# Tool Parameters

Tool parameter choices directly effect tool execution speed, but there is typically a tradeoff between tool speed and accuracy or robustness.

Tool Parameter | How it affects speed | Best value option | Watch out for...
--- | --- | --- | ---
Feature Size | At runtime, ViDi tools need to sample the entire input image. The feature size determines the number of samples required for a given image size. The larger the feature size, the fewer the samples. | O(n2) increase in speed with larger feature size. | Larger feature sizes may cause the tool to miss features or defects. Use the Parameter Search parameter optimization utility to find an optimal feature size.
Sampling Density | Similarly to feature size, the sampling density determines the number of samples required for a given image size. | O(n2) increase in speed with lower sampling density. | Risk of missing features or defects.
Refinement parameters | The Blue Locate and Red Analyze tools include the following processing-time parameters that provide more accurate results at the cost of increasing execution time: Blue Locate tool: Precision Red Analyze tool: Iterations | Increasing the iteration value increases processing time linearly. |
Low Precision | If your system meets certain specific requirements (CUDA Compute Capability 6.1 or greater), you can enable the Low Precision processing mode for Red Analyze and Green Classify tools. Enabling Low Precision mode converts any existing trained tool to use a low-precision computation mode during processing, and it generates low-precision tools for all future training operations until it is disabled. Note: Once a tool has been converted to use the Low Precision mode, it must be retrained to disable Low Precision mode. Low precision tools can execute from 25% to as much as 50% faster than normal-precision tools. | Additional run-time speed improvements for low precision tools are seen on systems with Turing Tensor cores. | Changing a tool to use Low Precision mode may change the results the tool produces to a small degree. Generally high-level feature identification, defect classification, and general classification will be unchanged, but specific feature and defect regions and scores may change slightly.