This module classifies the detected objects into classes. Each object can be in one class only. This module can classify the whole image, objects detected in previous modules, or objects on in static positions.
Classifier types
...
When using this type of Classifier, you assign classes to objects which have already been detected in the previous modules (for example Detector, Surface Detector, Code, Anomaly...). The combination with Detector is described in the video below. In this video, the classification and detection process is split into two modules, but it is possible to perform this type of classification directly in the Detector and thereby speed the process up.
...
With this type of Classifier, you first need to draw the rectangles determining the positions on which the objects are supposed to be located and then the contents of those areas are being classified on each image. You only need to draw the rectangles on one image and they will be automatically copied on onto the other images. Then you assign classes to each of these rectangles on images to be used for training, depending on which object appeared on in that position. To delete a rectangle you can either use the trashcan icon in the top right corner or press 'Delete' when the rectangle is selected.
...
To assign a class to an object, click on that object. The minimum number of classes for training is 2. Not all objects need to have a class assigned. The number in brackets next to the class name indicates the number of objects in that class. To select the class faster, you can press a numeric key corresponding to the class number which is in square brackets.
Max size
For training, the classifier module must resize all images to the size of the biggest one, so that they all have the same size, with a maximum value of 800 pixels.
...
The ‘classNames’ adds an attribute into to the ‘detectedRectangles’ box. It inserts an associative field with a class description.
...
The model can optionally be optimized using Tensor RT for the specific GPU used when you check the TRT switch in the model list. The optimisation optimization improves inference times when processing images using this model.
...