https://github.com/tencent/facedetection-dsfd
腾讯优图高精度双分支人脸检测器
https://github.com/tencent/facedetection-dsfd
Last synced: about 1 year ago
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腾讯优图高精度双分支人脸检测器
- Host: GitHub
- URL: https://github.com/tencent/facedetection-dsfd
- Owner: Tencent
- License: other
- Created: 2018-12-19T03:59:06.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-07-09T07:25:29.000Z (almost 6 years ago)
- Last Synced: 2025-05-15T07:05:40.558Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 145 MB
- Stars: 2,941
- Watchers: 103
- Forks: 729
- Open Issues: 66
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: License.txt
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README
## 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: swordli@tencent.com
```