- Description:
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, and landmark (or facial part) localization.
Additional Documentation: Explore on Papers With Code
Source code:
tfds.datasets.celeb_a.Builder
Versions:
2.0.1
: New split API (https://tensorflow.org/datasets/splits)2.1.0
(default): Identity feature added.
Download size:
1.39 GiB
Dataset size:
1.63 GiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'test' |
19,962 |
'train' |
162,770 |
'validation' |
19,867 |
- Feature structure:
FeaturesDict({
'attributes': FeaturesDict({
'5_o_Clock_Shadow': bool,
'Arched_Eyebrows': bool,
'Attractive': bool,
'Bags_Under_Eyes': bool,
'Bald': bool,
'Bangs': bool,
'Big_Lips': bool,
'Big_Nose': bool,
'Black_Hair': bool,
'Blond_Hair': bool,
'Blurry': bool,
'Brown_Hair': bool,
'Bushy_Eyebrows': bool,
'Chubby': bool,
'Double_Chin': bool,
'Eyeglasses': bool,
'Goatee': bool,
'Gray_Hair': bool,
'Heavy_Makeup': bool,
'High_Cheekbones': bool,
'Male': bool,
'Mouth_Slightly_Open': bool,
'Mustache': bool,
'Narrow_Eyes': bool,
'No_Beard': bool,
'Oval_Face': bool,
'Pale_Skin': bool,
'Pointy_Nose': bool,
'Receding_Hairline': bool,
'Rosy_Cheeks': bool,
'Sideburns': bool,
'Smiling': bool,
'Straight_Hair': bool,
'Wavy_Hair': bool,
'Wearing_Earrings': bool,
'Wearing_Hat': bool,
'Wearing_Lipstick': bool,
'Wearing_Necklace': bool,
'Wearing_Necktie': bool,
'Young': bool,
}),
'identity': FeaturesDict({
'Identity_No': int64,
}),
'image': Image(shape=(218, 178, 3), dtype=uint8),
'landmarks': FeaturesDict({
'lefteye_x': int64,
'lefteye_y': int64,
'leftmouth_x': int64,
'leftmouth_y': int64,
'nose_x': int64,
'nose_y': int64,
'righteye_x': int64,
'righteye_y': int64,
'rightmouth_x': int64,
'rightmouth_y': int64,
}),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
attributes | FeaturesDict | |||
attributes/5_o_Clock_Shadow | Tensor | bool | ||
attributes/Arched_Eyebrows | Tensor | bool | ||
attributes/Attractive | Tensor | bool | ||
attributes/Bags_Under_Eyes | Tensor | bool | ||
attributes/Bald | Tensor | bool | ||
attributes/Bangs | Tensor | bool | ||
attributes/Big_Lips | Tensor | bool | ||
attributes/Big_Nose | Tensor | bool | ||
attributes/Black_Hair | Tensor | bool | ||
attributes/Blond_Hair | Tensor | bool | ||
attributes/Blurry | Tensor | bool | ||
attributes/Brown_Hair | Tensor | bool | ||
attributes/Bushy_Eyebrows | Tensor | bool | ||
attributes/Chubby | Tensor | bool | ||
attributes/Double_Chin | Tensor | bool | ||
attributes/Eyeglasses | Tensor | bool | ||
attributes/Goatee | Tensor | bool | ||
attributes/Gray_Hair | Tensor | bool | ||
attributes/Heavy_Makeup | Tensor | bool | ||
attributes/High_Cheekbones | Tensor | bool | ||
attributes/Male | Tensor | bool | ||
attributes/Mouth_Slightly_Open | Tensor | bool | ||
attributes/Mustache | Tensor | bool | ||
attributes/Narrow_Eyes | Tensor | bool | ||
attributes/No_Beard | Tensor | bool | ||
attributes/Oval_Face | Tensor | bool | ||
attributes/Pale_Skin | Tensor | bool | ||
attributes/Pointy_Nose | Tensor | bool | ||
attributes/Receding_Hairline | Tensor | bool | ||
attributes/Rosy_Cheeks | Tensor | bool | ||
attributes/Sideburns | Tensor | bool | ||
attributes/Smiling | Tensor | bool | ||
attributes/Straight_Hair | Tensor | bool | ||
attributes/Wavy_Hair | Tensor | bool | ||
attributes/Wearing_Earrings | Tensor | bool | ||
attributes/Wearing_Hat | Tensor | bool | ||
attributes/Wearing_Lipstick | Tensor | bool | ||
attributes/Wearing_Necklace | Tensor | bool | ||
attributes/Wearing_Necktie | Tensor | bool | ||
attributes/Young | Tensor | bool | ||
identity | FeaturesDict | |||
identity/Identity_No | Tensor | int64 | ||
image | Image | (218, 178, 3) | uint8 | |
landmarks | FeaturesDict | |||
landmarks/lefteye_x | Tensor | int64 | ||
landmarks/lefteye_y | Tensor | int64 | ||
landmarks/leftmouth_x | Tensor | int64 | ||
landmarks/leftmouth_y | Tensor | int64 | ||
landmarks/nose_x | Tensor | int64 | ||
landmarks/nose_y | Tensor | int64 | ||
landmarks/righteye_x | Tensor | int64 | ||
landmarks/righteye_y | Tensor | int64 | ||
landmarks/rightmouth_x | Tensor | int64 | ||
landmarks/rightmouth_y | Tensor | int64 |
Supervised keys (See
as_supervised
doc):None
Figure (tfds.show_examples):
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{conf/iccv/LiuLWT15,
added-at = {2018-10-09T00:00:00.000+0200},
author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
biburl = {https://www.bibsonomy.org/bibtex/250e4959be61db325d2f02c1d8cd7bfbb/dblp},
booktitle = {ICCV},
crossref = {conf/iccv/2015},
ee = {http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.425},
interhash = {3f735aaa11957e73914bbe2ca9d5e702},
intrahash = {50e4959be61db325d2f02c1d8cd7bfbb},
isbn = {978-1-4673-8391-2},
keywords = {dblp},
pages = {3730-3738},
publisher = {IEEE Computer Society},
timestamp = {2018-10-11T11:43:28.000+0200},
title = {Deep Learning Face Attributes in the Wild.},
url = {http://dblp.uni-trier.de/db/conf/iccv/iccv2015.html#LiuLWT15},
year = 2015
}