https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch
News!
This pytorch version of AlphaPose runs at 20 fps on COCO validation set (4.6 people per image on average) and achieves 71 AP!
AlphaPose
Alpha Pose is an accurate multi-person pose estimator, which is the first open-source system that achieves 70+ mAP (72.3 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!
Installation
Windows Version please check out doc/win_install.md
- Get the code.
git clone -b pytorch https://github.com/MVIG-SJTU/AlphaPose.git
- Install pytorch 0.4.0
chmod +x install.sh
./install.sh
- Download the models manually: duc_se.pth (2018/08/30) (Google Drive | Baidu pan), yolov3.weights(Google Drive | Baidu pan). Place them into
./models/sppeand./models/yolorespectively.
Quick Start
- Input dir: Run AlphaPose for all images in a folder with:
python3 demo.py --indir ${img_directory} --outdir examples/res
- Video: Run AlphaPose for a video and save the rendered video with:
python3 video_demo.py --video ${path to video} --outdir examples/res --save_video
- Webcam: Run AlphaPose using webcam and visualize the results with:
python3 webcam_demo.py --webcam 0 --outdir examples/res --vis
- Input list: Run AlphaPose for images in a list and save the rendered images with:
python3 demo.py --list examples/list-coco-demo.txt --indir ${img_directory} --outdir examples/res --save_img
- Note: If you meet OOM(out of memory) problem, decreasing the pose estimation batch until the program can run on your computer:
python3 demo.py --indir ${img_directory} --outdir examples/res --posebatch 30
- Speeding up: Checkout the speed_up.md for more details.
- For more: Checkout the run.md for more options
FAQ
Check out faq.md for faq.
Contributors
Pytorch version of AlphaPose is developed and maintained by Jiefeng Li, Hao-Shu Fang and Cewu Lu.
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{2018arXiv180200977X,
author = {Xiu, Yuliang and Li, Jiefeng and Wang, Haoyu and Fang, Yinghong and Lu, Cewu},
title = {{Pose Flow}: Efficient Online Pose Tracking},
journal = {ArXiv e-prints},
eprint = {1802.00977},
year = {2018}
}
License
AlphaPose is freely available for free non-commercial use, and may be redistributed under these conditions. For commercial queries, contact Cewu Lu