# Unsupervised Mode – Scores

For Unsupervised mode, the overall score for the view is the maximum deviation for the view.

In this mode, the Red Analyze tool returns a deviation map indicating, for each pixel location in the input image, the degree to which this pixel represents a deviation from the expected value, based on the collection of image data used for training.

The actual value of the deviation is unitless, and is a function of the characteristics of the trained image data. Since every input image will generate a unique deviation value, it is not possible to normalize the deviation to a fixed range. Another way to think about this is that while the maximum possible probability for a defect pixel is 1.0, there is no maximum possible deviation for a pixel, and consequently no fixed bounds to which to normalize the deviation.

Note: In images that include objects or features on a background, a Red Analyze tool in Unsupervised mode will typically not be able to report missing objects of features as deviations. Because all of the “good” images provided for training include background regions, the tool will never report the presence of background as a deviation, even as the amount or proportion of background in the input image changes.