FaceDetector-Base-Yolov3-spp

FLYL Demo

Contents

Deep Face Detection

• Introduction
• Training Data
• Train
• Pretrained Models
• Evaluation
• Face Detection System

Deep Face Detection

Introduction

In this repository, we provide training data, network settings and loss designs for deep face detection. The training data is Widerface datasets, which were already packed in darknet format.he network backbone is darknet53, classifical loss is focal loss,which improve 1.2% Map on Widerface val.read focal loss and code,please check the blog.

This repository can help researcher/engineer to develop deep face detection algorithms quickly by only two steps: download the dataset and run the training script.In sum,we can run a face detection system in window easily.

Training Data

All Widerface data are into VOC format.Please click BaiduDrive 提取码：r4wc

• if you want to make data into Voc format with yourself, please check data_process/widerfaceToVoc.ipynb

Train

Requirements

• On windows

  • Visual Studio 2015 or 2017
  • cuda 10.0
  • cudnn 7
  • openCV3.0

• On linux

  • cuda9.0
  • openCV3.0
  • cudnn7

1、Clone the repository

git clone https://github.com/jmu201521121021/FaceDetector-Base-Yolov3-spp.git

2、Install darknetAB with GPU surpport

• detail,please check here

3、Download the training set (Widerface) and place it in darknet/build/darknet/x64/data/voc

4、go to darknet/build/darknet/x64/data/voc/

python  voc_label.py

5、start train,into ./train

sh train.sh

Pretrained Models

• Please check here BaiduDrive 提取码：if0r ​

Evaluation

• Test on widerFace val dataset

  

• Test on FDDB

Face Detection System

• Requiremets
  • windows10
  • GPU memory >=3GB (Nvidia version)
• Download,Please check BaiduDrive 提取码：zfik .

Contact

[Chen Jinshan](jinshanchen[at]jmu.edu.cn)