SanitizeBoundingBoxes

class torchvision.transforms.v2.SanitizeBoundingBoxes(min_size: float = 1.0, labels_getter: Optional[Union[Callable[[Any], Optional[Tensor]], str]] = 'default')

Remove degenerate/invalid bounding boxes and their corresponding labels and masks.

This transform removes bounding boxes and their associated labels/masks that:

• are below a given min_size: by default this also removes degenerate boxes that have e.g. X2 <= X1.

• have any coordinate outside of their corresponding image. You may want to call ClampBoundingBoxes first to avoid undesired removals.

It is recommended to call it at the end of a pipeline, before passing the input to the models. It is critical to call this transform if RandomIoUCrop was called. If you want to be extra careful, you may call it after all transforms that may modify bounding boxes but once at the end should be enough in most cases.

Parameters:

• min_size (float, optional) –

• labels_getter (callable or str or None, optional) – indicates how to identify the labels in the input. By default, this will try to find a “labels” key in the input (case-insensitive), if the input is a dict or it is a tuple whose second element is a dict. This heuristic should work well with a lot of datasets, including the built-in torchvision datasets. It can also be a callable that takes the same input as the transform, and returns the labels.

Examples using SanitizeBoundingBoxes:

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

forward(*inputs: Any) → Any

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.