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"""The Microsoft Cats vs. Dogs dataset""" |
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from pathlib import Path |
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from typing import List |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_URL = "https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip" |
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_HOMEPAGE = "https://www.microsoft.com/en-us/download/details.aspx?id=54765" |
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_DESCRIPTION = "A large set of images of cats and dogs. There are 1738 corrupted images that are dropped." |
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_CITATION = """\ |
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@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization, |
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author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared}, |
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title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization}, |
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booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, |
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year = {2007}, |
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month = {October}, |
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publisher = {Association for Computing Machinery, Inc.}, |
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url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/}, |
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edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)}, |
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} |
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""" |
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class CatsVsDogs(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"image_file_path": datasets.Value("string"), |
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"labels": datasets.features.ClassLabel(names=["cat", "dog"]), |
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} |
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), |
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supervised_keys=("image_file_path", "labels"), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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images_path = Path(dl_manager.download_and_extract(_URL)) / "PetImages" |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"images_path": images_path}), |
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] |
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def _generate_examples(self, images_path): |
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logger.info("generating examples from = %s", images_path) |
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labels = self.info.features["labels"] |
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for i, filepath in enumerate(images_path.glob("**/*.jpg")): |
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with filepath.open("rb") as f: |
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if b"JFIF" not in f.peek(10): |
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filepath.unlink() |
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continue |
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yield str(i), { |
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"image_file_path": str(filepath), |
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"labels": labels.encode_example(filepath.parent.name.lower()), |
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} |
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