AveragePrecisionRecall

class AveragePrecisionRecall

Abstract class to calculate average precision and recall for various vision tasks.

Parameters

max_dets: Union[List[int], Tuple[int]], default: [1, 10, 100]

Maximum number of detections per class.

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

Slices for small/medium/large buckets.

return_option: str, default: ‘ap’

ap: ap only, ar: ar only, None: all (not ignite compliant).

average: str, default: none

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

iou_range: Tuple[float, float, float], default: (0.5, 0.95, 10)

The closed range of the iou values (min, max (including), number of points)

__init__(*args, max_dets: Union[List[int], Tuple[int]] = (1, 10, 100), area_range: Tuple = (1024, 9216), return_option: Optional[str] = 'ap', average: str = 'none', iou_range: Tuple[float, float, float] = (0.5, 0.95, 10), **kwargs)

__new__(*args, **kwargs)

Attributes

AveragePrecisionRecall.required_output_keys

Methods

AveragePrecisionRecall.attach(engine, name)	Attaches current metric to provided engine.
AveragePrecisionRecall.calc_pairwise_ious(...)	Get a single result from group_class_detection_label and return a matrix of IoUs.
AveragePrecisionRecall.completed(engine, name)	Helper method to compute metric's value and put into the engine.
AveragePrecisionRecall.compute()	Compute metric value.
AveragePrecisionRecall.detach(engine[, usage])	Detaches current metric from the engine and no metric's computation is done during the run.
AveragePrecisionRecall.filter_res(res[, ...])	Get the value of a result by the filtering values.
AveragePrecisionRecall.get_classes_scores_at(res)	Get the mean value of the classes scores and the result values.
AveragePrecisionRecall.get_confidences(...)	Get detections object of single image and should return confidence for each detection.
AveragePrecisionRecall.get_detection_areas(...)	Get detection object of single image and should return area for each detection.
AveragePrecisionRecall.get_labels_areas(labels)	Get labels object of single image and should return area for each label.
AveragePrecisionRecall.group_class_detection_label(...)	Group detection and labels in dict of format {class_id: {'detected' [...], 'ground_truth': [...]}}.
AveragePrecisionRecall.is_attached(engine[, ...])	Checks if current metric is attached to provided engine.
AveragePrecisionRecall.iteration_completed(engine)	Helper method to update metric's computation.
AveragePrecisionRecall.load_state_dict(...)	Method replaces internal state of the class with provided state dict data.
AveragePrecisionRecall.reset()	Reset metric state.
AveragePrecisionRecall.started(engine)	Helper method to start data gathering for metric's computation.
AveragePrecisionRecall.state_dict()	Method returns state dict with attributes of the metric specified in its _state_dict_all_req_keys attribute.
AveragePrecisionRecall.update(output)	Update metric with batch of samples.

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ObjectDetectionAveragePrecision.update

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AveragePrecisionRecall.required_output_keys