HRNetPose (High-Resolution Network for Pose Estimation) is a high-performance human pose estimation model introduced in the paper "Deep High-Resolution Representation Learning for Human Pose Estimation". It is designed to address the limitations of traditional methods by maintaining high-resolution feature representations throughout the network, enabling more accurate detection of human keypoints.
| GPU | IXUCA SDK | Release | Branch |
|---|---|---|---|
| MR-V100 | 4.4.0 | 26.03 | release/26.03 |
| MR-V100 | 4.3.0 | 25.12 | release/25.12 |
Note: 请切换到与您的 SDK 版本对应的 Release 分支进行测试。请勿直接在 master 分支上运行测试，因为 master 分支可能包含与您的本地 SDK 版本不兼容的最新更改。
切换分支命令示例：
git checkout release/26.03
Pretrained model: https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth
Dataset:
- https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017labels.zip to download the labels dataset.
- http://images.cocodataset.org/zips/val2017.zip to download the validation dataset.
- http://images.cocodataset.org/zips/train2017.zip to download the train dataset.
- http://images.cocodataset.org/annotations/annotations_trainval2017.zip to download the annotations dataset.
unzip -q -d ./ coco2017labels.zip
unzip -q -d ./coco/images/ train2017.zip
unzip -q -d ./coco/images/ val2017.zip
unzip -q -d ./coco annotations_trainval2017.zip
coco
├── annotations
│ └── instances_train2017.json
│ └── instances_val2017.json
│ └── captions_train2017.json
│ └── captions_val2017.json
│ └── person_keypoints_train2017.json
│ └── person_keypoints_val2017.json
├── images
│ ├── train2017
│ └── val2017
├── labels
│ ├── train2017
│ └── val2017
├── LICENSE
├── README.txt
├── test-dev2017.txt
├── train2017.cache
├── train2017.txt
├── val2017.cache
└── val2017.txtwget https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth# before install mmpose==1.3.1 need to install chchumpy==0.70 which is too older that is not compatible with newer Python versions or pip
# so need to downgrade pip to version 20.2.4
pip install pip==20.2.4
pip install mmpose==1.3.1
pip install --upgrade pip
pip install -r requirements.txt# export onnx model
python3 export.py --weight hrnet_w32_coco_256x192-c78dce93_20200708.pth --cfg td-hm_hrnet-w32_8xb64-210e_coco-256x192.py --input 1,3,256,192 --output hrnetpose.onnx
# use onnxsim optimize onnx model
onnxsim hrnetpose.onnx hrnetpose_opt.onnxexport DATASETS_DIR=/Path/to/coco/# Accuracy
bash scripts/infer_hrnetpose_fp16_accuracy.sh
# Performance
bash scripts/infer_hrnetpose_fp16_performance.sh| Model | BatchSize | Input Shape | Precision | FPS | mAP@0.5(%) |
|---|---|---|---|---|---|
| HRNetPose | 32 | 252x196 | FP16 | 1831.20 | 0.926 |