# Label Quality Check

Correct 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. is critical for the performance of a deep neural network. But, when you use a lot of images, it is hard to label consistently and accurately because, in many cases, labeling is done by humans. For a Green Classify, a human labeler might mislabel an image. For a Red Analyze, a human labeler can mistakenly draw a defect region on 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. without any defect. Mislabeled images eventually bring worse results for a tool.

The label check 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). displays how consistent and how accurate the label of a view is for every view. Once you run the label check successfully, you can check the quality level of the label for each view, sort the views by the quality level, and correct noisy or wrong labels.

Label Check is only supported for the following tools:

- Legacy type Green Classify

- Standard type Green Classify

- Legacy type Red Analyze

- Standard type Red Analyze

## General Workflow

If you imported images and finished labeling them, you are ready to use label check.

1. Prepare labeled views.

2. Run label check to get the label quality value for each view in the current workspace.

3. Sort the views by the label quality value (ascending order).

4. Review the views with low label quality values and modify their labels if necessary.

5. Train or process the current tool and review the results.