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 and FDDB
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! or poster slide!
Our DSFD face detector achieves state-of-the-art performance on WIDER FACE and FDDB 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.
git clone xxxxxx/FaceDetection-DSFD.git
cd FaceDetection-DSFD
export CUDA_VISIBLE_DEVICES=0Evaluation
-
Download the images of WIDER FACE and FDDB to
$DSFD_ROOT/data/. -
Download our DSFD model 微云 google drive trained on WIDER FACE training set to
$DSFD_ROOT/weights/. -
Check out
./demo.pyon 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
- Evaluate the trained model via
./widerface_val.pyon 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
-
Download the eval_tool to show the WIDERFACE performance.
-
Evaluate the trained model via
./fddb_test.pyon 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
- Download the evaluation to show the FDDB performance.
- Lightweight DSFD is here.
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]