face_detection_yunet

YuNet

YuNet is a light-weight, fast and accurate face detection model, which achieves 0.834(AP_easy), 0.824(AP_medium), 0.708(AP_hard) on the WIDER Face validation set.

Notes:

• Model source: here.
• This model can detect faces of pixels between around 10x10 to 300x300 due to the training scheme.
• For details on training this model, please visit https://github.com/ShiqiYu/libfacedetection.train.
• face_detection_yunet_2026may.onnx is the default model with dynamic input shape (symbolic height and width dims), compatible with OpenCV 5.x ONNX Runtime engine (OPENCV_FORCE_DNN_ENGINE=4). It is re-exported from face_detection_yunet_2023mar.onnx with static H/W dims replaced by symbolic dims, allowing inference at any resolution without resizing.
• face_detection_yunet_2023mar.onnx has fixed input shape. OpenCV 4.x DNN infers on the exact shape of input image, but the ONNX Runtime engine in OpenCV 5.x requires dynamic dims for variable input sizes. See #44 for more information.
• face_detection_yunet_2023mar_int8bq.onnx represents the block-quantized version in int8 precision and is generated using block_quantize.py with block_size=64.
• Paper source: Yunet: A tiny millisecond-level face detector.

Results of accuracy evaluation with tools/eval.

Models	Easy AP	Medium AP	Hard AP
YuNet	0.8844	0.8656	0.7503
YuNet block	0.8845	0.8652	0.7504
YuNet quant	0.8810	0.8629	0.7503

*: 'quant' stands for 'quantized'. **: 'block' stands for 'blockwise quantized'.

Demo

Python

Run the following command to try the demo:

# detect on camera input
python demo.py
# detect on an image
python demo.py --input /path/to/image -v

# get help regarding various parameters
python demo.py --help

C++

Install latest OpenCV and CMake >= 3.24.0 to get started with:

# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build

# detect on camera input
./build/demo
# detect on an image
./build/demo -i=/path/to/image -v
# get help messages
./build/demo -h

Example outputs

License

All files in this directory are licensed under MIT License.

Reference

• https://github.com/ShiqiYu/libfacedetection
• https://github.com/ShiqiYu/libfacedetection.train

Citation

If you use YuNet in your work, please use the following BibTeX entries:

@article{wu2023yunet,
  title={Yunet: A tiny millisecond-level face detector},
  author={Wu, Wei and Peng, Hanyang and Yu, Shiqi},
  journal={Machine Intelligence Research},
  volume={20},
  number={5},
  pages={656--665},
  year={2023},
  publisher={Springer}
}