CocoDetection — Torchvision 0.22 documentation (original) (raw)

class torchvision.datasets.CocoDetection(root: Union[str, Path], annFile: str, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None)[source]¶

MS Coco Detection Dataset.

It requires pycocotools to be installed, which could be installed via pip install pycocotools or conda install conda-forge::pycocotools.

Parameters:

• root (str or pathlib.Path) – Root directory where images are downloaded to.
• annFile (string) – Path to json annotation file.
• transform (callable , optional) – A function/transform that takes in a PIL image and returns a transformed version. E.g, transforms.PILToTensor
• target_transform (callable , optional) – A function/transform that takes in the target and transforms it.
• transforms (callable , optional) – A function/transform that takes input sample and its target as entry and returns a transformed version.

Examples using CocoDetection:

Special-members:

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

Parameters:

index (int) – Index

Returns:

Sample and meta data, optionally transformed by the respective transforms.

Return type:

(Any)