# Using the Tools

Consider the following tips when using the various VisionPro Deep Learning tools:

- To centralize the focus of the tool, add a mask around the border of the view A view of an image is a region of pixels in an image. Tool processing is limited to the pixels within the view. You can manually specify a view, or you can use the results of an upstream tool to generate a view., and set the Masking Mode parameter to mask. This setting focuses the response of the network to the center of the 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..

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

  - Enable the Oriented parameter.
  - Make sure that all of the various rotation are accurately labeled.
- When using a Node Model, enable Shear. With Shear enabled, 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. of the tool searches space to apply an affine transform to the found region for downstream tools.

- When using Red Analyze Unsupervised, use the View Inspector to set the Iterations parameter and the Training Passes parameter.

  The View Inspector graphically illustrates areas of the image where the tool might not be responding (or not responding strongly enough) to small defects in the image. The heat map that is generated might show "warmth" where there are defects, but not enough heat for the defect to be returned. Increasing the iterative trainings makes 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, causes the tool to spend more time searching the warm spots in the image.

  The Training Passes parameter, when increased, attempts to have the response of the tool 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).