HALCON's Deep-Learning-Based Object Detection 2: Train a model

In the second part of this tutorial series on HALCON 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. 0:40 – The learning rate hyperparameter 1:35 – More hyperparameters: epochs, batch size, … 2:55 – Visualization of the training progress In this video, HALCON 19.11 is used. www.mvtec.com www.halcon.com