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To verify the quality of a detector model you can use Confusion Matrix available in the Inspection tab:
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You can also compare the detected annotations with the annotated ones and more. You can learn more about the confusion matrix on the related page:
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Configuring Evaluation Settings
Trained detector model detects features on an image and assigns them rectangles. Each rectangle has 2 basic parameters:
Confidence
Position (together with height and width)
You can then filter out rectangles detected by the model using these parameters. To do this, you can use the two sliders on the right side of the window:
Confidence threshold slider - rectangles with confidence value lower than the threshold will be filtered out.
IoU (Intersection over Union) slider - if two or more rectangles overlap, the ones with lower confidence will be filtered out. The value specifies the percentage of overlap. For example, with value of 50, the filter will be applied to all rectangles that share at least 50% of their area with other rectangle.
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In conclusion - Confidence threshold is useful for filtering out rectangles with low confidence, IoU is useful for filtering out many overlapping rectangles |
Confidence - more info
Trained model assigns each feature it detects a confidence value. This value represents how certain the model is that the feature is similar to the features used in training.
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Correct confidence threshold settings can only improve the capabilities of the trained model. Correct model training is the cornerstone of successful detection. |
IoU - more info
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IoU or Intersection over Union is method used to calculate the overlap of rectangles.
As the name suggests, the IoU value is calculated by taking the intersection are (the shared area) of two rectangles and dividing that by the area of their union (total area covered by both rectangles together).