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 opencv#44 for more information.
Results of accuracy evaluation with tools/eval.
| Models | Easy AP | Medium AP | Hard AP |
|---|---|---|---|
| YuNet | 0.8871 | 0.8710 | 0.7681 |
| YuNet quant | 0.8838 | 0.8683 | 0.7676 |
*: 'quant' stands for '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 --helpInstall 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.