metadata
dataset_info:
features:
- name: image
dtype: image
- name: 5_o_Clock_Shadow
dtype: int64
- name: Arched_Eyebrows
dtype: int64
- name: Attractive
dtype: int64
- name: Bags_Under_Eyes
dtype: int64
- name: Bald
dtype: int64
- name: Bangs
dtype: int64
- name: Big_Lips
dtype: int64
- name: Big_Nose
dtype: int64
- name: Black_Hair
dtype: int64
- name: Blond_Hair
dtype: int64
- name: Blurry
dtype: int64
- name: Brown_Hair
dtype: int64
- name: Bushy_Eyebrows
dtype: int64
- name: Chubby
dtype: int64
- name: Double_Chin
dtype: int64
- name: Eyeglasses
dtype: int64
- name: Goatee
dtype: int64
- name: Gray_Hair
dtype: int64
- name: Heavy_Makeup
dtype: int64
- name: High_Cheekbones
dtype: int64
- name: Male
dtype: int64
- name: Mouth_Slightly_Open
dtype: int64
- name: Mustache
dtype: int64
- name: Narrow_Eyes
dtype: int64
- name: No_Beard
dtype: int64
- name: Oval_Face
dtype: int64
- name: Pale_Skin
dtype: int64
- name: Pointy_Nose
dtype: int64
- name: Receding_Hairline
dtype: int64
- name: Rosy_Cheeks
dtype: int64
- name: Sideburns
dtype: int64
- name: Smiling
dtype: int64
- name: Straight_Hair
dtype: int64
- name: Wavy_Hair
dtype: int64
- name: Wearing_Earrings
dtype: int64
- name: Wearing_Hat
dtype: int64
- name: Wearing_Lipstick
dtype: int64
- name: Wearing_Necklace
dtype: int64
- name: Wearing_Necktie
dtype: int64
- name: Young
dtype: int64
- name: prompt_string
dtype: string
splits:
- name: train
num_bytes: 1209620544.21
num_examples: 162770
- name: validation
num_bytes: 148733684.292
num_examples: 19962
- name: test
num_bytes: 149605611.301
num_examples: 19867
download_size: 1424899346
dataset_size: 1507959839.803
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: mit
task_categories:
- feature-extraction
- image-classification
- image-feature-extraction
size_categories:
- 100K<n<1M
CelebA-128x128
CelebA with attrs at 128x128 resolution.
Dataset Information
The attributes are binary attributes. The dataset is already split into train/test/validation sets.
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}
}