# Label in Green Classify

In Green Classify, the only 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. process entails simply assigning a class to which the image belongs. The Green Classify tool performs classifications of views by assigning a classification tag to the views. The tag name can be up to 140 Unicode characters.

With the Green Classify tool, you will generally be using a large Training Set (). Prior to using the tool, it is recommended to annotate your image files with descriptive names or numbering schemes to help when labeling the views.

There are two methods in assigning a tag in ‘Label Views’ dialog.

1. You can label the views by specifying a tag.

2. You can use a Regular Expression () to programmatically assign tags based on the file names of the images.

When you want to specifying 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. with a new tag name, you can do it by selecting ‘Add new tag’ item and entering the tag name in input field. Also, you can assign the existing tags by selecting tags from existing tag lists.

When you use regular expression for labeling, it is recommended to change the filename of images to include the texts or numbers which you want to use for tag name.

After the classification tags have been applied, and the tool has been trained, you can review the results to ensure that the tool correctly marked the images.

The label (aka tag), in white text, represents the "actual" class, and the 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., in yellow, represents the "predicted" class returned by the tool.

Labeled | Not labeled
This indicates that the image was labeled as Grizzly (the white text), and the tool marked it as Grizzly with a 96.4% certainty that the correct tag is "Grizzly," while it has a 3.6% certainty that the correct tag is "Bauble." | This indicates where an image was not labeled, and the tool marked the image.

Note: You can also import labels as image files, JSON files, or files in VisionPro Deep Learning internal label formats. See Label Import/Export Plug-in for more information.
Note: If the created labels seem to be incorrect or noisy over many views, you can examine the quality level of the label for each view and correct the wrong label. See Label Quality Check for more information.