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# DTD

class torchvision.datasets.DTD(root: str, split: str = 'train', partition: int = 1, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False)

  Describable Textures Dataset (DTD).

  Parameters:

    - root (string) – Root directory of the dataset.

    - split (string, optional) – The dataset split, supports `"train"` (default), `"val"`, or `"test"`.

    - partition (int, optional) –

      The dataset partition. Should be `1 <= partition <= 10`. Defaults to `1`.

      Note

      The partition only changes which split each image belongs to. Thus, regardless of the selected partition, combining all splits will result in all images.

    - transform (callable, optional) – A function/transform that takes in a 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.

    - download (bool, optional) – If True, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. Default is False.

  Special-members:

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

      index (int) – Index

    Returns:

      Sample and meta data, optionally transformed by the respective transforms.

    Return type:

      (Any)

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