# Application Development

Consider the following recommendations when developing your VisionPro Deep Learning application:

- Create clones of tools to do A to B comparisons.

  By making a clone of a tool, you can then change parameters and retrain, and then compare the original against the clone to see the effect of the tool parameter changes.

- Create clones of streams to perform image set testing.

  If you have a large batch of images that you want to use for testing, but you do not want to contaminate your database, clone a stream and add the images to the clone. Then you can process the current tool state to test. When you are done, delete the cloned stream.

- Do not add duplicate images in order to "trick" the tools.

  This negatively impacts the model A specific spatial arrangement of a set of features (Blue Locate and Blue Read tools only.) During a post-processing step, the Blue Locate and Blue Read tools can fit all of the features detected in an image to the models defined for the tool. The overall pose and identity of the model is then returned.. Instead, if you want to increase the 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. weight for specific images, use the Class Weights option of Green Classify Standard.

- Perform "bootstrap" 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.. For more information, see Bootstrap Labeling.