Face-Detector-1MB-with-landmark

实现功能

• 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

带有关键点检测的超轻量级人脸检测器

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.

测试的运行环境

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

精度

Widerface测试

• 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.

Parameter and flop

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

Contents

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

Installation

Clone and install

1. git clone https://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

Data

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: from google cloud or baidu cloud Password: ruck

Training

1. Before training, you can check network configuration (e.g. batch_size, min_sizes and steps etc..) in data/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 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

Evaluation

Evaluation widerface val

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 from Here

cd ./widerface_evaluate
python setup.py build_ext --inplace
python evaluation.py

3. You can also use widerface official Matlab evaluate demo in Here

C++_inference _ncnn

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/tools
cd ./Face_Detector_ncnn/tools
./onnx2ncnn face.param face.bin

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

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

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

mkdir build
cd build
cmake ..
make -j4

5. run

./FaceDetector *.jpg

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

face.param
face.bin

References

• 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}