# Play with Tutorial

This topic introduces how you can play with the tutorial of the VisionPro Deep Learning tool.

## Recap: Training Workflow

The training workflow of Blue Locate can be summarized into:

1. Collect training sample images. You should also have images to review the results of your training.
2. Set the Region (ROI) where the tool will look for characters, and the Feature Size, which is the expected size of the characters within the ROI.
3. Create image sets for convenience, if necessary.
4. Label and Train.
   1. You will need to label at least one instance of each character. Labeling is the means of instructing the tool that it locates the feature correctly. If there are misread or missing characters, you will need to label them.
   2. Once characters are labeled, you need to train the tool, in other words, adding the images and character labels into the neural network. Split the entire images into the training images and the test images. Edit tool parameters, and start training the neural network.
5. Evaluate your results on untrained images to determine if the tool read the characters correctly.

## Download Tutorial

These tutorials are available on the Cognex Support website; download them and then import them into a newly created workspace, remove all tools and start from scratch.

Cognex provides several tutorials to help you learn how to use the VisionPro Deep Learning software, as well as understand some of the key concepts.

1. Download the VisionPro Deep Learning tutorials from Cognex support website.
2. Start the VisionPro Deep Learning graphical user interface (GUI).
3. Import a tutorial workspace from the Workspace menu, which launches the Import Workspace dialog.

4. Navigate to the directory where you stored the downloaded tutorials, and select a workspace to get started.

   Note:

   To navigate to a previously created workspace, expand the Workspace container by pressing the icon, and then select the desired workspace from the list and press the arrow icon:

   Note: You can consult the Cognex Deep Learning Tutorials topics for additional information.

5. Click each tool, update it with Tool - Update Parameters, and click the brain icon to train and see the results.

## Tutorial: Localize Screw

Screws or any other cylindrical object need to be rotated during inspection. Looking not only at one specific line along the rotation axis, but at the full object over several images, brings the advantage that you can see the surface and defects at different angles to the camera and the illumination. For instance, some defects will show best when they are on the center line, others will show better when they are slightly off to the sides.

In this particular set of images, however, the screws are moving across the entire image such that it is advisable to first use a Blue Locate Tool to localize and crop the screw image before feeding it to the Red Analyze tool for detecting any defect.

The objectives of this tutorial are:

- Use the Blue Locate tool to localize and track fuzzy features.
- Generate a geometric model to find the screw's pose.

Note: For more information on the specific steps of setting up the tools, see the Blue Locate.

1. Add the images for the Screw tutorial.
   1. While in training mode, after a Workspace has been added or opened, images can be added through either the Database menu (select Add Images), by pressing Add Images in the display space of the GUI, or dragging and dropping images from a Windows Explorer directory into the View Browser.
2. Add a Blue Locate tool.
3. Adjust the ROI to segment an image, and then press the Scissors icon to process the images based on the new ROI.
4. Set the Feature Size parameter to 80.
5. Define at least two features (for example, the end and the tip of the screw) on at least three images.
6. Press the Brain icon to train the system. Then review the results and manually label the images where the features were not found correctly.
7. If you had to manually label new images, retrain the system and review the results.
8. Right click on an image which has the two features and create a model named "screw".
9. Reprocess the image and make sure the model is correctly found.