies. USES NOT PERMITTED: You may not distribute, copy or use the Software except as explicitly permitted herein. Licensee has not been granted any trademark license as part of this Agreement and may not use the name or mark “AlphaPose", "Shanghai Jiao Tong" or any renditions thereof without the prior written permission of Licensor. 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https://gitee.com/seventhblue/AlphaPose.git
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