Evaluation
- Monika Rehakova (Unlicensed)
Automatically decides whether the image is OK or NOK. This information is represented by a green or red stripe in the corner of each image in the image preview.
Evaluation can be used in the following modules:
If one of the modules in flow uses evaluation, this evaluation will affect the next modules. In case the next module uses evaluation as well it will overwrite the results of the previous evaluation.
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
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.
Evaluation
To activate this function, you need to tick the ‘Evaluation’ button above the generated sensitivity graph. When processing is active, it evaluates the image as valid (green stripe) or invalid (red stripe).
Threshold
The value determines the number of anomalies that are tolerated.
Detector, Classifier, Surface Detector, Measure
Images are evaluated based on set rules. If the image follows all of the rules it is considered OK, otherwise it is NOK.
If there are multiple classes, the rules can be set for a specific class or one of Any/Every/Together.
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.
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.
OCR
The evaluation in the OCR module is based on the comparison of the results found in the image with the specified regex. It is possible to add multiple regexes and then the result will only be true if all of them are found in the image.
