LFWPeople — Torchvision 0.22 documentation (original) (raw)

class torchvision.datasets.LFWPeople(root: str, split: str = '10fold', image_set: str = 'funneled', transform: ~typing.Optional[~typing.Callable] = None, target_transform: ~typing.Optional[~typing.Callable] = None, download: bool = False, loader: ~typing.Callable[[str], ~typing.Any] = <function default_loader>)[source]¶

LFW Dataset.

Parameters:

• root (str or pathlib.Path) – Root directory of dataset where directorylfw-py exists or will be saved to if download is set to True.
• split (string , optional) – The image split to use. Can be one of train, test,10fold (default).
• image_set (str, optional) – Type of image funneling to use, original, funneled ordeepfunneled. Defaults to funneled.
• 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.
• 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.
• loader (callable , optional) – A function to load an image given its path. By default, it uses PIL as its image loader, but users could also pass intorchvision.io.decode_image for decoding image data into tensors directly.

Special-members:

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

Parameters:

index (int) – Index

Returns:

Tuple (image, target) where target is the identity of the person.

Return type:

tuple