Review the statistics and metrics that OneVision returns in the right-side panel:

- If the performance of the tool is optimal based on the returned statistics, you are ready with the training.

- If the performance is not satisfying, you can adjust your 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., refine the parameter settings, or provide more labeled data and retrain the tool. For more instructions, see Example of verifying and refining with an initially small train set.

Statistics

Information about processing time and the confidence score of the predicted classes.

Statistics help identify anomalies, for example, if any images fall outside the expected count range, selecting the given bar shows the incorrectly processed images and you can manually correct them.

Metrics

Section | Description
Confusion Matrix | The confusion matrix is a visual representation of the labels matched against the predictions of the tool. The horizontal dimension shows the predictions of the tool. The vertical dimension shows the actual labels you assigned. If the predictions match the labels, the results are good. A darker red color means a higher ratio and a lighter color means a lower ratio. A diagonal line of dark cells in the confusion matrix shows that performance is good. The color of a cell represents the ratio of the predicted views (the value of a cell) to the total number of labeled views (the total value of the row). Click the fields in the Confusion Matrix to filter for the given category of views.
Results Statistics | The table lists the classes and information about each one in the corresponding columns.
Column | Description
Predicted | Shows the total number of views the tool classified as a certain class.
Labeled | Shows the number of the views labeled as a certain class.
Trained | Shows the number of views labeled as a certain class and included in the training set.
Recall | Recall is the percentage of the labeled classes that the tool correctly identifies. OneVision calculates Recall by the fraction of classified views labeled as class i that are correctly classified as class i: For example, a Recall of 90% for the class i means that the neural network model Each AI tool is a neural network model. A neural network model mimics the way biological neurons work in the human brain. The neural network model consists of interconnected layers of artifical neurons, called nodes, and they have multiple layers. Neural network models excel at tasks like image classification and pattern recognition. correctly classifies a view of class i 9 out of 10 times.
Precision | Precision is the percentage of detected classes that match the labeled class. OneVision calculates Precision as the percentage of correctly classified views predicted as class i: For example, a Precision of 90% for the class i means that the neural network model confuses an view of class i with a different class 1 out of 10 times.
F-Score | The F-Score represents the overall accuracy of the tool. The F-Score is the harmonic mean of the Recall and Precision scores, which provides a single metric that balances both Precision and Recall. The value of the F-score ranges from 0 to 1, with 1 representing the highest accuracy.

Selected View

Detailed information about the selected view only, including image data, the assigned label, and a breakdown of the predicted classes.

Note: Unclassified is only visible as a class on Metrics and Statistics if you have set different anomaly score threshold upper and lower limit numbers in the Process step. For more information on the threshold, see How to set and optimize the threshold.

For more information on interpreting results, see Metrics and Statistics.