# Database Overview of Green Classify

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

Note: Results are only shown for labeled images that were not used 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.

.
Number | Description
1 | The Confusion Matrix is a visual representation of 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. versus the predictions of the tool. The horizontal dimension shows the predictions of the tool and the vertical dimension of the matrix shows the ground truth (actual labels you assigned). The Confusion Matrix of the Green Classify tool is a graphical representation of the Recall and Precision scores. If you click one of the colored cells in the Confusion Matrix, the corresponding views in are displayed in the View Browser. 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). A darker color means a higher ratio and a lighter color means a lower ratio. For example, the color of the cell at the index (1,1) is dark red because among the views whose label is Bauble the number of views classified as Bauble is 18 and it accounts for 75% (18/24) of the views whose label is Bauble. IA diagonal line of dark cells in the Confusion Matrix show that performance is good.
Good Performance | Poor Performance
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. In other words, the views that are not included in the training set.
3 | Detailed statistics about each class. The Recall, Precision, and the F-Score of the Green Classify tool are calculated per class based upon best_tag, which is the class that has the highest probability among all classes. The table lists the class and information about each in the corresponding columns.
Column | Description
Tag | Shows the name of the class.
Found | Shows the total number of views the tool classified as a certain class.
Train | Shows the number of views labeled as a certain class and included in the training set.
Labeled | Shows the number of the views labeled as a certain class.
n/c | Shows the number of views in the test set the tool cannot sort into any of the classes. When the tool inspects the 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., it assigns a score to each class. The score represents the probability that the view belongs to a certain class. If none the scores reach the minimum threshold, the tool cannot sort the view into a class.
Recall | Recall is the percentage of labeled features or classes that the tool correctly identified.
Precision | 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.
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. Note: The first line represents the F-Score for the entire image. The second line is the average F-Score for each class.