Awesome
Training for libfacedetection in PyTorch
It is the training program for libfacedetection. The source code is based on MMDetection. Some data processing functions from SCRFD modifications.
Visualization of our network architecture: [netron].
Contents
Installation
- Create conda environment. e.g.
conda create -n yunet python=3.8 conda activate yunet
- Install PyTorch == v1.8.2 (LTS) following official instruction. e.g.
On GPU platforms (cu11.1):
On GPU platforms (cu10.2):# LINUX: conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia # WINDOWS: conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c conda-forge
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch-lts
- Install MMCV >= v1.3.17 but <=1.6.0 following official instruction. e.g.
# cu11.1 pip install mmcv-full==1.3.17 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html # cu10.2 pip install mmcv-full==1.3.17 -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html
- Clone this repository. We will call the cloned directory as
$TRAIN_ROOT
.git clone https://github.com/ShiqiYu/libfacedetection.train.git cd libfacedetection.train
- Install dependencies.
python setup.py develop pip install -r requirements.txt
Note:
- Codes are based on Python 3+.
- If meet error "ModuleNotFoundError: No module named 'torch.ao'", you can
Ctrl + click
to origin line and replacetorch.ao
totorch
Preparation
- Download the WIDER Face dataset and its evaluation tools.
- Extract zip files under
$TRAIN_ROOT/data/widerface
as follows:$ tree data/widerface data/widerface ├── wider_face_split ├── WIDER_test ├── WIDER_train ├── WIDER_val └── labelv2 ├── train │ └── labelv2.txt └── val ├── gt └── labelv2.txt
NOTE:
The labelv2
comes from SCRFD.
Training
Following MMdetection training processing.
CUDA_VISIBLE_DEVICES=0,1 bash tools/dist_train.sh ./configs/yunet_n.py 2 12345
Detection
python tools/detect_image.py ./configs/yunet_n.py ./weights/yunet_n.pth ./image.jpg
Evaluation on WIDER Face
python tools/test_widerface.py ./configs/yunet_n.py ./weights/yunet_n.pth --mode 2
Performance on WIDER Face (Val): confidence_threshold=0.02, nms_threshold=0.45, in origin size:
AP_easy=0.892, AP_medium=0.883, AP_hard=0.811
Export CPP source code
The following bash code can export a CPP file for project libfacedetection
python tools/yunet2cpp.py ./configs/yunet_n.py ./weights/yunet_n.pth
Export to onnx model
Export to onnx model for libfacedetection/example/opencv_dnn.
python tools/yunet2onnx.py ./configs/yunet_n.py ./weights/yunet_n.pth
Compare ONNX model with other works
Inference on exported ONNX models using ONNXRuntime:
python tools/compare_inference.py ./onnx/yunet_n.onnx --mode AUTO --eval --score_thresh 0.02 --nms_thresh 0.45
Some similar approaches(e.g. SCRFD, Yolo5face, retinaface) to inference are also supported.
With Intel i7-12700K and input_size = origin size, score_thresh = 0.02, nms_thresh = 0.45
, some results are list as follow:
Model | AP_easy | AP_medium | AP_hard | #Params | Params Ratio | MFlops (320x320) | FPS(320x320) |
---|---|---|---|---|---|---|---|
SCRFD0.5(ICLR2022) | 0.892 | 0.885 | 0.819 | 631,410 | 8.32x | 184 | 284 |
Retinaface0.5(CVPR2020) | 0.907 | 0.883 | 0.742 | 426,608 | 5.62X | 245 | 235 |
YuNet_n(Ours) | 0.892 | 0.883 | 0.811 | 75,856 | 1.00x | 149 | 456 |
YuNet_s(Ours) | 0.887 | 0.871 | 0.768 | 54,608 | 0.72x | 96 | 537 |
The compared models can be downloaded from Google Drive.
Citation
We published a paper for the main idea of this repository:
@article{yunet,
title={YuNet: A Tiny Millisecond-level Face Detector},
author={Wu, Wei and Peng, Hanyang and Yu, Shiqi},
journal={Machine Intelligence Research},
pages={1--10},
year={2023},
doi={10.1007/s11633-023-1423-y},
publisher={Springer}
}
The paper can be open accessed at https://link.springer.com/article/10.1007/s11633-023-1423-y.
The loss used in training is EIoU, a novel extended IoU. More details can be found in:
@article{eiou,
author={Peng, Hanyang and Yu, Shiqi},
journal={IEEE Transactions on Image Processing},
title={A Systematic IoU-Related Method: Beyond Simplified Regression for Better Localization},
year={2021},
volume={30},
pages={5032-5044},
doi={10.1109/TIP.2021.3077144}
}
The paper can be open accessed at https://ieeexplore.ieee.org/document/9429909.
We also published a paper on face detection to evaluate different methods.
@article{facedetect-yu,
author={Feng, Yuantao and Yu, Shiqi and Peng, Hanyang and Li, Yan-Ran and Zhang, Jianguo},
journal={IEEE Transactions on Biometrics, Behavior, and Identity Science},
title={Detect Faces Efficiently: A Survey and Evaluations},
year={2022},
volume={4},
number={1},
pages={1-18},
doi={10.1109/TBIOM.2021.3120412}
}
The paper can be open accessed at https://ieeexplore.ieee.org/document/9580485