The Anomaly Detector has two preset classes: OK and NG. Label views without defects or debris as OK and views with any anomalies as NG.

To label a view as a class:

1. Select the view in the view panel.

2. Click the corresponding label in the Labeling Labeling is the process of marking features or defects in images, or categorizing the images into classes. Labeling is crucial for training Deep Learning Lite and Deep Learning Standard tools because the labels serve as training material to guide the tools how to function correctly. Options.

You can label several images at the same time by selecting them.

Labeled views have a colored graphical mark in the upper left corner that shows the assigned class.

To remove an assigned label from a view, select the view then click Remove Label.

Note: If you already have a trained Anomaly Detector tool, use the bootstrap labeling Bootstrap labeling is a fast labeling method, which involves labeling a small sample of images, training the tool, and reviewing the results. Accept views with correct markings to convert them into labels, and replace incorrect markings with correct labels. method to speed up the labeling process. For more information, see Bootstrap Labeling.

You can train the Anomaly Detector tool even if you only label OK images as the tool learns the appearance of the good parts and it recognizes bad parts based on how much they deviate from that. However, for the best results, also include images in the training image set that show defects and you can label as NG.