MeanIoU

class MeanIoU

Metric that calculates the mean IoU metric for each class.

See more: https://en.wikipedia.org/wiki/Jaccard_index

Parameters

threshold: float, default: 0.5

prediction value per pixel above which the pixel is considered True.

smooth: float, default: 1e-3

smoothing factor to prevent division by zero when the mask is empty.

average: str, default: none

The method for averaging over the classes. If none, returns the result per class.

__init__(*args, threshold: float = 0.5, smooth=0.001, average: str = 'none', **kwargs)

__new__(*args, **kwargs)

Attributes

MeanIoU.required_output_keys

Methods

MeanIoU.attach(engine, name[, usage])	Attaches current metric to provided engine.
MeanIoU.completed(engine, name)	Helper method to compute metric's value and put into the engine.
MeanIoU.compute()	Compute metric value.
MeanIoU.detach(engine[, usage])	Detaches current metric from the engine and no metric's computation is done during the run.
MeanIoU.is_attached(engine[, usage])	Checks if current metric is attached to provided engine.
MeanIoU.iteration_completed(engine)	Helper method to update metric's computation.
MeanIoU.load_state_dict(state_dict)	Method replaces internal state of the class with provided state dict data.
MeanIoU.reset()	Reset metric state.
MeanIoU.started(engine)	Helper method to start data gathering for metric's computation.
MeanIoU.state_dict()	Method returns state dict with attributes of the metric specified in its _state_dict_all_req_keys attribute.
MeanIoU.update(output)	Update metric with batch of samples.

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MeanDice.update

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MeanIoU.required_output_keys