OxfordIIITPet

class torchvision.datasets.OxfordIIITPet(root: Union[str, Path], split: str = 'trainval', target_types: Union[Sequence[str], str] = 'category', transforms: Optional[Callable] = None, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False)

Oxford-IIIT Pet Dataset.

Parameters:

• root (str or pathlib.Path) – Root directory of the dataset.

• split (string, optional) – The dataset split, supports "trainval" (default) or "test".

• target_types (string, sequence of strings, optional) –

  Types of target to use. Can be category (default) or segmentation. Can also be a list to output a tuple with all specified target types. The types represent:

  • category (int): Label for one of the 37 pet categories.

  • binary-category (int): Binary label for cat or dog.

  • segmentation (PIL image): Segmentation trimap of the image.

  If empty, None will be returned as target.

• 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 into root/oxford-iiit-pet. If dataset is already downloaded, it is not downloaded again.

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)