# Using the Cognex Deep Learning Parameter Search Utility

To use the Cognex Deep Learning Parameter Search utility:

1. Set up the workspace, the stream, and the tool for search on the Home page.

   1. Select the path for loading workspaces

   2. Select a workspace among the loaded workspaces

   3. Select a stream in the selected workspace

   4. Select a tool.

   5. Click OK to go to the Search page.

2. Select the search mode.

3. Select a set of tool parameters and provide the value range of them to be searched through.

4. Set the search options in the Search page if necessary.

5. Click Start.

   1. Choose the target directory for saving the results:

   2. Click Save.

6. After the search is completed, go to the Result page to review the results for each combination of parameters.

   If searching in manual mode Manual mode is a grid-search mode in the Cognex Deep Learning Parameter Search utility, which runs tests on every selected parameter and their value ranges., go to the Graph page to review the contour graphs that displays the relationships between the selected parameters in terms of the tool performance metrics, and decides which set is the best combination of the parameters for the current tool.

7. Before you click to go back to the Home page, click Save to save the current parameter search results.

   Note: Going back to the Home page resets all the results.

## Best Practices

Best practices for auto mode The auto mode is a grid-search mode in the Cognex Deep Learning Parameter Search utility, which automatically optimizes the search schedule by adopting a policy that obtain the best results with minimizing the search time. Given a list of parameters and their values, auto mode stops training the loaded tool and moves on to training with another parameter values when it finds no reason for better results from the current parameter values. Only Standard type tools support auto mode.:

- Focus on selecting parameters with potential rather than choosing all parameters.
- Even if all parameters are selected, pause and resume to check whether satisfactory performance is achieved even if the progress of the search is low.

Best practices for manual mode:

Manual mode trains and processes a tool with every possible combination of the selected parameters and their value ranges. To optimize the search schedule, you must manually stop the test and change the list of parameters or their value ranges to get the best results within a certain time limit.