Detector
- Vojtěch Kováčik
The detector module allows you to find and possibly classify an object in the image.
Training
Annotations
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.
Feature size
During annotations it is important to find the ideal feature size. This value determines the size of the defects the model is able to reliably find. To find out more, open the following page Feature Size
Very small feature size on big defects is allowed but not ideal for performance and reliability.
Wrong feature size can make certain rectangles invalid. If such a thing occurs PEKAT is going to notify you before the training with possible solutions.
Include
In case your dataset contains empty images or images with no defects, it is possible to add them into training with the Include button.
This way the model learns how empty images look and therefore it should improve detection accuracy.
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 auto-annotations afterward, 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 initial training with less annotations.
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. Class management is the same as in classifier and the annotations change color based on the assigned class for better visibility.
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.