# FaceBoxes.PyTorch

**Repository Path**: yinlichang19/FaceBoxes.PyTorch

## Basic Information

- **Project Name**: FaceBoxes.PyTorch
- **Description**: A PyTorch Implementation of FaceBoxes
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-01-25
- **Last Updated**: 2021-01-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FaceBoxes in PyTorch

[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)

By [Zisian Wong](https://github.com/zisianw), [Shifeng Zhang](http://www.cbsr.ia.ac.cn/users/sfzhang/)

A [PyTorch](https://pytorch.org/) implementation of [FaceBoxes: A CPU Real-time Face Detector with High Accuracy](https://arxiv.org/abs/1708.05234). The official code in Caffe can be found [here](https://github.com/sfzhang15/FaceBoxes).

## Performance
| Dataset | Original Caffe | PyTorch Implementation |
|:-|:-:|:-:|
| AFW | 98.98 % | 98.47% |
| PASCAL | 96.77 % | 96.84% |
| FDDB | 95.90 % | 95.44% |

## Citation
Please cite the paper in your publications if it helps your research:

    @inproceedings{zhang2017faceboxes,
      title = {Faceboxes: A CPU Real-time Face Detector with High Accuracy},
      author = {Zhang, Shifeng and Zhu, Xiangyu and Lei, Zhen and Shi, Hailin and Wang, Xiaobo and Li, Stan Z.},
      booktitle = {IJCB},
      year = {2017}
    }

### Contents
- [Installation](#installation)
- [Training](#training)
- [Evaluation](#evaluation)
- [References](#references)

## Installation
1. Install [PyTorch-0.4.0](https://pytorch.org/) according to your environment.

2. Clone this repository. We will call the cloned directory as `$FaceBoxes_ROOT`.
```Shell
git clone https://github.com/zisianw/FaceBoxes.PyTorch.git
```

3. Compile the nms:
```Shell
./make.sh
```

_Note: We currently only support PyTorch-0.4.0 and Python 3+._

## Training
1. Download [WIDER FACE](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/index.html) dataset, place the images under this directory:
  ```Shell
  $FaceBoxes_ROOT/data/WIDER_FACE/images
  ```
2. Convert WIDER FACE annotations to VOC format or download [our converted annotations](https://drive.google.com/open?id=1-s4QCu_v76yNwR-yXMfGqMGgHQ30WxV2), place them under this directory:
  ```Shell
  $FaceBoxes_ROOT/data/WIDER_FACE/annotations
  ```

3. Train the model using WIDER FACE:
  ```Shell
  cd $FaceBoxes_ROOT/
  python3 train.py
  ```

If you do not wish to train the model, you can download [our pre-trained model](https://drive.google.com/open?id=128m1QasIwQRkrY-Eb5Epi-ShXnrZWUCQ) and save it in `$FaceBoxes_ROOT/weights`.


## Evaluation
1. Download the images of [AFW](https://drive.google.com/open?id=1Kl2Cjy8IwrkYDwMbe_9DVuAwTHJ8fjev), [PASCAL Face](https://drive.google.com/open?id=1p7dDQgYh2RBPUZSlOQVU4PgaSKlq64ik) and [FDDB](https://drive.google.com/open?id=17t4WULUDgZgiSy5kpCax4aooyPaz3GQH) to:
```Shell
$FaceBoxes_ROOT/data/AFW/images/
$FaceBoxes_ROOT/data/PASCAL/images/
$FaceBoxes_ROOT/data/FDDB/images/
```

2. Evaluate the trained model using:
```Shell
# dataset choices = ['AFW', 'PASCAL', 'FDDB']
python3 test.py --dataset FDDB
```

3. Download [eval_tool](https://bitbucket.org/marcopede/face-eval) to evaluate the performance.
    
## References
- [Official release (Caffe)](https://github.com/sfzhang15/FaceBoxes)
- A huge thank you to SSD ports in PyTorch that have been helpful:
  * [ssd.pytorch](https://github.com/amdegroot/ssd.pytorch), [RFBNet](https://github.com/ruinmessi/RFBNet)

  _Note: If you can not download the converted annotations, the provided images and the trained model through the above links, you can download them through [BaiduYun](https://pan.baidu.com/s/1HoW3wbldnbmgW2PS4i4Irw)._