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• Retinaface-mobile0.25的训练/测试/评估/ncnn C++推理
• Face-Detector-1MB slim和RFB版本的训练/测试/评估/ncnn C++推理
• 人脸5个关键点检测
• 支持onnx导出
• 网络parameter和flop计算

提供了一系列适合移动端部署包含关键的人脸检测器: 对Retinaface-mobile0.25修改anchor尺寸,使其更适合边缘计算; 重新实现了Face-Detector-1MB 并添加了关键点检测和ncnn C++部署功能, 在绝大部分情况下精度均好于原始版本.

• Ubuntu18.04
• Python3.7
• Pytorch1.2
• CUDA10.0 + CUDNN7.5

• 在wider face val精度（单尺度输入分辨率：320*240）

• 在wider face val精度（单尺度输入分辨率：640*480）

ps: 测试的时候,长边为320 或者 640 ,图像等比例缩放.

ps: 320*240作为输入

• Installation
• Training
• Evaluation
• C++_inference _ncnn
• References

1. git clonehttps://github.com/biubug6/Face-Detector-1MB-with-landmark.git

2. Pytorch version 1.1.0+ and torchvision 0.3.0+ are needed.

3. Codes are based on Python 3

1. The dataset directory as follows:

  ./data/widerface/    train/      images/      label.txt    val/      images/      wider_val.txt

ps: wider_val.txt only include val file names but not label information.

2. We provide the organized dataset we used as in the above directory structure.

Link: fromgoogle cloud orbaidu cloud Password: ruck

1. Before training, you can check network configuration (e.g. batch_size, min_sizes and steps etc..) indata/config.py and train.py.

2. Train the model using WIDER FACE:

CUDA_VISIBLE_DEVICES=0 python train.py --network mobile0.25 or CUDA_VISIBLE_DEVICES=0 python train.py --network slim orCUDA_VISIBLE_DEVICES=0 python train.py --network RFB

If you don't want to train, we also provide a trained model on ./weights

mobilenet0.25_Final.pth RBF_Final.pthslim_Final.pth

1. Generate txt file

python test_widerface.py --trained_model weight_file --network mobile0.25 or slim or RFB

2. Evaluate txt results. Demo come fromHere

cd ./widerface_evaluatepython setup.py build_ext --inplacepython evaluation.py

3. You can also use widerface official Matlab evaluate demo inHere

1. Generate onnx file

python convert_to_onnx.py --trained_model weight_file --network mobile0.25 or slim or RFB

2. Onnx file change to ncnn(*.param and *.param)

cp*.onnx ./Face_Detector_ncnn/toolscd ./Face_Detector_ncnn/tools./onnx2ncnn face.param face.bin

3. Move *.param and *.bin to model

cp face.param ../modelcp face.bin ../model

4. Build Project(set opencv path in CmakeList.txt)

mkdir buildcd buildcmake ..make -j4

5. run

./FaceDetector*.jpg

We also provide the converted file in "./model".

face.paramface.bin

• FaceBoxes
• Retinaface (mxnet)
• Retinaface (pytorch)
• Ultra-Light-Fast-Generic-Face-Detector-1MB

@inproceedings{deng2019retinaface,title={RetinaFace: Single-stage Dense Face Localisation in the Wild},author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos},booktitle={arxiv},year={2019}