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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 a new rectangle with the same size as the last one you made will be added automatically.
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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.
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Filter invalid images will take you back to Labeling with the images with invalid annotations will be filtered in the list on the right side, and you can easily go through those images and fix the annotations.
Change feature size will set the feature size to the largest possible value that would allow using all of the annotations for training (or to size 32 as it is the smallest possible feature size).
Continue will proceed with the training without using the invalid annotations.
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.
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If we select a trained model from a list of models and then go to the training Labeling tab, we can see predictions of that selected model on our images. They are marked with red rectangles with percentages.
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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 (you can change class names and color in the Class Manager).
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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.
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