cats_vs_dogs.py

# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""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(),
                        }