https://github.com/switchablenorms/celebamask-hq
A large-scale face dataset for face parsing, recognition, generation and editing.
https://github.com/switchablenorms/celebamask-hq
celeba face-generation face-recognition face-segmentation generative-adversarial-network
Last synced: about 1 year ago
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A large-scale face dataset for face parsing, recognition, generation and editing.
- Host: GitHub
- URL: https://github.com/switchablenorms/celebamask-hq
- Owner: switchablenorms
- Created: 2019-04-01T06:35:34.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-06-20T04:33:41.000Z (almost 2 years ago)
- Last Synced: 2025-05-07T11:40:56.633Z (about 1 year ago)
- Topics: celeba, face-generation, face-recognition, face-segmentation, generative-adversarial-network
- Language: Python
- Homepage:
- Size: 41.5 MB
- Stars: 2,205
- Watchers: 48
- Forks: 355
- Open Issues: 60
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# CelebAMask-HQ
[[Paper]](https://arxiv.org/abs/1907.11922) [[Demo]](https://www.youtube.com/watch?v=T1o38DFalWs)
**CelebAMask-HQ** is a large-scale face image dataset that has **30,000** high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has segmentation mask of facial attributes corresponding to CelebA.
The masks of CelebAMask-HQ were manually-annotated with the size of **512 x 512** and **19 classes** including all facial components and accessories such as skin, nose, eyes, eyebrows, ears, mouth, lip, hair, hat, eyeglass, earring, necklace, neck, and cloth.
CelebAMask-HQ can be used to **train and evaluate algorithms of face parsing, face recognition, and GANs for face generation and editing**.
* If you need the identity labels and the attribute labels of the images, please send request to the [CelebA team](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html).
* Demo of interactive facial image manipulation
## Sample Images
## Face Manipulation Model with CelebAMask-HQ
CelebAMask-HQ can be used on several research fields including: facial image manipulation, face parsing, face recognition, and face hallucination. We showcase an application on interactive facial image manipulation as bellow:
* Samples of interactive facial image manipulation
## CelebAMask-HQ Dataset Downloads
* Google Drive: [downloading link](https://drive.google.com/open?id=1badu11NqxGf6qM3PTTooQDJvQbejgbTv)
* Baidu Drive: [downloading link](https://pan.baidu.com/s/1wN1E-B1bJ7mE1mrn9loj5g)
## Related Works
* **CelebA** dataset:
Ziwei Liu, Ping Luo, Xiaogang Wang and Xiaoou Tang, "Deep Learning Face Attributes in the Wild", in IEEE International Conference on Computer Vision (ICCV), 2015
* **CelebA-HQ** was collected from CelebA and further post-processed by the following paper :
Karras et. al, "Progressive Growing of GANs for Improved Quality, Stability, and Variation", in Internation Conference on Reoresentation Learning (ICLR), 2018
## Dataset Agreement
* The CelebAMask-HQ dataset is available for **non-commercial research purposes** only.
* You agree **not to** reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
* You agree **not to** further copy, publish or distribute any portion of the CelebAMask-HQ dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.
## Related Projects using CelebAMask-HQ
* [SPADE-TensorFlow](https://github.com/taki0112/SPADE-Tensorflow)
* [FaceParsing-PyTorch](https://github.com/zllrunning/face-parsing.PyTorch)
## License and Citation
The use of this software is RESTRICTED to **non-commercial research and educational purposes**.
```
@inproceedings{CelebAMask-HQ,
title={MaskGAN: Towards Diverse and Interactive Facial Image Manipulation},
author={Lee, Cheng-Han and Liu, Ziwei and Wu, Lingyun and Luo, Ping},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}
```