# VisionPro Deep Learning Tool Operation Guidelines

The following guidelines will help you determine how best to configure your Deep Learning application, within these operational parameters. The figures in the table below (for example the maximum number of images per stream) are experimentally tested and the values vary more or less on which hardware settings you rely. Generally, the larger your memory (not GPU RAM) and hard disk capacity you have, the more the number and the size of the image you can handle.

Image and Deep Learning Tools | Configuration | Maximum Supported Value
--- | --- | ---
Images | Maximum image size (Channel parameter set to 1 to 4) | Images up to 32MP (up to 16k in height or width) are supported Images up to 32k pixels wide (in one dimension, dependent upon GPU resources) Images over 32k are not supported
Maximum number of images per stream | Red Analyze tool = 1000
Blue Locate, Blue Read and Green Classify tools = 25000
Blue Locate and Blue Read tools | Maximum number of trained features in an image | 1000
Maximum number of discrete labeled features | 256
Red Analyze tool, Supervised and Unsupervised Mode | Maximum number of defects per image | 1000
Green Classify tool | Maximum number of classes | 200

Tip: As the number and the size of images are getting larger, the training and processing time for all images increases as well.

## Development Tips

Consider the following tips when using the various Cognex Deep Learning Tools:

- To centralize focus of the tool, add a mask around the border of the view, and set the Masking Mode parameter to Mask (one of the ). This setting will focus the response of the network to the center of the ROI.

- If your application contains rotated features, teach the Blue Locate tool to properly handle rotation.

  - Enable the Oriented parameter (see the Oriented for more information).
  - Make sure that all of the various rotation variances are accurately labled.
- When using a Node Model, enable Shear. With Shear enabled, this will allow the tool's model search space to apply an affine transform to the found region for downstream tools.

- When using the Red Analyze tool in Unsupervised Mode, use the View Inspector to set the Iterations parameter (one of the ) and the Training Passes parameter (one of the ).

  The View Inspector graphically illustrates areas of the image where the tool may not be responding (or not responding strongly enough) to small defects in the image. The heat map that is generated may show "warmth" where there are defects, but not enough heat for the defect to be returned. By increasing the iterative trainings, this will force the tool to spend more time searching these "warm spot" areas, allowing the tool to find and report the defects.

  The Iteration parameter, when increased, will cause the tool to spend more time searching the warm spots in the image.

  The Training Passes parameter, when increased, will attempt to have the tool's response more homogenous between low-noise and high-noise regions. A typical indication that this parameter should be increased is when the heat map is much "hotter" on high-noise (in other words, highly structured, but good regions, of the image) than it is on low-noise regions (in other words, low complexity, normal regions of the image).