https://github.com/tensorboy/pytorch_realtime_multi-person_pose_estimation
https://github.com/tensorboy/pytorch_realtime_multi-person_pose_estimation
Last synced: 11 months ago
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- Host: GitHub
- URL: https://github.com/tensorboy/pytorch_realtime_multi-person_pose_estimation
- Owner: tensorboy
- Created: 2017-03-28T07:53:58.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2023-02-07T01:46:17.000Z (about 3 years ago)
- Last Synced: 2025-04-01T09:25:50.571Z (11 months ago)
- Language: Python
- Homepage:
- Size: 66.4 MB
- Stars: 1,368
- Watchers: 36
- Forks: 409
- Open Issues: 61
-
Metadata Files:
- Readme: README.md
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README
## Introduction
Multi Person PoseEstimation By PyTorch
## Results
[](https://opensource.org/licenses/MIT)
## Require
1. [Pytorch](http://pytorch.org/)
## Installation
1. git submodule init && git submodule update
## Demo
- Download [converted pytorch model](https://www.dropbox.com/s/ae071mfm2qoyc8v/pose_model.pth?dl=0).
- Compile the C++ postprocessing: `cd lib/pafprocess; sh make.sh`
- `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` to evaluate the model on coco val2017 dataset.
- It should have `mAP 0.653` for the rtpose, previous rtpose have `mAP 0.577` because we do left and right flip for heatmap and PAF for the evaluation.
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### Main Results
| model name| mAP | Inference Time |
| :---------: | :---------: |:---------: |
|[original rtpose] | 0.653 |-|
Download link:
[rtpose](https://www.dropbox.com/s/ae071mfm2qoyc8v/pose_model.pth?dl=0)
## Development environment
The code is developed using python 3.6 on Ubuntu 18.04. NVIDIA GPUs are needed. The code is developed and tested using 4 1080ti GPU cards. Other platforms or GPU cards are not fully tested.
## Quick start
### 1. Preparation
#### 1.1 Prepare the dataset
- `cd training; bash getData.sh` to obtain the COCO 2017 images in `/data/root/coco/images/`, keypoints annotations in `/data/root/coco/annotations/`,
make them look like this:
```
${DATA_ROOT}
|-- coco
|-- annotations
|-- person_keypoints_train2017.json
|-- person_keypoints_val2017.json
|-- images
|-- train2017
|-- 000000000009.jpg
|-- 000000000025.jpg
|-- 000000000030.jpg
|-- ...
|-- val2017
|-- 000000000139.jpg
|-- 000000000285.jpg
|-- 000000000632.jpg
|-- ...
```
### 2. How to train the model
- Modify the data directory in `train/train_VGG19.py` and `python train/train_VGG19.py`
## Related repository
- CVPR'17, [Realtime Multi-Person Pose Estimation](https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation).
### Network Architecture
- testing architecture
- training architecture
## Contributions
All contributions are welcomed. If you encounter any issue (including examples of images where it fails) feel free to open an issue.
## 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}
}