File size: 2,745 Bytes
e293846 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | # 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](https://github.com/ShiqiYu/libfacedetection.train/blob/a61a428929148171b488f024b5d6774f93cdbc13/tasks/task1/onnx/yunet.onnx).
- 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 https://github.com/opencv/opencv_zoo/issues/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](../../tools/quantize/block_quantize.py) with `block_size=64`.
- Paper source: [Yunet: A tiny millisecond-level face detector](https://link.springer.com/article/10.1007/s11633-023-1423-y).
Results of accuracy evaluation with [tools/eval](../../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:
```shell
# 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:
```shell
# 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](./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}
}
```
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