mednet.engine.detect.evaluator¶
Defines functionality for the evaluation of object detection predictions.
Functions
|
Create plots for all curves and score distributions in |
|
Tabulate summaries from multiple splits. |
|
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.