# Markings Returned by Legacy and Standard Type Red Analyze

A trained Red Analyze tool returns an anomaly score, markings that outline defects, and a colored border around 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..

- Green border: The tool marks the view as defect-free.
- Red border: The tool marks the view as having a defect.

The anomaly score each view receives is displayed in the bottom left-corner of the view. The small band in the top-right corner shows the label you assigned to the view.

Image with Defects | Defect-Free Image
Images with a high score and a red border indicate a defect. The defects are outlined. Different classes of defects have different color outlines. The tool marks a defect if at least three adjacent defect pixels have scores that exceed the threshold. | Images with a low score and a green border indicate that the tool detected no defects.

## Region List

The Red Analyze tool also returns two tables displayed on the bottom of image display area that show the information of each defect region.

- The first table shows the labeled defect region (the defect region you labeled for 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 second table shows the marked defect region (the predicted defect region as the result of processing).

Each row in a table represents the information of each defect region. For each table, you can click a row to highlight its information both in the table and in the image display area.

Column | Description
--- | ---
Class | The class of the defect. You can define your own defect classes with custom names in the Edit Regions toolbar while 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..
Score | The highest defect probability among the defect probabilities of each pixel in the defect region, as the result of processing. For the labels table, this represents the highest defect probability for a region that was labeled as a defect region. For the markings table, this represents the highest defect probability for a region that was predicted as a defect region.
Position | The x, y coordinate of the center of the defect region.
Area | The number of pixels in the defect region.
Coverage | The overlapping ratio between the labeled defect region and the predicted (marked) defect region.

How to convert markings into labels?

To provide more labeled data for further trainingTraining 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., convert some of the markings to labels.

1. Review the markings that the tool returns.

2. If you agree with the markingImage 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., right-click the viewA 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. and select Accept View to convert the marking to a label.

How to correct markings after training?

To correct markings after trainingTraining 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.:

1. Identify a viewA 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. that does not appear to be properly marked.

2. Make sure that the image is improperly marked. In the above image, the overlay markings have been hidden (by pressing Alt+Arrow key), indicating that the defect area was improperly drawn.
3. Accept the view in order to retain the area already marked as a bad region.

4. Open the Edit Regions toolbar.

5. Properly draw over the defect region.

6. Optionally, if the tool has difficulty with this particular image, add the image to the list of images that are always used when training.