In the second part of this tutorial series on HALCON’s object detection, you will learn how to train a deep-learning-based object detection model with MVTec HALCON.
We will have a look at some hyperparameters that influence the training progress, like for example the learning rate. Additionally, we will learn how and when to augment your data. Lastly, we will have a look how to interpret the extensive training progress visualization. For example, we will examine the ‘mean average precision’, which is used to evaluate the performance of the model during training.
