In this tutorial you will learn how to train a deep-learning-based Anomaly Detection model for your own application.
First, we will take a look at the use cases and advantages of anomaly detection. Then we’ll go through the workflow step by step. We start with default preprocessing as well as with application-specific preprocessing. Afterwards the training parameters will be explained and the trained model will be evaluated. Finally, we talk about the thresholds that define when a region or an image is classified as anomalous and visualize the inference results.
