a google colab link to test https://colab.research.google.com/drive/1o9RhThxyxHr4P3n6a19UGmc5LTRn5zfp?usp=sharing
follow my csdn: https://blog.csdn.net/qq_35975447/article/details/114940943
python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_2x-dcn.yaml --checkpoint pretrained_models/fast_dcn_res50_256x192.pth --indir examples/demo/ --vis --showbox --save_img --pose_track --sp --vis_fast --detector yolov5
python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_2x-dcn.yaml --checkpoint pretrained_models/fast_dcn_res50_256x192.pth --indir examples/demo/ --vis --showbox --save_img --pose_track --sp --vis_fast --detector yolov4
python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_2x-dcn.yaml --checkpoint pretrained_models/fast_dcn_res50_256x192.pth --indir examples/demo/ --vis --showbox --save_img --pose_track --sp --vis_fast --detector yolov3
or
python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_2x-dcn.yaml --checkpoint pretrained_models/fast_dcn_res50_256x192.pth --indir examples/demo/ --vis --showbox --save_img --pose_track --sp --vis_fast --detector yolo
- Aug 2020: v0.4.0 version of AlphaPose is released! Stronger tracking! Include whole body(face,hand,foot) keypoints!
- 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 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!
COCO 17 keypoints
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 | 73.3 | 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.
Please read trackers/README.md for details.
Please read docs/CrowdPose.md for details.
Please check out docs/INSTALL.md
Please check out docs/MODEL_ZOO.md
- 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:
Demo using FastPose model.
./scripts/inference.sh configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml pretrained_models/fast_res50_256x192.pth ${VIDEO_NAME}
#or
python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --indir examples/demo/Train FastPose on mscoco dataset.
./scripts/train.sh ./configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml exp_fastposeMore detailed inference options and examples, please refer to GETTING_STARTED.md
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!
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.
- 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.
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}
}
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.