pytorch_Realtime_Multi-Person_Pose_Estimation

This is a pytorch version of Realtime_Multi-Person_Pose_Estimation, origin code is here https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation

Introduction

Code repo for reproducing 2017 CVPR Oral paper using pytorch.

Results

Contents

1. Testing
2. Training

Require

1. Pytorch
2. Caffe is required if you want convert caffe model to a pytorch model.
3. pip install pycocotools
4. pip install tensorboardX
5. pip install torch-encoding

Demo

• Download converted pytorch model.
• cd network/caffe_to_pytorch; python convert.py to convert a trained caffe model to pytorch model. The converted model have relative error less than 1e-6, and will be located in ./network/weight after convert.
• python demo/picture_demo.py to run the picture demo.
• python demo/web_demo.py to run the web demo.

Evalute

• python evaluate/evaluation.py

Training

• cd training; bash getData.sh to obtain the COCO images in dataset/COCO/images/, keypoints annotations in dataset/COCO/annotations/
• Download the mask of the unlabeled person at Dropbox
• Download the official training format at Dropbox
• python train.py --batch_size 100 --logdir {where to store tensorboardX logs}

Related repository

• CVPR'16, Convolutional Pose Machines.
• CVPR'17, Realtime Multi-Person Pose Estimation.

Network Architecture

• testing architecture

• training architecture

Citation

Please cite the paper in your publications if it helps your research:

@InProceedings{cao2017realtime,
  title = {Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
  author = {Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017}
  }