Datasets:
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File size: 1,762 Bytes
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import pickle
from pathlib import Path
from typing import List
import datasets
logger = datasets.logging.get_logger(__name__)
_HOMEPAGE = "https://www.kaggle.com/datasets/msambare/fer2013"
_URL = "https://huggingface.co/datasets/Jeneral/fer2013/resolve/main/"
_URLS = {
"train": _URL + "train.pt",
}
_DESCRIPTION = "A large set of images of faces with seven emotional classes"
_CITATION = """\
@TECHREPORT{Fer2013 dataset,
author = {Prince Awuah Baffour},
title = {Facial Emotion Detection},
institution = {},
year = {2022}
}
"""
class fer2013(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"img_bytes": datasets.Value("binary"),
"labels": datasets.features.ClassLabel(names=["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]),
}
),
supervised_keys=("img_bytes", "labels"),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
]
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):
yield str(i), ex
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