# Markings Returned by Blue Locate

For the Blue Locate tool, the Marking contains a list of features and 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. matches.

## Mismatches

The Blue Locate and Blue Read tools indicate a mismatch with an orange marking Image markings are annotations produced by the VisionPro Deep Learning tools. The markings produced by a tool are the "answers" that the tool obtained when it processed a specific image. You validate the performance of the tool by comparing the markings produced by the tool with the labels that you applied to the same image. As with labels, the specific markings produced depend on the tool.. A mismatch refers to a 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). that is found by the system in an image that you labeled, but there is either a mismatch between a corresponding label, or there are possibly more instances of a labeled feature. If encountering a mismatch, the first thing to do is to check the accuracy of the 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. on the Training Sample Image Set. For example, check the stability of your lighting and appearance of the feature in the image.

During application development, a mismatch is indication that 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 is not generalized. For example, applications with very subtle differences between two features could have mismatches because Deep Learning does not have a full understanding of the features yet. In short, there is a difference between what Deep Learning has learned (from the training set) and what it sees in production (processing the test set). During this phase, you should create a distinct Training and Testing Image Sample Set. It is up to you to manage this, but any images in the training set should be trained (using a 100% fraction).

If you encounter mismatches in your testing set, this indicates that your tool has not been generalized properly. This is generally due to images that don’t properly capture the features, in other words, there are gaps in your image database. In this scenario, you will need to retrain your tool, making sure that the images/views that contain the mismatches make it into your training set and are properly labeled.

## Markings and Results

Once you have applied labels to the features in your images, and you have trained the tool, the tool will apply Markings to the areas of the image that it thinks match the feature. The difference between labels and markings is their appearance.

Note:

- The label graphics are for illustrative purposes only, and they do not affect or influence the training of the network model.

| Feature Not in a Model | Feature in a Model | Feature Assumed to be in a Model
--- | --- | --- | ---
Label |  |  |
Marking |  |  |
Mismatched Marking |  |  |

| Neither in a model | Both in a model | Only the label is in a model | Only the marking is in a model | Only the marking is assumed to be in a model | Label is in a model and the marking is assumed to be in a model
--- | --- | --- | --- | --- | --- | ---
Label covering a marking |  |  |  |  |  |
Label with a mismatched marking |  |  |  |  |  |

| Unselected | Selected | In a model | Assumed to be in a model
--- | --- | --- | --- | ---
Label feature graphic |  |  |  |
Marking feature graphic |  |  |  |
Mismatched marking feature graphic |  |  |  |

## Overlay Checkbox

The 2 tables above are shown back and forth or side by side, with the overlay graphics of labels and markings, whenever you press Alt + Left/Right () or enable each checkbox (Labels and Markings) on Overlay Checkbox