TpFpFn

class TpFpFn

Abstract class to calculate the TP, FP, FN and runs an evaluating function on the result.

Parameters

iou_thres: float, default: 0.5

IoU below this threshold will be ignored.

confidence_thres: float, default: 0.5

Confidence below this threshold will be ignored.

evaluating_function: Union[Callable, str], default: “recall”

will run on each class result i.e func(tp, fp, fn)

averaging_methodstr, default: ‘per_class’

Determines which averaging method to apply, possible values are: ‘per_class’: Return a np array with the scores for each class (sorted by class name). ‘binary’: Returns the score for the positive class. Should be used only in binary classification cases. ‘micro’: Returns the micro-averaged score. ‘macro’: Returns the mean of scores per class. ‘weighted’: Returns a weighted mean of scores based of the class size in y_true.

__init__(*args, iou_thres: float = 0.5, confidence_thres: float = 0.5, evaluating_function: Union[Callable, str] = 'recall', averaging_method='per_class', **kwargs)

__new__(*args, **kwargs)

Attributes

TpFpFn.required_output_keys

Methods

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

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ObjectDetectionTpFpFn.update

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TpFpFn.required_output_keys