faceDetection-DSFD

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faceDetection-DSFD

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## [DSFD: Dual Shot Face Detector](https://arxiv.org/abs/1810.10220)

[![License](https://img.shields.io/badge/license-BSD-blue.svg)](https://raw.githubusercontent.com/penglisha/faceDetection-DSFD/master/LICENSE)

By [Jian Li](https://lijiannuist.github.io/), [Yabiao Wang](https://github.com/ChaunceyWang), [Changan Wang](https://github.com/HiKapok), [Ying Tai](https://github.com/tyshiwo)

## 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](https://arxiv.org/abs/1810.10220)!

Our DSFD face detector achieves state-of-the-art performance on [WIDER FACE](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/WiderFace_Results.html) and [FDDB](http://vis-www.cs.umass.edu/fddb/results.html) benchmark.

### WIDER FACE

<img src="https://github.com/TencentYoutuResearch/FaceDetection-DSFD/blob/master/imgs/DSFD_widerface.PNG" alt="DSFD Widerface Performance" width="1000px">

### FDDB

<img src="https://github.com/TencentYoutuResearch/FaceDetection-DSFD/blob/master/imgs/DSFD_fddb.PNG" alt="DSFD FDDB Performance" width="1000px">

## Qualitative Results

 <img src='https://github.com/TencentYoutuResearch/FaceDetection-DSFD/blob/master/imgs/DSFD_demo1.PNG' width='1000'/>

## 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`.
```bash
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](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) and [FDDB](https://drive.google.com/open?id=17t4WULUDgZgiSy5kpCax4aooyPaz3GQH) to `$DSFD_ROOT/data/`.

2. Download our [DSFD model](https://share.weiyun.com/567x0xQ) trained on WIDER FACE training set to `$DSFD_ROOT/weights/`.

3. Check out [`./demo.py`](https://github.com/TencentYoutuResearch/FaceDetection-DSFD/blob/master/test/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 threshold
```

4. Evaluate the trained model via [`./widerface_val.py`](https://github.com/TencentYoutuResearch/FaceDetection-DSFD/blob/master/test/widerface_test.py) on WIDER FACE.
```
python widerface_test.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
```

5. Evaluate the trained model via [`./fddb_test.py`](https://github.com/sTencentYoutuResearch/FaceDetection-DSFD/blob/master/test/fddb_test.py) on FDDB.

6. Download the [eval_tool](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/eval_script/eval_tools.zip) to show the WIDERFACE performance.

### Citing DSFD
Please cite DSFD in your publications if it helps your research:
```
@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}
}
```

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