HigherHRNet-Human-Pose-Estimation
HigherHRNet-Human-Pose-Estimation copied to clipboard
This is an official implementation of our CVPR 2020 paper "HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation" (https://arxiv.org/abs/1908.10357)
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation (CVPR 2020)
News
- [2021/04/12] Welcome to check out our recent work on bottom-up pose estimation (CVPR 2021) HRNet-DEKR!
- [2020/07/05] A very nice blog from Towards Data Science introducing HRNet and HigherHRNet for human pose estimation.
- [2020/03/12] Support train/test on the CrowdPose dataset.
- [2020/02/24] HigherHRNet is accepted to CVPR2020!
- [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 HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation.
Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRNet: a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids. Equipped with multi-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 keypoints more precisely, especially for small person. The feature pyramid in HigherHRNet consists of feature map outputs from HRNet and upsampled higher-resolution outputs 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 new state-of-the-art result on COCO test-dev (70.5% AP) without using refinement or other post-processing techniques, surpassing all existing bottom-up methods. HigherHRNet even surpasses all top-down methods on CrowdPose test (67.6% AP), suggesting its robustness in crowded scene.
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 |
Results on CrowdPose test
Method | AP | Ap .5 | AP .75 | AP (E) | AP (M) | AP (H) |
---|---|---|---|---|---|---|
Mask-RCNN | 57.2 | 83.5 | 60.3 | 69.4 | 57.9 | 45.8 |
AlphaPose | 61.0 | 81.3 | 66.0 | 71.2 | 61.4 | 51.1 |
SPPE | 66.0. | 84.2 | 71.5 | 75.5 | 66.3 | 57.4 |
OpenPose | - | - | - | 62.7 | 48.7 | 32.3 |
HigherHRNet | 65.9 | 86.4 | 70.6 | 73.3 | 66.5 | 57.9 |
HigherHRNet+ | 67.6 | 87.4 | 72.6 | 75.8 | 68.1 | 58.9 |
Note: + indicates using multi-scale test.
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 >= v1.1.0 following official instruction.
- Tested with pytorch v1.4.0
-
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.
-
Install CrowdPoseAPI exactly the same as COCOAPI.
- There is a bug in the CrowdPoseAPI, please reverse https://github.com/Jeff-sjtu/CrowdPose/commit/785e70d269a554b2ba29daf137354103221f479e
-
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
|-- ...
For CrowdPose data, please download from CrowdPose download, Train/Val is needed for CrowdPose keypoints training and validation. Download and extract them under {POSE_ROOT}/data, and make them look like this:
${POSE_ROOT}
|-- data
`-- |-- crowd_pose
`-- |-- json
| |-- crowdpose_train.json
| |-- crowdpose_val.json
| |-- crowdpose_trainval.json (generated by tools/crowdpose_concat_train_val.py)
| `-- crowdpose_test.json
`-- images
|-- 100000.jpg
|-- 100001.jpg
|-- 100002.jpg
|-- 100003.jpg
|-- 100004.jpg
|-- 100005.jpg
|-- ...
After downloading data, run python tools/crowdpose_concat_train_val.py
under ${POSE_ROOT}
to create trainval set.
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
Synchronized BatchNorm training
If you have limited GPU memory, please try to reduce batch size and use SyncBN 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 \
MODEL.SYNC_BN True
Our code for mixed-precision training is borrowed from NVIDIA Apex API.
Training on CrowdPose trainval dataset
python tools/dist_train.py \
--cfg experiments/crowd_pose/higher_hrnet/w32_512_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.
Other implementations
Citation
If you find this work or code is helpful in your research, please cite:
@inproceedings{cheng2020bottom,
title={HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation},
author={Bowen Cheng and Bin Xiao and Jingdong Wang and Honghui Shi and Thomas S. Huang and Lei Zhang},
booktitle={CVPR},
year={2020}
}
@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={TPAMI},
year={2019}
}