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Datasets:
keremberke
/
indoor-scene-classification

Tasks:
Image Classification
Modalities:
Image
Text
Size:
10K - 100K
Tags:
roboflow
roboflow2huggingface
Retail
Pest Control
Benchmark
Libraries:
Datasets
Croissant
Dataset card Viewer Files Community
indoor-scene-classification
File size: 4,802 Bytes 
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import os

import datasets
from datasets.tasks import ImageClassification


_HOMEPAGE = "https://universe.roboflow.com/popular-benchmarks/mit-indoor-scene-recognition/dataset/5"
_LICENSE = "MIT"
_CITATION = """\

"""
_CATEGORIES = ['meeting_room', 'cloister', 'stairscase', 'restaurant', 'hairsalon', 'children_room', 'dining_room', 'lobby', 'museum', 'laundromat', 'computerroom', 'grocerystore', 'hospitalroom', 'buffet', 'office', 'warehouse', 'garage', 'bookstore', 'florist', 'locker_room', 'inside_bus', 'subway', 'fastfood_restaurant', 'auditorium', 'studiomusic', 'airport_inside', 'pantry', 'restaurant_kitchen', 'casino', 'movietheater', 'kitchen', 'waitingroom', 'artstudio', 'toystore', 'kindergarden', 'trainstation', 'bedroom', 'mall', 'corridor', 'bar', 'classroom', 'shoeshop', 'dentaloffice', 'videostore', 'laboratorywet', 'tv_studio', 'church_inside', 'operating_room', 'jewelleryshop', 'bathroom', 'clothingstore', 'closet', 'winecellar', 'livingroom', 'nursery', 'gameroom', 'inside_subway', 'deli', 'bakery', 'library', 'prisoncell', 'gym', 'concert_hall', 'greenhouse', 'elevator', 'poolinside', 'bowling']


class INDOORSCENECLASSIFICATIONConfig(datasets.BuilderConfig):
    """Builder Config for indoor-scene-classification"""

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

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


class INDOORSCENECLASSIFICATION(datasets.GeneratorBasedBuilder):
    """indoor-scene-classification image classification dataset"""

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