MeanAveragePrecisionReport

class MeanAveragePrecisionReport

Summarize mean average precision metrics on a dataset and model per IoU and bounding box area.

Parameters

area_range: tuple, default: (32**2, 96**2)

Slices for small/medium/large buckets.

__init__(area_range: Tuple = (1024, 9216), **kwargs)

__new__(*args, **kwargs)

Methods

MeanAveragePrecisionReport.add_condition(...)	Add new condition function to the check.
MeanAveragePrecisionReport.add_condition_average_mean_average_precision_not_less_than([...])	Add condition - average mAP for IoU values between 0.5 to 0.9 in all areas is not less than given score.
MeanAveragePrecisionReport.add_condition_mean_average_precision_not_less_than(...)	Add condition - mAP scores for in different area thresholds is not less than given score.
MeanAveragePrecisionReport.clean_conditions()	Remove all conditions from this check instance.
MeanAveragePrecisionReport.compute(context, ...)	Compute the metric result using the ignite metrics compute method and create display.
MeanAveragePrecisionReport.conditions_decision(result)	Run conditions on given result.
MeanAveragePrecisionReport.finalize_check_result(...)	Finalize the check result by adding the check instance and processing the conditions.
MeanAveragePrecisionReport.initialize_run(context)	Initialize run by asserting task type and initializing metric.
MeanAveragePrecisionReport.metadata([...])	Return check metadata.
MeanAveragePrecisionReport.name()	Name of class in split camel case.
MeanAveragePrecisionReport.params([...])	Return parameters to show when printing the check.
MeanAveragePrecisionReport.remove_condition(index)	Remove given condition by index.
MeanAveragePrecisionReport.run(dataset[, ...])	Run check.
MeanAveragePrecisionReport.update(context, ...)	Update the metrics by passing the batch to ignite metric update method.

Examples

Mean Average Precision Report

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ImageSegmentPerformance.update

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MeanAveragePrecisionReport.add_condition