mednet.engine.detect.evaluator¶
Defines functionality for the evaluation of object detection predictions.
Functions
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Create plots for all curves and score distributions in |
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Tabulate summaries from multiple splits. |
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Run inference and calculates measures for multilabel object detection. |
- mednet.engine.detect.evaluator.run(predictions, binning, iou_threshold=None)[source]¶
Run inference and calculates measures for multilabel object detection.
- Parameters:
predictions (
Sequence[tuple[str,Sequence[tuple[Sequence[int],int]],Sequence[tuple[Sequence[int|float],int,float]]]]) – A list of predictions to consider for measurement.binning (
str|int) – The binning algorithm to use for computing the bin widths and distribution for histograms. Choose from algorithms supported bynumpy.histogram().iou_threshold (
float|None) – IOU threshold by which we consider successful object detection. If set toNone, then apply no thresholding.
- Return type:
- Returns:
A dictionary containing the performance summary on the specified threshold, general performance curves (under the key
curves), and score histograms (under the keyscore-histograms).
- mednet.engine.detect.evaluator.make_table(data, fmt)[source]¶
Tabulate summaries from multiple splits.
This function can properly tabulate the various summaries produced for all the splits in a prediction database.