1. You have a CZI image with image analysis results. For detailed information on analysis, see Image Analysis.
2. You have licensed the AI Toolkit and activated it under Tools > Toolkit Manager.

1. On the Analysis tab, in the Intellesis Object Classification tool, click and select New.
2. Enter a name for the new model and click . Alternatively, enter the name and press Enter.
3. A new setting with the name is created and automatically selected as Model.
3. Click Start Training.
4. The Training User Interface Intellesis Object Classification opens.
4. In the Image Gallery on the right, click Import Images.
5. A file browser opens.
5. Select the image(s) you want to use for classification and click Open.
6. If the image analysis contains more than one class and channel, the Select Region Class and Channel dialog opens.
6. Select the Region Class and the channels you want to use for classification and click OK.
7. The image(s) is/are imported into the trainings interface.
7. In the Open Images tool on the right, click on the image you want to label.
8. The image is displayed, and the table shows the data of the analysis result.
8. On the left side, add the classes you need for your object classification by clicking Add Class.
9. You have created the classes that you want to distinguish.
   
9. To change the label color for a class, click on the color field of the list entry and select one from the window.
10. To change the name of a class, right click on the entry, select Rename, enter a new name, and click . Alternatively, double click the name entry, enter a new one and click , or press Enter.
11. In the Labeling Options tab, click .
12. You are now in labeling mode.
12. In the classes list, select a class and click on an object that belongs to this class in the image or in the table.
13. You have labeled the object and assigned it a class.
    
13. Repeat the labeling for the objects of the different classes you created.
14. Click Train & Classify.
15. Your model is trained based on the labeling and a prediction is displayed.
    
15. If you are not satisfied with the result, you can label more objects, click Train & Classify again, and iteratively improve your results.
16. When you are satisfied with the result, click Finish.
17. All changes are saved, and the training interface is closed.

1. You have successfully created and trained a model for object classification. You can now use it to classify objects in your analyzed images with the processing function Intellesis Object Classification, see Using an Intellesis Object Classification Model for Image Processing.