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.. warning:
The LFW dataset is no longer available for automatic download. Please
download it manually and place it in the specified directory.
Args:
root (str or ``pathlib.Path``): Root directory of dataset where directory
``lfw-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`` or
``deepfunneled``. 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): NOT SUPPORTED ANYMORE, leave to False.
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 in
``torchvision.io.decode_image`` for decoding image data into tensors directly.
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index (int): Index
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.. warning:
The LFW dataset is no longer available for automatic download. Please
download it manually and place it in the specified directory.
Args:
root (str or ``pathlib.Path``): Root directory of dataset where directory
``lfw-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``. Defaults to ``10fold``.
image_set (str, optional): Type of image funneling to use, ``original``, ``funneled`` or
``deepfunneled``. Defaults to ``funneled``.
transform (callable, optional): A function/transform that takes in a PIL image
and returns a transformed version. E.g, ``transforms.RandomRotation``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
download (bool, optional): NOT SUPPORTED ANYMORE, leave to False.
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 in
``torchvision.io.decode_image`` for decoding image data into tensors directly.
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