After the Label step, proceed to the Train step to train the model.

1. Only one Training Mode is available for the Anomaly Detector:

   Deep Learning Lite - Few Sample (Default): Optimized for standardized objects. Requires only a few images to train.

2. Adjust the training parameters as needed:

   - Resize Parameters

     Feature Size specifies the typical feature diameter in pixels. Adjust this setting to achieve a more accurate result by ignoring smaller image features than the specified Feature Size. The Feature Size approximates the size of a typical feature that is needed for a good classification. For example, If the Feature Size is set to 16 pixels, any image feature smaller than 16 pixels is ignored for anomaly detection.

     Feature Size strongly influences processing time. A Feature Size of 100 is 100 times faster than a size of 10, while a Feature Size less than 8 usually does not yield good results. When setting the Feature Size, consider the equation in regards to processing time (Ptime):

     Ptime ~ F2

3. To auto-assign roles to images, click Auto-Assign Roles. A pop-up window provides you with options for how to assign roles:

   See the Label section for more information.