# Statistical Results

The VisionPro Deep Learning application uses several statistical results that show the performance of a tool from different aspects. Use these statistics together to judge the performance of the tool.

## Statistical Results in the Database Overview

You can find the following statistical results in the Database Overview pane:

- The Confusion Matrix is a visual representation that compares your labels, which represents the ground truth Ground truth refers to the actual nature of the problem that is the target of a machine learning model, reflected by the relevant data sets associated with the use case in question. Supervised machine learning models are trained on labeled data that are considered for the model to identify patterns that predict those labels on new data., to the predictions of the tool.

- Recall is the percentage of labeled features or classes that the tool correctly identified.

- Precision is the percentage of detected features or classes that are correct, or in other words, match the labeled feature A feature is a visually distinguishable area in an image. Features typically represent something of interest for the application (a defect, an object, a particular component of an object). or class.

- 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 is between 0 to 1 with 1 being a better score.

- The Processing Time is the average processing time per image measured in the last processing session. The Processing Time is the sum of the processing time and the post-processing time.

## Outlier Score in the View Inspector

The outlier score shows how much a view A view of an image is a region of pixels in an image. Tool processing is limited to the pixels within the view. You can manually specify a view, or you can use the results of an upstream tool to generate a view. deviates from the other views in the training Training is the process that your tool, which is a neural network, is learning about the features (pixels) based on the labels you made. For example, a tool will learn the defect/normal pixels in each image based on the defect/normal labels you drew. The goal of the tool Training is learning enough to give the correct inspection results of whether an unseen image is defective or not. The key to training is to ensure that you include all possible variations within your training set, and that your images are accurately labeled. Training times vary by the application, tool setup and the GPU in the PC being used to train the network. set. A high outlier score can indicate that an anomaly happened on the production like, for example, the lighting conditions changed.

Standard type Green Classify tools support the outlier score statistic, which you can enable with Outlier Score processing parameter.

If you enable this parameter, the outlier score is included in the view browser list.

## Model Match Score in the View Inspector

The model A specific spatial arrangement of a set of features (Blue Locate and Blue Read tools only.) During a post-processing step, the Blue Locate and Blue Read tools can fit all of the features detected in an image to the models defined for the tool. The overall pose and identity of the model is then returned. match score is called Score in the View Inspector. This metric is unique to Blue Locate and Blue Read tools that are trained using a model. The Score is a combination of the following metrics:

- The average score of the features in the model.
- The geometry score based on the geometrical arrangement of the features in the model.

Note: The layout model score of the Blue Locate tool is the average score of the features in the model, it does not include a geometry score.

## Validation Loss Score in the Loss Inspector

The Loss The Loss refers to validation loss, which is a metric that shows how a tool performs on the validation set. Loss can have a value between 0 and 1. The VisionPro Deep Learning application calculates the Loss based on the errors the tool makes when processing the images in the validation set. During training, you can check the Loss in real-time using the Loss Inspector. refers to validation Validation refers to a process during the training of a tool that helps to evaluate performance. Validation is like a mock exam that the tool takes during the training phase, separate from the final test. For example, validation helps you to recognize overfitting and avoid wasting time when training a tool. If you recognize that the tool is overfitting, you can stop the training early. loss, which is a metric that shows how a tool performs on the validation set The validation set is a set of images reserved for validation. The validation set is separate from the training set and test set. Since the validation set is separate, it allows you to evaluate how well the tool performs when using unseen images. The VisionPro Deep Learning application calculates a special metric, the Loss. The Loss is based on the performance of the tool on the validation set.. Loss can have a value between 0 and 1. The VisionPro Deep Learning application calculates the Loss based on the errors the tool makes when processing the images in the validation set. During training, you can check the Loss in real-time using the Loss Inspector.

Note: This metric is unique to Standard type Green Classify and Red Analyze tools.