Higher-Resolution Networks (HigherHRNet) for Human Pose Estimation

News

[2019/11/23] Code and models for HigherHRNet are now released!
[2019/08/27] HigherHRNet is now on ArXiv. We will also release code and models, stay tuned!

Introduction

This is the official code of Bottom-up Higher-Resolution Networks for Multi-Person Pose Estimation.
Bottom-up multi-person pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present a Higher-Resolution Network (HigherHRNet) to learn high-resolution feature pyramids. Equipped with mutli-resolution supervision for training and multi-resolution aggregation for inference, the proposed approach is able to solve the scale variation challenge in bottom-up multi-person pose estimation and localize the keypoints, especially for small person, more precisely. The feature pyramid in HigherHRNet consists of the feature map output from HRNet and the upsampled higher-resolution one through a transposed convolution. HigherHRNet outperforms the previous best bottom-up method by 2.5% AP for medium person on COCO test-dev, showing its effectiveness in handling scale variation. Furthermore, HigherHRNet achieves a state-of-the-art result of 70.5% AP on COCO test-dev without using refinement or other post-processing techniques, surpassing all existing bottom-up methods.

Main Results

Results on COCO val2017 without multi-scale test

Method	Backbone	Input size	#Params	GFLOPs	AP	Ap .5	AP .75	AP (M)	AP (L)
HigherHRNet	HRNet-w32	512	28.6M	47.9	67.1	86.2	73.0	61.5	76.1
HigherHRNet	HRNet-w32	640	28.6M	74.8	68.5	87.1	74.7	64.3	75.3
HigherHRNet	HRNet-w48	640	63.8M	154.3	69.9	87.2	76.1	65.4	76.4

Results on COCO val2017 with multi-scale test

Method	Backbone	Input size	#Params	GFLOPs	AP	Ap .5	AP .75	AP (M)	AP (L)
HigherHRNet	HRNet-w32	512	28.6M	47.9	69.9	87.1	76.0	65.3	77.0
HigherHRNet	HRNet-w32	640	28.6M	74.8	70.6	88.1	76.9	66.6	76.5
HigherHRNet	HRNet-w48	640	63.8M	154.3	72.1	88.4	78.2	67.8	78.3

Results on COCO test-dev2017 without multi-scale test

Method	Backbone	Input size	#Params	GFLOPs	AP	Ap .5	AP .75	AP (M)	AP (L)
OpenPose*	-	-	-	-	61.8	84.9	67.5	57.1	68.2
Hourglass	Hourglass	512	277.8M	206.9	56.6	81.8	61.8	49.8	67.0
PersonLab	ResNet-152	1401	68.7M	405.5	66.5	88.0	72.6	62.4	72.3
PifPaf	-	-	-	-	66.7	-	-	62.4	72.9
Bottom-up HRNet	HRNet-w32	512	28.5M	38.9	64.1	86.3	70.4	57.4	73.9
HigherHRNet	HRNet-w32	512	28.6M	47.9	66.4	87.5	72.8	61.2	74.2
HigherHRNet	HRNet-w48	640	63.8M	154.3	68.4	88.2	75.1	64.4	74.2

Results on COCO test-dev2017 with multi-scale test

Method	Backbone	Input size	#Params	GFLOPs	AP	Ap .5	AP .75	AP (M)	AP (L)
Hourglass	Hourglass	512	277.8M	206.9	63.0	85.7	68.9	58.0	70.4
Hourglass*	Hourglass	512	277.8M	206.9	65.5	86.8	72.3	60.6	72.6
PersonLab	ResNet-152	1401	68.7M	405.5	68.7	89.0	75.4	64.1	75.5
HigherHRNet	HRNet-w48	640	63.8M	154.3	70.5	89.3	77.2	66.6	75.8

Note: * indicates using refinement.

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

Install pytorch >= v1.0.0 following official instruction. Note that if you use pytorch's version < v1.0.0, you should following the instruction at https://github.com/Microsoft/human-pose-estimation.pytorch to disable cudnn's implementations of BatchNorm layer. We encourage you to use higher pytorch's version(>=v1.0.0)
Clone this repo, and we'll call the directory that you cloned as ${POSE_ROOT}.
Install dependencies:
```
pip install -r requirements.txt
```

Install 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.

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

Download pretrained models from our model zoo(GoogleDrive or OneDrive)

${POSE_ROOT}
 `-- models
     `-- pytorch
         |-- imagenet
         |   `-- hrnet_w32-36af842e.pth
         `-- pose_coco
             `-- pose_higher_hrnet_w32_512.pth

Data preparation

For COCO data, please download from COCO download, 2017 Train/Val is needed for COCO keypoints training and validation. 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
        `-- images
            |-- train2017
            |   |-- 000000000009.jpg
            |   |-- 000000000025.jpg
            |   |-- 000000000030.jpg
            |   |-- ... 
            `-- val2017
                |-- 000000000139.jpg
                |-- 000000000285.jpg
                |-- 000000000632.jpg
                |-- ...

Training and Testing

Testing on COCO val2017 dataset using model zoo's models (GoogleDrive)

For single-scale testing:

python tools/valid.py \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml \
    TEST.MODEL_FILE models/pytorch/pose_coco/pose_higher_hrnet_w32_512.pth

By default, we use horizontal flip. To test without flip:

python tools/valid.py \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml \
    TEST.MODEL_FILE models/pytorch/pose_coco/pose_higher_hrnet_w32_512.pth \
    TEST.FLIP_TEST False

Multi-scale testing is also supported, although we do not report results in our paper:

python tools/valid.py \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml \
    TEST.MODEL_FILE models/pytorch/pose_coco/pose_higher_hrnet_w32_512.pth \
    TEST.SCALE_FACTOR '[0.5, 1.0, 2.0]'

Training on COCO train2017 dataset

python tools/dist_train.py \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml

By default, it will use all available GPUs on the machine for training. To specify GPUs, use

CUDA_VISIBLE_DEVICES=0,1 python tools/dist_train.py \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml

Mixed-precision training

Due to large input size for bottom-up methods, we use mixed-precision training to train our Higher-HRNet by using the following command:

python tools/dist_train.py \
    --cfg experiments/coco/higher_hrnet/w32_512_adam_lr1e-3.yaml \
    FP16.ENABLED True FP16.DYNAMIC_LOSS_SCALE True

Our code for mixed-precision training is borrowed from NVIDIA Apex API.

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.

Citation

If you find this work or code is helpful in your research, please cite:

@article{cheng2019bottom,
  title={Bottom-up Higher-Resolution Networks for Multi-Person Pose Estimation},
  author={Bowen Cheng and Bin Xiao and Jingdong Wang and Honghui Shi and Thomas S. Huang and Lei Zhang},
  journal   = {CoRR},
  volume    = {abs/1908.10357},
  year={2019}
}

@inproceedings{SunXLW19,
  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
  booktitle={CVPR},
  year={2019}
}

@article{wang2019deep,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Wang, Jingdong and Sun, Ke and Cheng, Tianheng and Jiang, Borui and Deng, Chaorui and Zhao, Yang and Liu, Dong and Mu, Yadong and Tan, Mingkui and Wang, Xinggang and Liu, Wenyu and Xiao, Bin},
  journal={arXiv preprint arXiv:1908.07919},
  year={2019}
}

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