Datasets:
license:
- cc-by-4.0
size_categories:
- 10K<n<100K
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: fairface
language:
- en
pretty_name: FairFace
Dataset Card for FairFace
Usage
from io import BytesIO
from PIL import Image
import datasets
def bytes_to_pil(example_batch):
example_batch['img'] = [
Image.open(BytesIO(b)) for b in example_batch.pop('img_bytes')
]
return example_batch
ds = datasets.load_dataset('nateraw/fairface')
ds = ds.with_transform(bytes_to_pil)
Table of Contents
Dataset Description
- Repository: FairFace repository
- Paper: FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
Dataset Summary
Existing public face datasets are strongly biased toward Caucasian faces, and other races (e.g., Latino) are significantly underrepresented. This can lead to inconsistent model accuracy, limit the applicability of face analytic systems to non-White race groups, and adversely affect research findings based on such skewed data. To mitigate the race bias in these datasets, we construct a novel face image dataset, containing 108,501 images, with an emphasis of balanced race composition in the dataset. We define 7 race groups: White, Black, Indian, East Asian, Southeast Asian, Middle East, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups. Evaluations were performed on existing face attribute datasets as well as novel image datasets to measure generalization performance. We find that the model trained from our dataset is substantially more accurate on novel datasets and the accuracy is consistent between race and gender groups.
Dataset Structure
Data Fields
- img_bytes: Bytes representing an image
- age: Age of the person in the image
- gender: Gender of the person in the image
- race: Race of the person in the image
Data Instances
{
'age': 6,
'gender': 1,
'img_bytes': b'\\\\\\\\\\\\\\\\xff\\\\\\\\\\\\\\\\xd8...',
'race': 1
}