Detector

The detector module allows you to find and possibly classify an object in the image.

 

Training

Annotations

First, annotate defects in the image - the size of the annotation should be just enough to fit the whole defect without too much excess background (especially when the background is very variable). You can draw rectangles by hand or you can double-click with the left or right mouse and the rectangle will be added automatically.

To delete an annotation, you can press ‘Delete' or 'Backspace’ when the annotation is selected. If ‘Classify’ is enabled, you can assign a class to each annotated defect - similarly as in the Classifier module. Then you can start the training.

 

Auto-annotations

To speed up the annotation process, there is a possibility to train a model on a small number of annotations first and then use the predictions of this model to quickly make more annotations for further training.

If we select a trained model from a list of models and then go to the training tab, we can see predictions of that selected model on our images. They are marked with red rectangles with percentages.

 

Clicking Set annotations will automatically set annotations for all the detected rectangles in the selected image.

 

It is possible to edit these annotations afterward if we want to make them more precise. We can also add more annotations, delete them if some of them are wrong, or change their classes if Classify is enabled, the same as with normal annotations.

After auto-annotating enough images, we can start another training, which should offer better results than the one we made with just a few annotations before.

Classify

This adds the option to classify the object into one of the classes. If the classification is enabled, each marked object needs a class to be assigned to it.

For more details visit Classifier. This can also be achieved by combining Detector and Classifier as separate modules as described in the video below. However, classifying objects directly in the Detector is easier and faster.