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.
• This ONNX model has fixed input shape, but OpenCV DNN infers on the exact shape of input image. 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.

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

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

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

All files in this directory are licensed under MIT License.

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

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}
}