# Labeling in Unsupervised Type Red Analyze

For the Unsupervised type Red Analyze tool, simply assign the "good" label to defect-free views and the "bad" label to the views that have defects. The label appears as a green or a red stripe on the top right corner of 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..

"Good" Label | "Bad" Label
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When 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. this type of the Red Analyze tool, the tools only learns from the good views, but include both good and bad views in the test image set to evaluate the performance of the tool.

The accuracy of your 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 crucial for the performance of the tool. Mislabeled views can negatively impact training by teaching the tool incorrectly.

Note: You can import labels as image files, JSON files, or files in VisionPro Deep Learning internal label format. See Label Import/Export Plug-in for more information.

You can label views using the following methods:

- Click once or twice on the view in the image display area.

  - Click once on the view to label it as good.

  - Click twice to label as bad.

  - Click three times to reset the label to good.

- Using the Actions for N Views menu option in the view browser:

  This option is useful when you have a large number of images to label. Selecting this option launches a dialog, where you can define all of the images as either good or bad.