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Think about boundary: Fusing multi-level boundary information for landmark heatmap regression.

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// repository documentation

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## Facial Landmark Detection
<div align=center>

![](https://raw.githubusercontent.com/Sierkinhane/TAB/main/images/JasonWang.gif)

</div>

![](https://raw.githubusercontent.com/Sierkinhane/TAB/main/images/landmark.png)

</div>

## Introduction
This is the official code of [Think about boundary: Fusing multi-level boundary information for landmark heatmap regression](https://arxiv.org/abs/2008.10924).
<div align=center>

![](https://raw.githubusercontent.com/Sierkinhane/TAB/main/images/tab.png)

</div>
<!--## Performance
<div align=center>

![](https://raw.githubusercontent.com/Sierkinhane/TAB/main/images/tab_vis.png)

</div>
-->

## Performance
### WFLW

| NME |  *test* | *pose* | *expression* | *illumination* | *makeup* | *occlution* | *blur* | 
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
|TAB | 3.94 | 6.70 | 4.11 | 3.84 | 3.85 | 4.56 | 4.45|

## Quick start
#### Environment
This code is developed using on Python 3.7 and PyTorch 1.0.0 on Ubuntu 16.04 with NVIDIA GPUs. Training and testing are 
performed using 1 NVIDIA P100 GPU with CUDA 9.0 and cuDNN 7.5. Other platforms or GPUs are not fully tested.

#### Install
1. Install PyTorch 1.0.0 following the [official instructions](https://pytorch.org/)
2. Install dependencies
````bash
pip install -r requirements.txt
````

#### Demo
1. Download pre-trained model from [BaiduYun(Acess Code:p1q9)](https://pan.baidu.com/s/1lwc7zTz8HTFM0mu0hCUM6w) to `pre-trained` directory.

```bash
python tools/demo.py --cfg experiments/wflw/face_alignment_wflw_tab.yaml --type video --best_model pre-trained/wflw_nme_0.0394_best_checkpoint_vgg_multi-scale_2x.pth
```

#### Data

1. You need to download the annotations files(supported by HRNet) which have been processed from [OneDrive](https://1drv.ms/u/s!AiWjZ1LamlxzdmYbSkHpPYhI8Ms), [Cloudstor](https://cloudstor.aarnet.edu.au/plus/s/m9lHU2aJId8Sh8l), and [BaiduYun(Acess Code:ypxg)](https://pan.baidu.com/s/1Yg1IEp3l2IpGPolpUsWdfg).

Your `data` directory should look like this:

#### Train the SCBE module
Please specify the configuration file in `experiments` (learning rate should be adjusted when the number of GPUs is changed).
````bash
python tools/train_scbe.py --cfg experiments/wflw/face_alignment_wflw_tab.yaml
````

#### Train the whole model
Please specify the configuration file in `experiments` (learning rate should be adjusted when the number of GPUs is changed).
````bash
python tools/train_tab.py --cfg experiments/wflw/face_alignment_wflw_tab.yaml
````

#### Test the model
Please specify the configuration file in `experiments` (learning rate should be adjusted when the number of GPUs is changed).
````bash
python tools/test.py --cfg experiments/wflw/face_alignment_wflw_tab.yaml --best_model pre-trained/wflw_nme_0.0394_best_checkpoint_vgg_multi-scale_2x.pth
````

## Citation
```
@article{xie2020think,
  title={Think about boundary: Fusing multi-level boundary information for landmark heatmap regression},
  author={Xie, Jinheng and Wan, Jun and Shen, Linlin and Lai, Zhihui},
  journal={arXiv preprint arXiv:2008.10924},
  year={2020}
}
```

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