Viewing performance metrics

After training is complete, the console displays the performance metrics.

The Defect Detection App console shows the following performance metrics for the classifications made during testing:

• Precision

• Recall

• F1 score

The test results overview section shows you the total correct and incorrect predictions for images in the test dataset. You can also see the predicted and actual label assignments for individual images in the test dataset.

The following procedure shows how to get the performance metrics from a project's model list view.

To view performance metrics

1. If you're not on the project details page, do the following:

   1. Sign in to the Defect Detection App Console.

   2. In the top navigation pane, choose Projects.

   3. On the projects page, choose the project. The console opens the project details page.

2. From the Model versions tab,choose the model that you want to view.

3. On the model page, view the performance metrics in the model details panel. The panel displays the overall model metrics (precision, recall, F1 score) for the test images.

4. Note the following:

   The Test results overview section provides the results for each test image that Defect Detection App uses to evaluate the model. It includes the following:

   • The total number of correct (true positive) and incorrect (false negative) classification predictions (normal or anomaly) for all test images.

   • The classification prediction for each test image. If you see Prediction correct under an image, the predicted classification matches the actual classification for the image. Otherwise the model didn't correctly classify the image.

   • With an image segmentation model, you see anomaly labels that the model assigned to the image and masks on the image that match the colors of the anomaly labels.