# LaPa-Dataset for face parsing (unofficial mirror)
## Introduction
we develop a high-efficiency framework for pixel-level face parsing annotating and construct a new large-scale **La**ndmark guided face **Pa**rsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks.


## Citation
If you use our datasets, please cite the following paper:

[A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing.](https://aaai.org/ojs/index.php/AAAI/article/view/6832/6686) Yinglu Liu, Hailin Shi, Hao Shen, Yue Si, Xiaobo Wang, Tao Mei. In AAAI, 2020.

```
@inproceedings{liu2020new,  
  title={A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing.},  
  author={Liu, Yinglu and Shi, Hailin and Shen, Hao and Si, Yue and Wang, Xiaobo and Mei, Tao},  
  booktitle={AAAI},  
  pages={11637--11644},  
  year={2020}  
}
```

## License
This LaPa Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms.
region:us
{}
TAGS
#region-us 

# LaPa-Dataset for face parsing (unofficial mirror)
## Introduction
we develop a high-efficiency framework for pixel-level face parsing annotating and construct a new large-scale Landmark guided face Parsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks.


If you use our datasets, please cite the following paper:

A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing. Yinglu Liu, Hailin Shi, Hao Shen, Yue Si, Xiaobo Wang, Tao Mei. In AAAI, 2020.



## License
This LaPa Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms.
# LaPa-Dataset for face parsing (unofficial mirror) ## Introduction
we develop a high-efficiency framework for pixel-level face parsing annotating and construct a new large-scale Landmark guided face Parsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks.


If you use our datasets, please cite the following paper:

A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing. Yinglu Liu, Hailin Shi, Hao Shen, Yue Si, Xiaobo Wang, Tao Mei. In AAAI, 2020. ## License
This LaPa Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms.
TAGS
#region-us 
 # LaPa-Dataset for face parsing (unofficial mirror) ## Introduction
we develop a high-efficiency framework for pixel-level face parsing annotating and construct a new large-scale Landmark guided face Parsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks.


If you use our datasets, please cite the following paper:

A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing. Yinglu Liu, Hailin Shi, Hao Shen, Yue Si, Xiaobo Wang, Tao Mei. In AAAI, 2020. ## License
This LaPa Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms.