# Train Legacy Type Green Classify

You train the tool to identify and classify an object or the entire scene in an image. The goal of 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. is to reduce the number of mismatches between labeled and detected classes.

## Prerequisites

- You have created or imported a workspace into VisionPro Deep Learning application.

- You have collected images and loaded them into the application.

## Procedure

1. Add a Green Classify tool and set the Type parameter to Legacy.

2. Define ROI The Region of Interest (ROI) defines the area of operation for the tool. The ROI preserves the positions, angles, stretch and skew of the original image. (Region of Interest) to construct Views.
   1. If a pose from a Blue Locate tool is being used to transform the orientation of the View being used as an input to the Green Classify tool, process the images (press the Scissors icon) before opening the Green Classify tool.

   2. If necessary, adjust the Region of Interest (ROI). Within the Display Area, right-click and select Edit ROI from the menu.

   3. After adjusting the ROI, press the Apply button and the adjusted ROI will be applied to all of the images.
   4. Press the Close button on the toolbar to continue.
3. If there is extraneous information in the image, add appropriate masks to exclude those areas of the image. Within the Display Area, right-click and select Edit Mask from the menu.

   1. From the Mask toolbar, select and edit the appropriate masks.

   2. After adding the necessary mask(s), press the Apply button and the mask will be applied to the current image.

      If you click Apply All and click Yes button on the ensuing Apply Mask dialog after adding the necessary masks, the same mask will be applied on all the images. If you click No on the dialog, the mask will not be applied and you go back to the Edit Mask window.

   3. Press the Close button on the toolbar to continue.

4. Go through all of the images and label the images with classification tags.

   Tip: It is recommended to annotate your image files with descriptive names or numbering schemes to help when 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. the images.

5. You can use the Label Views option in the View Browser.

6. When labeling, you can either apply a tag or use regular expressions to apply the classification tag label.

7. Make sure that all of the images have been labeled with a classification tag.
8. Split the entire images into the training images and the test images. Utilize image sets to properly divide them into the training and test group. Add images to the training set.
   1. Select the images in the View Browser, and on the right-click pop-up menu click Add views to training set. To select multiple images in the View Browser, use the Shift + Left Mouse Button.
   2. Or, use to show the desired images for the training only and add them to the training set by clicking Actions for ... views → Add views to training set.
9. Prior to training, you need to set parameters in Tool Parameters. You can configure Training, Sampling, and Perturbation parameters or just use the default values of these.
   1. Ensure that the Feature Size parameter in Sampling parameters has been set. The Feature Size parameter gives the network a clue to the size of the features that you are interested for classification. So, if the Feature Size parameter setting is larger than the features in your application, there is a good chance the tool will not determine the correct class for your image.
   2. You can set the Feature Size by either manually adjusting the parameter value, or graphically by re-sizing the interactive Feature Size graphic.
   3. If you want more granular control over training or processing, turn on Expert Mode on Help menu to initialize additional parameters in Tool Parameters.
10. Train the tool by pressing the Brain icon.
    1. If you stop training in the middle of it by pressing the Stop icon, you can stop training but you will lose the current tool so far trained.

11. After training, review the results. Open Database Overview panel and review the Confusion Matrix and Precision, Recall, F-Score for each class (tag) to understand the results.

12. After reviewing the results, go through all of the images and see how the tool correctly or incorrectly marked the tags for each image.
    1. If the tool has correctly marked the images with the appropriate tag, right-click the image and select Accept View.
    2. If the tool has incorrectly marked the images with incorrect tags:
       1. Right-click the image again and select Clear Markings and Labels.

       2. Manually tag the image.

    3. If you encountered the scenario in (a.), you are ready to use the tool. If you encountered the scenario in (b.), you will need to retrain the tool.