Imagenette

class torchvision.datasets.Imagenette(root: ~typing.Union[str, ~pathlib.Path], split: str = 'train', size: str = 'full', download=False, transform: ~typing.Optional[~typing.Callable] = None, target_transform: ~typing.Optional[~typing.Callable] = None, loader: ~typing.Callable[[str], ~typing.Any] = <function default_loader>)

Imagenette image classification dataset.

Parameters:

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

• split (string, optional) – The dataset split. Supports "train" (default), and "val".

• size (string, optional) – The image size. Supports "full" (default), "320px", and "160px".

• download (bool, optional) – If True, downloads the dataset components and places them in root. Already downloaded archives are not downloaded again.

• transform (callable, optional) – A function/transform that takes in a PIL image or torch.Tensor, depends on the given loader, and returns a transformed version. E.g, transforms.RandomCrop

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

• loader – A function to load an image given its path. By default, it uses PIL as its image loader, but users could also pass in torchvision.io.decode_image for decoding image data into tensors directly.

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)