Flowers102

class torchvision.datasets.Flowers102(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>)

Oxford 102 Flower Dataset.

Warning

This class needs scipy to load target files from .mat format.

Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers were chosen to be flowers commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images.

The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category, and several very similar categories.

Parameters:

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

• split (string, optional) – The dataset split, supports "train" (default), "val", or "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.

• 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)