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# FakeData

class torchvision.datasets.FakeData(size: int = 1000, image_size: Tuple[int, int, int] = (3, 224, 224), num_classes: int = 10, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, random_offset: int = 0)

  A fake dataset that returns randomly generated images and returns them as PIL images

  Parameters:

    - size (int, optional) – Size of the dataset. Default: 1000 images

    - image_size (tuple, optional) – Size if the returned images. Default: (3, 224, 224)

    - num_classes (int, optional) – Number of classes in the dataset. Default: 10

    - transform (callable, optional) – A function/transform that takes in an PIL image and returns a transformed version. E.g, `transforms.RandomCrop`

    - target_transform (callable, optional) – A function/transform that takes in the target and transforms it.

    - random_offset (int) – Offsets the index-based random seed used to generate each image. Default: 0

  Special-members:

  __getitem__(index: int) → Tuple[Any, Any]
    Parameters:

      index (int) – Index

    Returns:

      (image, target) where target is class_index of the target class.

    Return type:

      tuple

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