- 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)
方法 | Easy | Medium | Hard |
---|---|---|---|
libfacedetection v1(caffe) | 0.65 | 0.5 | 0.233 |
libfacedetection v2(caffe) | 0.714 | 0.585 | 0.306 |
version-slim(原版) | 0.765 | 0.662 | 0.385 |
version-RFB(原版) | 0.784 | 0.688 | 0.418 |
version-slim(our) | 0.795 | 0.683 | 0.34.5 |
version-RFB(our) | 0.814 | 0.710 | 0.363 |
Retinaface-Mobilenet-0.25(our) | 0.811 | 0.697 | 0.376 |
- 在wider face val精度(单尺度输入分辨率:640*480)
方法 | Easy | Medium | Hard |
---|---|---|---|
libfacedetection v1(caffe) | 0.741 | 0.683 | 0.421 |
libfacedetection v2(caffe) | 0.773 | 0.718 | 0.485 |
version-slim(原版) | 0.757 | 0.721 | 0.511 |
version-RFB(原版) | 0.851 | 0.81 | 0.541 |
version-slim(our) | 0.850 | 0.808 | 0.595 |
version-RFB(our) | 0.865 | 0.828 | 0.622 |
Retinaface-Mobilenet-0.25(our) | 0.873 | 0.836 | 0.638 |
ps: 测试的时候,长边为320 或者 640 ,图像等比例缩放.
方法 | parameter(M) | flop(M) |
---|---|---|
version-slim(our) | 0.343 | 98.793 |
version-RFB(our) | 0.359 | 118.435 |
Retinaface-Mobilenet-0.25(our) | 0.426 | 193.921 |
ps: 320*240作为输入
-
git clone https://github.com/biubug6/Face-Detector-1MB-with-landmark.git
-
Pytorch version 1.1.0+ and torchvision 0.3.0+ are needed.
-
Codes are based on Python 3
- 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.
- We provide the organized dataset we used as in the above directory structure.
Link: from google cloud or baidu cloud Password: ruck
-
Before training, you can check network configuration (e.g. batch_size, min_sizes and steps etc..) in
data/config.py and train.py
. -
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 or
CUDA_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.pth
slim_Final.pth
- Generate txt file
python test_widerface.py --trained_model weight_file --network mobile0.25 or slim or RFB
- Evaluate txt results. Demo come from Here
cd ./widerface_evaluate
python setup.py build_ext --inplace
python evaluation.py
- You can also use widerface official Matlab evaluate demo in Here
- Generate onnx file
python convert_to_onnx.py --trained_model weight_file --network mobile0.25 or slim or RFB
- Onnx file change to ncnn(*.param and *.param)
cp *.onnx ./Face_Detector_ncnn/tools
cd ./Face_Detector_ncnn/tools
./onnx2ncnn face.param face.bin
- Move *.param and *.bin to model
cp face.param ../model
cp face.bin ../model
- Build Project(set opencv path in CmakeList.txt)
mkdir build
cd build
cmake ..
make -j4
- run
./FaceDetector *.jpg
We also provide the converted file in "./model".
face.param
face.bin
@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}