A tensorflow2 implementation of HRNet for human pose estimation.
- Python == 3.7
- TensorFlow == 2.1.0
- numpy == 1.17.0
- opencv-python == 4.1.0
- Download the COCO2017 dataset.
- Unzip the train2017.zip, annotations_trainval2017.zip and place them in the 'dataset' folder, make sure the directory is like this :
|——dataset
|——COCO
|——2017
|——annotations
|——train2017
- Open the file "./configuration/base_config.py", change the parameters according to your needs.
- Run "write_coco_to_txt.py" to generate coco annotation files.
- Run "train.py" to train on coco2017 dataset.
- Prepare the test pictures and make sure that the TEST_PICTURES_DIRS in "./configuration/base_config.py" is correct.
- Run "test.py" to test on your test pictures.
@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}
}
@article{WangSCJDZLMTWLX19,
title={Deep High-Resolution Representation Learning for Visual Recognition},
author={Jingdong Wang and Ke Sun and Tianheng Cheng and
Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and
Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
journal = {CoRR},
volume = {abs/1908.07919},
year={2019}
}