HigherHRNet

This implements training of HigherHRNet on the COCO dataset, mainly modified from GitHub - HRNet/HigherHRNet-Human-Pose-Estimation

1. Install package dependencies. Make sure the python environment >=3.7

   pip install -r requirements.txt
   

2. 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.

3. Download pretrained models from the releases of HigherHRNet-Human-Pose-Estimation to the specified directory

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

Data Preparation

Please download or link COCO to ${POSE_ROOT}/data/coco/, 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
	|   |-- ... 
	`-- val2017
		|-- 000000000139.jpg
		|-- ... 

Training

To train a model, run main.py with the desired model architecture and the path to the ImageNet dataset:

# training 1p accuracy
bash ./test/train_full_1p.sh --data_path=real_data_path

# training 1p performance
bash ./test/train_performance_1p.sh --data_path=real_data_path

# training 8p accuracy
bash ./test/train_full_8p.sh --data_path=real_data_path

# training 8p performance
bash ./test/train_performance_8p.sh --data_path=real_data_path

#test 8p accuracy
bash test/train_eval_8p.sh --data_path=real_data_path --pth_path=real_pre_train_model_path

HigherHRNet training result

名称	精度	性能
NPU-8p	66.9	2.2s/step
GPU-8p	67.1	1.2s/step
NPU-1p		1.1s/step
GPU-1p		0.7s/step

Statement

For details about the public address of the code in this repository, you can get from the file public_address_statement.md