You can use the classifier block to categorize each inspection region from the ROI block into different categories, perform presence-absence checks, defect detection, assembly verification, etc,
Click on the Edit option under the Classifier Block
For each Inspection Type you are creating under each recipe, repeat the following process until you have set up all classes:
Select Edit next to the Inspection Type and a new class will be generated
Select the Capture option from the main page, and then select capture again once the live image has been activated
Click on +Add New Class to generate all of the chosen class designations for each Inspection Type until completed**:**
Select the class type e.g., Good under Letter Verification and click on the respective ROIs to set the appropriate class. To change the ROI to “Bad” —> use your cursor to double click within the selected ROI and this will change the designation
Once you have at least five representations for each classes for each Inspection Type, click on the Train Classification Model and the system will generate your fully trained AI model.
You should ensure that the features that differentiate the inspection region between multiple classes are clearly visible on the image to be able to train a performant classifier.
To create a highly performant classifier, you should collect data with these principles in mind.
Diverse - You should collect images under conditions that represent all the different variations you expect to see in production. Changing ambient lighting, placing objects in different places in the field of view, etc.
Label Consistently - You should ensure that all images are labeled correctly and consistently. You can check this through the
View All ROIs functionality.
Large in Number - The more images (diverse) you provide the classifier to learn from, the better it can learn.
Example of a good setup with consistent labelling and diverse images.
You should try and create as balanced a dataset as possible. Avoid situations where you have > 10x images in one class than the other.
Example of a Balanced Dataset
View All ROIsto ensure that all images have been labelled correctly and consistently.