| Tutorial, HALCON

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

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.

Screenshot deep-learning-based object detection train a model

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Watch our other videos about HALCON's Deep-Learning-Based Object Detection:

1. Introduction & Preparation of the Dataset

3. Evaluate the Trained Model

4. Apply the Model (Inference)