shoe-classification.py

import os

import datasets
from datasets.tasks import ImageClassification


_HOMEPAGE = "https://universe.roboflow.com/popular-benchmarks/nike-adidas-and-converse-shoes-classification/dataset/4"
_LICENSE = "Public Domain"
_CITATION = """\

"""
_CATEGORIES = ['converse', 'adidas', 'nike']


class SHOECLASSIFICATIONConfig(datasets.BuilderConfig):
    """Builder Config for shoe-classification"""

    def __init__(self, data_urls, **kwargs):
        """
        BuilderConfig for shoe-classification.

        Args:
          data_urls: `dict`, name to url to download the zip file from.
          **kwargs: keyword arguments forwarded to super.
        """
        super(SHOECLASSIFICATIONConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.data_urls = data_urls


class SHOECLASSIFICATION(datasets.GeneratorBasedBuilder):
    """shoe-classification image classification dataset"""

    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        SHOECLASSIFICATIONConfig(
            name="full",
            description="Full version of shoe-classification dataset.",
            data_urls={
    "train": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/train.zip",
    "validation": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/valid.zip",
    "test": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/test.zip",
}
,
        ),
        SHOECLASSIFICATIONConfig(
            name="mini",
            description="Mini version of shoe-classification dataset.",
            data_urls={
                "train": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/valid-mini.zip",
                "validation": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/valid-mini.zip",
                "test": "https://huggingface.co/datasets/keremberke/shoe-classification/resolve/main/data/valid-mini.zip",
            },
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "image_file_path": datasets.Value("string"),
                    "image": datasets.Image(),
                    "labels": datasets.features.ClassLabel(names=_CATEGORIES),
                }
            ),
            supervised_keys=("image", "labels"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
            task_templates=[ImageClassification(image_column="image", label_column="labels")],
        )

    def _split_generators(self, dl_manager):
        data_files = dl_manager.download_and_extract(self.config.data_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "files": dl_manager.iter_files([data_files["train"]]),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "files": dl_manager.iter_files([data_files["validation"]]),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "files": dl_manager.iter_files([data_files["test"]]),
                },
            ),
]

    def _generate_examples(self, files):
        for i, path in enumerate(files):
            file_name = os.path.basename(path)
            if file_name.endswith((".jpg", ".png", ".jpeg", ".bmp", ".tif", ".tiff")):
                yield i, {
                    "image_file_path": path,
                    "image": path,
                    "labels": os.path.basename(os.path.dirname(path)),
                }