OxfordIIITPet — Torchvision 0.22 documentation (original) (raw)

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)[source]¶

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) orsegmentation. 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.
• transforms (callable , optional) – A function/transform that takes input sample and its target as entry and returns a transformed version.
• download (bool, optional) – If True, downloads the dataset from the internet and puts it intoroot/oxford-iiit-pet. If dataset is already downloaded, it is not downloaded again.

Special-members:

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

Parameters:

index (int) – Index

Returns:

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

Return type:

(Any)