...
When you use parallelism, a boolean operator AND is used among the branches. That means if one of the branches has a NOK result, the overall result will be NOK as well.
...
Anomaly Detector
Evaluation The evaluation considers the number of anomalies and compares it with a threshold value. More information on calculating the threshold can be found here. If the values are bigger than the threshold, the image is evaluated as NOK, otherwise, it is considered OK.
...
Any - OK if the rule is true for at least one of the classes (e.g. rule “Any Count = 10” is true if Class1 count is 10 and Class2 count is 4).
Every - OK if the rule is true for each of the classes found in the image (e.g. rule “Every Count = 10” is true if Class1 count is 10 and Class2 count is 10).
Together - OK if the rule is true for all classes present in the image combined (e.g. rule “Together Count = 10” is true if Class1 count is 6 and Class2 count is 4).
Then it is possible to choose the type of evaluation based on the model: Count/Edge length.
Count - represents the number of detected rectangles.
Edge length - represents the length of the chosen rectangle/rectangles.
| Info |
|---|
If you set the rules for a trained model and then train another one, the rules are automatically copied to the new model, so you don’t need to set them again if no changes in evaluation are needed. |
...