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Deep learning classification with MVTec HALCON – 03 - Evaluate a trained classifier

In the last two video tutorials (part 1 and part 2), you learned how to prepare your data and perform the training of a deep-learning-based classifier with MVTec HALCON. In this video, we check out different methods that can help you evaluate this trained classifier, like confusion matrices and heatmaps. Furthermore, we use a procedure that can display, for example, all falsely classified images, which can help you evaluate limitations of your classifier. Lastly, different evaluation measures can be computed, like the F-score, the Top-K-error, precision, and recall.

You can download the HDevelop example used in this video below.