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If you want to see better how a trained model detects or classifies the defects, you can go back into labeling and annotate more images and press “RECALC STATISTICS“ button. This will update the confusion matrix with the data from the new annotations without the need to retrain the model.
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The “RECALC MATRIX“ function isn’t available in the Surface Detection and OCR modules. |
Anomaly Detector confusion matrix
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The detector confusion matrix works much like the classifier confusion matrix but it has one extra misdetections - when the model detects defect when there is none in annotations. If the classification is turned off, the confusion matrix is like matrix with one unnamed class.
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The detection is considered correct (it corresponds to one of the green cells in confusion matrix) if at least half of the detected rectangle is overlapping with annotated rectangle (and if you have classification turned on, the rectangles must also be of the same class as the annotation to be considered correct).
You can compare the annotated rectangles with the detected ones in the Inspection tab by having the Show Annotations button ticked on (or in the Labeling tab by switching the Active Annotations on).
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For more details on how the confusion matrix works you can read the module confusion matrix section.
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It looks like this and works just like detector confusion matrix, except that surface detector doesn’t provide the “RECALC MATRIX” function in the Labeling tab:
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OCR confusion matrix
OCR confusion matrix is very different from the rest of the modules. First, it’s accessed by clicking on the “MODEL STATISTICS” button:
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