FaceDetection-DSFD

## Update * 2019.04: Release pytorch-version DSFD inference code. * 2019.03: DSFD is accepted by CVPR2019. * 2018.10: Our DSFD ranks No.1 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) ## Introduction

In this repo, 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)! or poster [slide](./imgs/DSFD_CVPR2019_poster.pdf)!

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

### FDDB

## 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 `$DSFD_ROOT`. ```bash git clone xxxxxx/FaceDetection-DSFD.git cd FaceDetection-DSFD export CUDA_VISIBLE_DEVICES=0 ``` ### Evaluation 1. Download the images of [WIDER FACE](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) and [FDDB](https://drive.google.com/open?id=17t4WULUDgZgiSy5kpCax4aooyPaz3GQH) to `$DSFD_ROOT/data/`. 2. Download our DSFD model [微云](https://share.weiyun.com/567x0xQ) [google drive](https://drive.google.com/file/d/1WeXlNYsM6dMP3xQQELI-4gxhwKUQxc3-/view?usp=sharing) 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 ``` 4. 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 ``` 5. Download the [eval_tool](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/eval_script/eval_tools.zip) to show the WIDERFACE performance. 6. 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 ``` 7. Download the [evaluation](http://vis-www.cs.umass.edu/fddb/evaluation.tgz) to show the FDDB performance. 8. Lightweight DSFD is [here](https://github.com/lijiannuist/lightDSFD). ## Qualitative Results

### 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} } ``` ## Contact For any question, please file an issue or contact ``` Jian Li: [email protected] ```