Datasets:
cats_vs_dogs

	# coding=utf-8
	# Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""The Microsoft Cats vs. Dogs dataset"""

	import os
	from typing import List

	import datasets
	from datasets.tasks import ImageClassification


	logger = datasets.logging.get_logger(__name__)

	_URL = "https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip"

	_HOMEPAGE = "https://www.microsoft.com/en-us/download/details.aspx?id=54765"

	_DESCRIPTION = "A large set of images of cats and dogs. There are 1738 corrupted images that are dropped."

	_CITATION = """\
	@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
	author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
	title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
	booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
	year = {2007},
	month = {October},
	publisher = {Association for Computing Machinery, Inc.},
	url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},
	edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
	}
	"""


	class CatsVsDogs(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"image": datasets.Image(),
	"labels": datasets.features.ClassLabel(names=["cat", "dog"]),
	}
	),
	supervised_keys=("image", "labels"),
	task_templates=[ImageClassification(image_column="image", label_column="labels")],
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
	images_path = os.path.join(dl_manager.download_and_extract(_URL), "PetImages")
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_files([images_path])}
	),
	]

	def _generate_examples(self, files):
	for i, file in enumerate(files):
	if os.path.basename(file).endswith(".jpg"):
	with open(file, "rb") as f:
	if b"JFIF" in f.peek(10):
	yield str(i), {
	"image": file,
	"labels": os.path.basename(os.path.dirname(file)).lower(),
	}