GitHub - TencentYoutuResearch/FaceDetection-DSFD

README.md

DSFD: Dual Shot Face Detector

By Jian Li, Yabiao Wang, Changan Wang, Ying Tai

Introduction

This paper is accepted by CVPR 2019.

In this paper, we propose a novel face detection network, named DSFD, with superior performance over the state-of-the-art face detectors. You can use the code to evaluate our DSFD for face detection.

For more details, please refer to our paper DSFD: Dual Shot Face Detector!

Our DSFD face detector achieves state-of-the-art performance on WIDER FACE and FDDB benchmark.

WIDER FACE

FDDB

Qualitative Results

Requirements

• Torch == 0.3.1
• Torchvision == 0.2.1
• Python == 3.6
• NVIDIA GPU == Tesla P40
• Linux CUDA CuDNN

Getting Started

Installation

Clone the github repository. We will call the cloned directory as $DSFD_ROOT.

git clone https://github.com/TencentYoutuResearch/FaceDetection-DSFD.git
cd FaceDetection-DSFD
export CUDA_VISIBLE_DEVICES=0

Evaluation

1. Download the images of WIDER FACE and FDDB to $DSFD_ROOT/data/.

2. Download our DSFD model [微云] [google drive] trained on WIDER FACE training set to $DSFD_ROOT/weights/.

3. Check out ./demo.py on how to detect faces using the DSFD model and how to plot detection results.

python demo.py [--trained_model [TRAINED_MODEL]] [--img_root  [IMG_ROOT]] 
               [--save_folder [SAVE_FOLDER]] [--visual_threshold [VISUAL_THRESHOLD]] 
    --trained_model      Path to the saved model
    --img_root           Path of test images
    --save_folder        Path of output detection resutls
    --visual_threshold   Confidence thresh

1. Evaluate the trained model via ./widerface_val.py on WIDER FACE.

python widerface_val.py [--trained_model [TRAINED_MODEL]] [--save_folder [SAVE_FOLDER]] 
                         [--widerface_root [WIDERFACE_ROOT]]
    --trained_model      Path to the saved model
    --save_folder        Path of output widerface resutls
    --widerface_root     Path of widerface dataset

1. Download the eval_tool to show the WIDERFACE performance.

2. Evaluate the trained model via ./fddb_test.py on FDDB.

python widerface_test.py [--trained_model [TRAINED_MODEL]] [--split_dir [SPLIT_DIR]] 
                         [--data_dir [DATA_DIR]] [--det_dir [DET_DIR]]
    --trained_model      Path of the saved model
    --split_dir          Path of fddb folds
    --data_dir           Path of fddb all images
    --det_dir            Path to save fddb results

1. Download the evaluation to show the FDDB performance.

Citation

If you find DSFD useful in your research, please consider citing:

@inproceedings{li2018dsfd,
  title={DSFD: Dual Shot Face Detector},
  author={Li, Jian and Wang, Yabiao and Wang, Changan and Tai, Ying and Qian, Jianjun and Yang, Jian and Wang, Chengjie and Li, Jilin and Huang, Feiyue},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2019}
}