If the model is an image segmentation model, Anomalies contains a list of
anomaly types found in the image. There is one entry for each type of anomaly found (even
if multiple instances of an anomaly type exist on the image). The first element in the list
is always an anomaly type representing the image background ('background') and shouldn't be
considered an anomaly. Amazon Lookout for Vision automatically add the background anomaly type to the
response, and you don't need to declare a background anomaly type in your dataset.
If the list has one entry ('background'), no anomalies were found on the image.
An image classification model doesn't return an Anomalies list.
If the model is an image segmentation model, AnomalyMask contains pixel masks that covers all anomaly types found on the image.
Each anomaly type has a different mask color. To map a color to an anomaly type, see the color field
of the PixelAnomaly object.
An image classification model doesn't return an Anomalies list.
The confidence that Lookout for Vision has in the accuracy of the classification in IsAnomalous.
True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.
The source of the image that was analyzed. direct means that the
images was supplied from the local computer. No other values are supported.
The prediction results from a call to DetectAnomalies.
DetectAnomalyResultincludes classification information for the prediction (IsAnomalousandConfidence). If the model you use is an image segementation model,DetectAnomalyResultalso includes segmentation information (AnomaliesandAnomalyMask). Classification information is calculated separately from segmentation information and you shouldn't assume a relationship between them.