Food101

class torchvision.datasets.Food101(root: ~typing.Union[str, ~pathlib.Path], split: str = 'train', transform: ~typing.Optional[~typing.Callable] = None, target_transform: ~typing.Optional[~typing.Callable] = None, download: bool = False, loader: ~typing.Callable[[~typing.Union[str, ~pathlib.Path]], ~typing.Any] = <function default_loader>)

The Food-101 Data Set.

The Food-101 is a challenging data set of 101 food categories with 101,000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.

Parameters:

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

• split (string, optional) – The dataset split, supports "train" (default) and "test".

• 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. Default is 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.

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)