Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
File size: 2,068 Bytes
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from pathlib import Path
from typing import List
import datasets
import pickle
logger = datasets.logging.get_logger(__name__)
class Fairface(datasets.GeneratorBasedBuilder):
_HOMEPAGE = "https://huggingface.co/datasets/nateraw/fairface/"
_URL = "https://huggingface.co/datasets/nateraw/fairface/resolve/main/"
_URLS = {
"train": _URL + "train.pt",
"dev": _URL + "val.pt",
}
_DESCRIPTION = "The Fairface dataset"
_CITATION = None
def _info(self):
return datasets.DatasetInfo(
description=self._DESCRIPTION,
features=datasets.Features(
{
'img_bytes': datasets.Value('binary'),
'age': datasets.features.ClassLabel(names=['0-2', '3-9', '10-19', '20-29', '30-39', '40-49', '50-59', '60-69', 'more than 70']),
"gender": datasets.features.ClassLabel(names=['Female', 'Male']),
'race': datasets.features.ClassLabel(names=['Black', 'East Asian', 'Indian', 'Latino_Hispanic', 'Middle Eastern', 'Southeast Asian', 'White'])
}
),
supervised_keys=('img_bytes', 'age'),
homepage=self._HOMEPAGE,
citation=self._CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
downloaded_files = dl_manager.download_and_extract(self._URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]})
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with Path(filepath).open('rb') as f:
examples = pickle.load(f)
for i, ex in enumerate(examples):
_id = ex.pop('_id')
yield _id, ex
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