# Making Cognex VisionPro Changes that Invalidate a Deep Learning Tool's Training

Essentially, a Deep Learning tool should be re-trained whenever the input images (or the resulting Views) drastically change.

In the context of VisionPro, the following are common VisionPro tweaks that are likely to necessitate re-training:

- The image's calibration is changed.
- An image processing step is added, removed, or modified that significantly alters the image (such as subsampling).
- The PatMax tool used for fixturing the Deep Learning tool is modified and re-trained.

The following flow from acquisition to a Deep Learning tool is recommended: Acquire → Calibrate → Image Processing → PatMax → Deep Learning Red

Significant modifications to any of these steps will likely require the downstream steps to be updated. This flow orders the tools from least likely to change to most likely to change, thus minimizing the number of downstream changes that must be made.