CelebA
E431002
CelebA is a large-scale face attributes dataset widely used in computer vision research for tasks like facial recognition, attribute prediction, and generative modeling.
All labels observed (2)
Statements (72)
| Predicate | Object |
|---|---|
| instanceOf |
benchmark dataset
ⓘ
face attributes dataset ⓘ image dataset ⓘ |
| contains |
celebrity face images
ⓘ
face images ⓘ |
| domain | computer vision ⓘ |
| hasApproximateNumberOfImages | 200000 ⓘ |
| hasAttribute |
5 o Clock Shadow
ⓘ
Arched Eyebrows ⓘ Attractive ⓘ Bags Under Eyes ⓘ Bald ⓘ Bangs ⓘ Big Lips ⓘ Big Nose ⓘ Black Hair ⓘ Blond Hair ⓘ Brown Hair ⓘ Bushy Eyebrows ⓘ Chubby ⓘ Double Chin ⓘ Eyeglasses ⓘ Goatee ⓘ Gray Hair ⓘ Heavy Makeup ⓘ High Cheekbones ⓘ Male ⓘ Mouth Slightly Open ⓘ Mustache ⓘ Narrow Eyes ⓘ No Beard ⓘ No Eyewear ⓘ Oval Face ⓘ Pale Skin ⓘ Pointy Nose ⓘ Receding Hairline ⓘ Rosy Cheeks ⓘ Sideburns ⓘ Smiling ⓘ Straight Hair ⓘ Wavy Hair ⓘ Wearing Earrings ⓘ Wearing Hat ⓘ Wearing Lipstick ⓘ Wearing Necklace ⓘ Wearing Necktie ⓘ Young ⓘ Young vs Old ⓘ |
| hasAttributeType | binary facial attributes ⓘ |
| hasDataSplit |
test set
ⓘ
training set ⓘ validation set ⓘ |
| hasImageResolution | 178x218 ⓘ |
| hasLicense | research only ⓘ |
| hasNumberOfAttributes | 40 ⓘ |
| hasNumberOfIdentities | 10000 ⓘ |
| hasProvider | The Chinese University of Hong Kong NERFINISHED ⓘ |
| isPopularBenchmarkFor |
face attribute classification
ⓘ
face editing ⓘ identity-preserving generation ⓘ |
| relatedDataset |
CelebA-HQ
NERFINISHED
ⓘ
CelebAMask-HQ NERFINISHED ⓘ |
| usedFor |
GAN training
ⓘ
attribute-conditioned image generation ⓘ domain adaptation in vision ⓘ face detection research ⓘ face recognition research ⓘ facial attribute prediction ⓘ fairness analysis in face recognition ⓘ generative modeling of faces ⓘ image-to-image translation ⓘ representation learning ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: CelebA Description of subject: CelebA is a large-scale face attributes dataset widely used in computer vision research for tasks like facial recognition, attribute prediction, and generative modeling.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
torchvision
this entity surface form:
CelebA-HQ