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CelebA
2D Box
Classification
2D Keypoints
Face
|...
License: Unknown

Overview

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 detection,
landmark (or facial part) localization, and face editing & synthesis.

Citation

Please use the following citation when referencing the dataset:

@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}
}
Data Summary
Type
Image,
Amount
202.599K
Size
9779.6GB
Provided by
CUHK Multimedia Lab
The CUHK Multimedia Lab (MMLab) is one of the pioneering institutes on deep learning. In GPU Technology Conference (GTC) 2016, a world-wide technology summit, our lab is recognized as one of the top ten AI pioneers, and listed together with top research groups in the world (e.g. MIT, Stanford, Berkeley, and Univ. of Toronto).
| Amount 202.599K | Size 9779.6GB
CelebA
2D Box Classification 2D Keypoints
Face
License: Unknown

Overview

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 detection,
landmark (or facial part) localization, and face editing & synthesis.

Citation

Please use the following citation when referencing the dataset:

@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}
}
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