You can refine 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. or the training parameters to improve the accuracy of the predictions. The filters and the confusion matrix can help you find the images where the labels and predictions do not match. After filtering, the view panel on the left-hand side only displays the filtered images and you can view them to see where the model makes different predictions from the labels.

For more information, see Process and Metrics and Statistics

To filter for images that contain mismatches, open the Mismatches filter dropdown on the left-hand side of the UI and select one of the following options:

- Unmatched Labels: This option filters for images where the model makes predictions for features that you did not label.

- Unmatched Predictions: This option filters for images where the prediction does not match your labeling.

- All Mismatches: This option filters for unmatched labels and unmatched predictions.

To find the images with mismatched labels or predictions based on classes, click on the number in the confusion matrix that represents a mismatched label or prediction. For more information, see Metrics. For example, in the following image, you can click the number in the Not predicted column of the cables row to find the image where a cable is labeled but the model does not make a prediction:

When you review the images, you can turn off the labels and the predictions in the overlay with the checkboxes under the image view:

This helps with visual clarity when labels and predictions overlap.