Datasets:
Tasks:
Other
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
License:
File size: 3,530 Bytes
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# 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"""
from pathlib import Path
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_3367a.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):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image_file_path": datasets.Value("string"),
"labels": datasets.features.ClassLabel(names=["cat", "dog"]),
}
),
supervised_keys=("image_file_path", "labels"),
task_templates=[
ImageClassification(
image_file_path_column="image_file_path", label_column="labels", labels=["Cat", "Dog"]
)
],
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
images_path = Path(dl_manager.download_and_extract(_URL)) / "PetImages"
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"images_path": images_path}),
]
def _generate_examples(self, images_path):
logger.info("generating examples from = %s", images_path)
labels = self.info.features["labels"]
for i, filepath in enumerate(images_path.glob("**/*.jpg")):
with filepath.open("rb") as f:
if b"JFIF" not in f.peek(10):
filepath.unlink()
continue
yield str(i), {
"image_file_path": str(filepath),
"labels": labels.encode_example(filepath.parent.name.lower()),
}
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