https://github.com/hrnet/hrnet-human-pose-estimation
This repo is copied from https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
https://github.com/hrnet/hrnet-human-pose-estimation
Last synced: about 1 year ago
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This repo is copied from https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
- Host: GitHub
- URL: https://github.com/hrnet/hrnet-human-pose-estimation
- Owner: HRNet
- License: mit
- Created: 2019-08-19T02:05:36.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2021-10-12T22:58:49.000Z (over 4 years ago)
- Last Synced: 2025-03-31T12:08:15.301Z (about 1 year ago)
- Language: Cuda
- Homepage: https://github.com/leoxiaobin/deep-high-resolution-net.pytorch
- Size: 1.65 MB
- Stars: 280
- Watchers: 7
- Forks: 74
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep High-Resolution Representation Learning for Human Pose Estimation (accepted to CVPR2019)
## News
- If you are interested in internship or research positions related to computer vision in ByteDance AI Lab, feel free to contact me(leoxiaobin-at-gmail.com).
- Our new work [High-Resolution Representations for Labeling Pixels and Regions](https://arxiv.org/abs/1904.04514) is available at [HRNet](https://github.com/HRNet). Our HRNet has been applied to a wide range of vision tasks, such as [image classification](https://github.com/HRNet/HRNet-Image-Classification), [objection detection](https://github.com/HRNet/HRNet-Object-Detection), [semantic segmentation](https://github.com/HRNet/HRNet-Semantic-Segmentation) and [facial landmark](https://github.com/HRNet/HRNet-Facial-Landmark-Detection).
## Introduction
This is an official pytorch implementation of [*Deep High-Resolution Representation Learning for Human Pose Estimation*](https://arxiv.org/abs/1902.09212).
In this work, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods **recover high-resolution representations from low-resolution representations** produced by a high-to-low resolution network. Instead, our proposed network **maintains high-resolution representations** through the whole process.
We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks **in parallel**. We conduct **repeated multi-scale fusions** such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.
## Main Results
### Results on MPII val
| Arch | Head | Shoulder | Elbow | Wrist | Hip | Knee | Ankle | Mean | Mean@0.1 |
|--------------------|------|----------|-------|-------|------|------|-------|------|----------|
| pose_resnet_50 | 96.4 | 95.3 | 89.0 | 83.2 | 88.4 | 84.0 | 79.6 | 88.5 | 34.0 |
| pose_resnet_101 | 96.9 | 95.9 | 89.5 | 84.4 | 88.4 | 84.5 | 80.7 | 89.1 | 34.0 |
| pose_resnet_152 | 97.0 | 95.9 | 90.0 | 85.0 | 89.2 | 85.3 | 81.3 | 89.6 | 35.0 |
| **pose_hrnet_w32** | 97.1 | 95.9 | 90.3 | 86.4 | 89.1 | 87.1 | 83.3 | 90.3 | 37.7 |
### Note:
- Flip test is used.
- Input size is 256x256
- pose_resnet_[50,101,152] is our previous work of [*Simple Baselines for Human Pose Estimation and Tracking*](http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html)
### Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset
| Arch | Input size | #Params | GFLOPs | AP | Ap .5 | AP .75 | AP (M) | AP (L) | AR | AR .5 | AR .75 | AR (M) | AR (L) |
|--------------------|------------|---------|--------|-------|-------|--------|--------|--------|-------|-------|--------|--------|--------|
| pose_resnet_50 | 256x192 | 34.0M | 8.9 | 0.704 | 0.886 | 0.783 | 0.671 | 0.772 | 0.763 | 0.929 | 0.834 | 0.721 | 0.824 |
| pose_resnet_50 | 384x288 | 34.0M | 20.0 | 0.722 | 0.893 | 0.789 | 0.681 | 0.797 | 0.776 | 0.932 | 0.838 | 0.728 | 0.846 |
| pose_resnet_101 | 256x192 | 53.0M | 12.4 | 0.714 | 0.893 | 0.793 | 0.681 | 0.781 | 0.771 | 0.934 | 0.840 | 0.730 | 0.832 |
| pose_resnet_101 | 384x288 | 53.0M | 27.9 | 0.736 | 0.896 | 0.803 | 0.699 | 0.811 | 0.791 | 0.936 | 0.851 | 0.745 | 0.858 |
| pose_resnet_152 | 256x192 | 68.6M | 15.7 | 0.720 | 0.893 | 0.798 | 0.687 | 0.789 | 0.778 | 0.934 | 0.846 | 0.736 | 0.839 |
| pose_resnet_152 | 384x288 | 68.6M | 35.3 | 0.743 | 0.896 | 0.811 | 0.705 | 0.816 | 0.797 | 0.937 | 0.858 | 0.751 | 0.863 |
| **pose_hrnet_w32** | 256x192 | 28.5M | 7.1 | 0.744 | 0.905 | 0.819 | 0.708 | 0.810 | 0.798 | 0.942 | 0.865 | 0.757 | 0.858 |
| **pose_hrnet_w32** | 384x288 | 28.5M | 16.0 | 0.758 | 0.906 | 0.825 | 0.720 | 0.827 | 0.809 | 0.943 | 0.869 | 0.767 | 0.871 |
| **pose_hrnet_w48** | 256x192 | 63.6M | 14.6 | 0.751 | 0.906 | 0.822 | 0.715 | 0.818 | 0.804 | 0.943 | 0.867 | 0.762 | 0.864 |
| **pose_hrnet_w48** | 384x288 | 63.6M | 32.9 | 0.763 | 0.908 | 0.829 | 0.723 | 0.834 | 0.812 | 0.942 | 0.871 | 0.767 | 0.876 |
### Note:
- Flip test is used.
- Person detector has person AP of 56.4 on COCO val2017 dataset.
- pose_resnet_[50,101,152] is our previous work of [*Simple Baselines for Human Pose Estimation and Tracking*](http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html).
- GFLOPs is for convolution and linear layers only.
### Results on COCO test-dev2017 with detector having human AP of 60.9 on COCO test-dev2017 dataset
| Arch | Input size | #Params | GFLOPs | AP | Ap .5 | AP .75 | AP (M) | AP (L) | AR | AR .5 | AR .75 | AR (M) | AR (L) |
|--------------------|------------|---------|--------|-------|-------|--------|--------|--------|-------|-------|--------|--------|--------|
| pose_resnet_152 | 384x288 | 68.6M | 35.3 | 0.737 | 0.919 | 0.828 | 0.713 | 0.800 | 0.790 | 0.952 | 0.856 | 0.748 | 0.849 |
| **pose_hrnet_w48** | 384x288 | 63.6M | 32.9 | 0.755 | 0.925 | 0.833 | 0.719 | 0.815 | 0.805 | 0.957 | 0.874 | 0.763 | 0.863 |
| **pose_hrnet_w48\*** | 384x288 | 63.6M | 32.9 | 0.770 | 0.927 | 0.845 | 0.734 | 0.831 | 0.820 | 0.960 | 0.886 | 0.778 | 0.877 |
### Note:
- Flip test is used.
- Person detector has person AP of 60.9 on COCO test-dev2017 dataset.
- pose_resnet_152 is our previous work of [*Simple Baselines for Human Pose Estimation and Tracking*](http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html).
- GFLOPs is for convolution and linear layers only.
- pose_hrnet_w48\* means using additional data from [AI challenger](https://challenger.ai/dataset/keypoint) for training.
## Environment
The code is developed using python 3.6 on Ubuntu 16.04. NVIDIA GPUs are needed. The code is developed and tested using 4 NVIDIA P100 GPU cards. Other platforms or GPU cards are not fully tested.
## Quick start
### Installation
1. Install pytorch >= v1.0.0 following [official instruction](https://pytorch.org/).
**Note that if you use pytorch's version < v1.0.0, you should following the instruction at to disable cudnn's implementations of BatchNorm layer. We encourage you to use higher pytorch's version(>=v1.0.0)**
2. Clone this repo, and we'll call the directory that you cloned as ${POSE_ROOT}.
3. Install dependencies:
```
pip install -r requirements.txt
```
4. Make libs:
```
cd ${POSE_ROOT}/lib
make
```
5. Install [COCOAPI](https://github.com/cocodataset/cocoapi):
```
# COCOAPI=/path/to/clone/cocoapi
git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
cd $COCOAPI/PythonAPI
# Install into global site-packages
make install
# Alternatively, if you do not have permissions or prefer
# not to install the COCO API into global site-packages
python3 setup.py install --user
```
Note that instructions like # COCOAPI=/path/to/install/cocoapi indicate that you should pick a path where you'd like to have the software cloned and then set an environment variable (COCOAPI in this case) accordingly.
4. Init output(training model output directory) and log(tensorboard log directory) directory:
```
mkdir output
mkdir log
```
Your directory tree should look like this:
```
${POSE_ROOT}
├── data
├── experiments
├── lib
├── log
├── models
├── output
├── tools
├── README.md
└── requirements.txt
```
6. Download pretrained models from our model zoo([GoogleDrive](https://drive.google.com/drive/folders/1hOTihvbyIxsm5ygDpbUuJ7O_tzv4oXjC?usp=sharing) or [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blW231MH2krnmLq5kkQ))
```
${POSE_ROOT}
`-- models
`-- pytorch
|-- imagenet
| |-- hrnet_w32-36af842e.pth
| |-- hrnet_w48-8ef0771d.pth
| |-- resnet50-19c8e357.pth
| |-- resnet101-5d3b4d8f.pth
| `-- resnet152-b121ed2d.pth
|-- pose_coco
| |-- pose_hrnet_w32_256x192.pth
| |-- pose_hrnet_w32_384x288.pth
| |-- pose_hrnet_w48_256x192.pth
| |-- pose_hrnet_w48_384x288.pth
| |-- pose_resnet_101_256x192.pth
| |-- pose_resnet_101_384x288.pth
| |-- pose_resnet_152_256x192.pth
| |-- pose_resnet_152_384x288.pth
| |-- pose_resnet_50_256x192.pth
| `-- pose_resnet_50_384x288.pth
`-- pose_mpii
|-- pose_hrnet_w32_256x256.pth
|-- pose_hrnet_w48_256x256.pth
|-- pose_resnet_101_256x256.pth
|-- pose_resnet_152_256x256.pth
`-- pose_resnet_50_256x256.pth
```
### Data preparation
**For MPII data**, please download from [MPII Human Pose Dataset](http://human-pose.mpi-inf.mpg.de/). The original annotation files are in matlab format. We have converted them into json format, you also need to download them from [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blW00SqrairNetmeVu4) or [GoogleDrive](https://drive.google.com/drive/folders/1En_VqmStnsXMdldXA6qpqEyDQulnmS3a?usp=sharing).
Extract them under {POSE_ROOT}/data, and make them look like this:
```
${POSE_ROOT}
|-- data
`-- |-- mpii
`-- |-- annot
| |-- gt_valid.mat
| |-- test.json
| |-- train.json
| |-- trainval.json
| `-- valid.json
`-- images
|-- 000001163.jpg
|-- 000003072.jpg
```
**For COCO data**, please download from [COCO download](http://cocodataset.org/#download), 2017 Train/Val is needed for COCO keypoints training and validation. We also provide person detection result of COCO val2017 and test-dev2017 to reproduce our multi-person pose estimation results. Please download from [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blWzzDXoz5BeFl8sWM-) or [GoogleDrive](https://drive.google.com/drive/folders/1fRUDNUDxe9fjqcRZ2bnF_TKMlO0nB_dk?usp=sharing).
Download and extract them under {POSE_ROOT}/data, and make them look like this:
```
${POSE_ROOT}
|-- data
`-- |-- coco
`-- |-- annotations
| |-- person_keypoints_train2017.json
| `-- person_keypoints_val2017.json
|-- person_detection_results
| |-- COCO_val2017_detections_AP_H_56_person.json
| |-- COCO_test-dev2017_detections_AP_H_609_person.json
`-- images
|-- train2017
| |-- 000000000009.jpg
| |-- 000000000025.jpg
| |-- 000000000030.jpg
| |-- ...
`-- val2017
|-- 000000000139.jpg
|-- 000000000285.jpg
|-- 000000000632.jpg
|-- ...
```
### Training and Testing
#### Testing on MPII dataset using model zoo's models([GoogleDrive](https://drive.google.com/drive/folders/1hOTihvbyIxsm5ygDpbUuJ7O_tzv4oXjC?usp=sharing) or [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blW231MH2krnmLq5kkQ))
```
python tools/test.py \
--cfg experiments/mpii/hrnet/w32_256x256_adam_lr1e-3.yaml \
TEST.MODEL_FILE models/pytorch/pose_mpii/pose_hrnet_w32_256x256.pth
```
#### Training on MPII dataset
```
python tools/train.py \
--cfg experiments/mpii/hrnet/w32_256x256_adam_lr1e-3.yaml
```
#### Testing on COCO val2017 dataset using model zoo's models([GoogleDrive](https://drive.google.com/drive/folders/1hOTihvbyIxsm5ygDpbUuJ7O_tzv4oXjC?usp=sharing) or [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blW231MH2krnmLq5kkQ))
```
python tools/test.py \
--cfg experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml \
TEST.MODEL_FILE models/pytorch/pose_coco/pose_hrnet_w32_256x192.pth \
TEST.USE_GT_BBOX False
```
#### Training on COCO train2017 dataset
```
python tools/train.py \
--cfg experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml \
```
### Other applications
Many other dense prediction tasks, such as segmentation, face alignment and object detection, etc. have been benefited by HRNet. More information can be found at [Deep High-Resolution Representation Learning](https://jingdongwang2017.github.io/Projects/HRNet/).
### Citation
If you use our code or models in your research, please cite with:
```
@inproceedings{sun2019deep,
title={Deep High-Resolution Representation Learning for Human Pose Estimation},
author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
booktitle={CVPR},
year={2019}
}
@inproceedings{xiao2018simple,
author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
title={Simple Baselines for Human Pose Estimation and Tracking},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2018}
}
```