# Label Quality Check

Correct labeling 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 without any defect. Mislabeled images eventually bring worse results for a tool.

The label check feature 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:

- Green Classify Focused

- Green Classify High Detail

  - Basic Mode

  - Adaptation Mode

- Red Analyze High Detail

  - Basic Mode

  - Adaptation Mode

- Red Analyze Focused Supervised

## 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.