Source code for

@author: Junguang Jiang

from torchvision.datasets.folder import ImageFolder
import random

[docs]class Resisc45(ImageFolder): """`Resisc45 <>`_ dataset \ is a scene classification task from remote sensing images. There are 45 classes, \ containing 700 images each, including tennis court, ship, island, lake, \ parking lot, sparse residential, or stadium. \ The image size is RGB 256x256 pixels. .. note:: You need to download the source data manually into `root` directory. Args: root (str): Root directory of dataset split (str, optional): The dataset split, supports ``train``, or ``test``. transform (callable, optional): A function/transform that takes in an PIL image and returns a \ transformed version. E.g, :class:`torchvision.transforms.RandomCrop`. target_transform (callable, optional): A function/transform that takes in the target and transforms it. """ def __init__(self, root, split='train', download=False, **kwargs): super(Resisc45, self).__init__(root, **kwargs) random.seed(0) random.shuffle(self.samples) if split == 'train': self.samples = self.samples[:25200] else: self.samples = self.samples[25200:] @property def num_classes(self) -> int: """Number of classes""" return len(self.classes)


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