# Database Overview of Blue Locate and Blue Read

After you train the tool, you can find statistics about the performance of the tool in the Database Overview pane.

Note: The Blue Locate and Blue Read tools use the same metrics to calculate statistical results.

Number | Description
1 | The Confusion Matrix is a table that compares the predictions of the tool with your labeling Labeling is the process of annotating an image with "ground truth". Depending on the tool that you are using, labeling can take different forms. You label an image set for two reasons: to provide the information needed to train the tool and to allow you to measure and validate the performance of the trained tool against the ground truth.. Each row in the matrix shows the number of features you labeled, which is 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.. Each column shows the number of features the tool has predicted, which are the markings that the tool returns after 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.. The color of a cell represents the ratio of the predicted features (the value of a cell) to the total number of labeled features (the total value of the row). A darker color means a higher ratio and a lighter color means a lower ratio. For example, the color of the cell at the position (2,2) is brown because the tool predicted 127 features shaft, which is 90.7% (127/140) of the features labeled as shaft. A diagonal line of dark cells in the Confusion Matrix show that performance is good.
Good Performance | Poor Performance
Note: If you click one of the colored cells in the Confusion Matrix, the View Browser displays corresponding views.
2 | The Count option allows you to select whether you want to see the statistical results from all views, or only from the untrained views. All: See the statistics calculated from both the training set and the test set. Untrained: See the statistics calculated from the test set. These are the views that are not included in the training set.
3 | Detailed statistics about each 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). class.
Column | Description
Feature | Shows the name of the feature class.
Found | Shows the number of features the tool identified as this class when processing all views.
Train | Shows the number of features in this feature class included in the training set.
Labeled | Shows the number of features in this feature class included in all views.
Recall | Recall is the percentage of labeled features or classes that the tool correctly identified.
Precision | The Precision is the percentage of detected features or classes that match the labeled feature or class.
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 is between 0 to 1 with 1 being a better score.
When using models, the Database Overview returns statistical results for each 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., not just individual features. The statistical results of the models are displayed in a similar table format because the feature statistics and the categories are the same. A found model is a correct match (correctly marked model) if there is a labeled model in approximately the same position and if this found model has features that are close to the corresponding labeled features. The VisionPro Deep Learning application calculates the Precision, Recall, and F-Score of the model on this ground. If 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. has multiple models, the application calculates all model metrics by each model separately.