Delete loading script
Browse files- resisc45.py +0 -108
resisc45.py
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import datasets
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from datasets.data_files import DataFilesDict
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from datasets.packaged_modules.imagefolder.imagefolder import ImageFolder, ImageFolderConfig
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logger = datasets.logging.get_logger(__name__)
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class RESISC45(ImageFolder):
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R"""
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RESISC45 dataset for image classification.
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"""
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BUILDER_CONFIG_CLASS = ImageFolderConfig
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BUILDER_CONFIGS = [
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ImageFolderConfig(
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name="default",
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features=("images", "labels"),
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data_files=DataFilesDict(
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{
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split: f"data/{split}.zip"
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for split in ["train", "test"]
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+ ["contrast", "gaussian_noise", "impulse_noise", "jpeg_compression", "motion_blur", "pixelate", "spatter"]
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}
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),
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)
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]
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classnames = [
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"airplane",
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"airport",
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"baseball diamond",
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"basketball court",
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"beach",
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"bridge",
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"chaparral",
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"church",
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"circular farmland",
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"cloud",
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"commercial area",
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"dense residential",
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"desert",
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"forest",
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"freeway",
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"golf course",
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"ground track field",
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"harbor",
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"industrial area",
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"intersection",
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"island",
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"lake",
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"meadow",
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"medium residential",
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"mobile home park",
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"mountain",
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"overpass",
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"palace",
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"parking lot",
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"railway",
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"railway station",
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"rectangular farmland",
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"river",
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"roundabout",
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"runway",
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"sea ice",
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"ship",
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"snowberg",
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"sparse residential",
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"stadium",
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"storage tank",
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"tennis court",
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"terrace",
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"thermal power station",
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"wetland",
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]
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clip_templates = [
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lambda c: f"satellite imagery of {c}.",
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lambda c: f"aerial imagery of {c}.",
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lambda c: f"satellite photo of {c}.",
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lambda c: f"aerial photo of {c}.",
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lambda c: f"satellite view of {c}.",
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lambda c: f"aerial view of {c}.",
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lambda c: f"satellite imagery of a {c}.",
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lambda c: f"aerial imagery of a {c}.",
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lambda c: f"satellite photo of a {c}.",
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lambda c: f"aerial photo of a {c}.",
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lambda c: f"satellite view of a {c}.",
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lambda c: f"aerial view of a {c}.",
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lambda c: f"satellite imagery of the {c}.",
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lambda c: f"aerial imagery of the {c}.",
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lambda c: f"satellite photo of the {c}.",
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lambda c: f"aerial photo of the {c}.",
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lambda c: f"satellite view of the {c}.",
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lambda c: f"aerial view of the {c}.",
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]
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def _info(self):
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return datasets.DatasetInfo(
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description="RESISC45 dataset for image classification.",
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features=datasets.Features(
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{
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"image": datasets.Image(),
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"label": datasets.ClassLabel(names=self.classnames),
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}
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),
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supervised_keys=("image", "label"),
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task_templates=[datasets.ImageClassification(image_column="image", label_column="label")],
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)
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