class torchvision.datasets.INaturalist(root: Union[str, Path], version: str = '2021_train', target_type: Union[List[str], str] = 'full', transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False)[source]

iNaturalist Dataset.

  • root (str or pathlib.Path) – Root directory of dataset where the image files are stored. This class does not require/use annotation files.

  • version (string, optional) – Which version of the dataset to download/use. One of ‘2017’, ‘2018’, ‘2019’, ‘2021_train’, ‘2021_train_mini’, ‘2021_valid’. Default: 2021_train.

  • target_type (string or list, optional) –

    Type of target to use, for 2021 versions, one of:

    • full: the full category (species)

    • kingdom: e.g. “Animalia”

    • phylum: e.g. “Arthropoda”

    • class: e.g. “Insecta”

    • order: e.g. “Coleoptera”

    • family: e.g. “Cleridae”

    • genus: e.g. “Trichodes”

    for 2017-2019 versions, one of:

    • full: the full (numeric) category

    • super: the super category, e.g. “Amphibians”

    Can also be a list to output a tuple with all specified target types. Defaults to full.

  • transform (callable, optional) – A function/transform that takes in a PIL image 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.

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

index (int) – Index


(image, target) where the type of target specified by target_type.

Return type:


category_name(category_type: str, category_id: int) str[source]
  • category_type (str) – one of “full”, “kingdom”, “phylum”, “class”, “order”, “family”, “genus” or “super”

  • category_id (int) – an index (class id) from this category


the name of the category


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources