Large-scale CelebFaces Attributes (CelebA) Dataset


Ziwei Liu   Ping Luo   Xiaogang Wang   Xiaoou Tang
Multimedia Laboratory, The Chinese University of Hong Kong

News

2021-09-10 Another related dataset, CelebA-Dialog has been released.

2020-07-10 Two related datasets, CelebAMask-HQ and CelebA-Spoof, have been released.

2016-07-29 If Dropbox is not accessible, please download the dataset using Google Drive or Baidu Drive (password: rp0s).

Details


CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including

  • 10,177 number of identities,

  • 202,599 number of face images, and

  • 5 landmark locations, 40 binary attributes annotations per image.

The dataset can be employed as the training and test sets for the following computer vision tasks: face attribute recognition, face recognition, face detection, landmark (or facial part) localization, and face editing & synthesis.

Sample Images



Downloads


  • In-The-Wild Images

  • Align&Cropped Images

  • Landmarks Annotations

  • Attributes Annotations

  • Identities Annotations

Evaluation


  • Train/Val/Test Partitions


If the above links are not accessable, you could download the dataset using Baidu Drive.
For more details of the dataset, please refer to the paper "Deep Learning Face Attributes in the Wild".

Agreement

  • The CelebA dataset is available for non-commercial research purposes only.
  • All images of the CelebA dataset are obtained from the Internet which are not property of MMLAB, The Chinese University of Hong Kong. The MMLAB is not responsible for the content nor the meaning of these images.
  • 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 CelebA dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.
  • The MMLAB reserves the right to terminate your access to the CelebA dataset at any time.
  • The face identities are released upon request for research purposes only. Please contact us for details.

Citation

@inproceedings{liu2015faceattributes,
  title = {Deep Learning Face Attributes in the Wild},
  author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
  booktitle = {Proceedings of International Conference on Computer Vision (ICCV)},
  month = {December},
  year = {2015} 
}

Related Datasets


Contact


Please contact Ziwei Liu and Ping Luo for questions about the dataset.