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)), }