Datasets:

keremberke
/

indoor-scene-classification

	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)),
	}