Shortcuts

Source code for tllib.vision.datasets.resisc45

"""
@author: Junguang Jiang
@contact: JiangJunguang1123@outlook.com
"""

from torchvision.datasets.folder import ImageFolder
import random


[docs]class Resisc45(ImageFolder): """`Resisc45 <http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html>`_ 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)

Docs

Access comprehensive documentation for Transfer Learning Library

View Docs

Tutorials

Get started for Transfer Learning Library

Get Started

Paper List

Get started for transfer learning

View Resources