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https://github.com/zisianw/FaceBoxes.PyTorch
A PyTorch Implementation of FaceBoxes
https://github.com/zisianw/FaceBoxes.PyTorch
face-detection faceboxes pytorch
Last synced: 13 days ago
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A PyTorch Implementation of FaceBoxes
- Host: GitHub
- URL: https://github.com/zisianw/FaceBoxes.PyTorch
- Owner: zisianw
- License: mit
- Created: 2019-01-10T08:04:39.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-12-08T07:44:01.000Z (almost 3 years ago)
- Last Synced: 2024-08-01T03:45:07.660Z (3 months ago)
- Topics: face-detection, faceboxes, pytorch
- Language: Python
- Size: 202 KB
- Stars: 843
- Watchers: 19
- Forks: 213
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# FaceBoxes in PyTorch
[](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 % | 98.55% |
| PASCAL | 96.77 % | 97.05% |
| FDDB | 95.90 % | 96.00% |
## 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](https://pytorch.org/) >= v1.0.0 following official instruction.
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: Codes are based on 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 $FaceBoxes_ROOT/
python3 train.py
```
If you do not wish to train the model, you can download [our pre-trained model](https://drive.google.com/file/d/1tRVwOlu0QtjvADQ2H7vqrRwsWEmaqioI) and save it in `$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
# evaluate using cpu
python3 test.py --cpu
# visualize detection results
python3 test.py -s --vis_thres 0.3
```
3. Download [eval_tool](https://github.com/sfzhang15/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)._