CocoDetection

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

MS Coco Detection Dataset.

It requires the COCO API to be installed.

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:

Getting started with transforms v2

Getting started with transforms v2

Transforms v2: End-to-end object detection/segmentation example

Transforms v2: End-to-end object detection/segmentation example

Special-members:

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

Parameters:

index (int) – Index

Returns:

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

Return type:

(Any)