Machine Vision & Industrial Inspection AI worked example

Defect Classification Accuracy at 68% required classification accuracy per quality plan: a worked example

Suppose required classification accuracy per quality plan falls to 68%. This page works the full calculation at that level so you can see exactly which result moves and by how much. Calculate the accuracy with which an AI inspection model or vision system correctly identifies the type of defect detected, by comparing correctly classified defects against the total number of defects in the validation sample.

The inputs for this scenario

  • Defects correctly assigned to the right defect class: 186 defects (held at the documented default)
  • Total defects in the classification validation sample: 200 defects (held at the documented default)
  • Required classification accuracy per quality plan: 68 % (the input this scenario stresses; the baseline uses 95)

Working through the calculation

  • The calculation starts from the formula this tool documents: Defect classification accuracy = correctly classified / total defects x 100.
  • Defect classification accuracy works out to 93 % at these inputs, and this is the headline figure for the scenario.
  • Gap to required classification accuracy works out to -25 points at these inputs.
  • Defects correctly classified works out to 186 count at these inputs.
  • Total defects in validation sample works out to 200 count at these inputs.

How this compares with the baseline

  • Against the tool's baseline example, where required classification accuracy per quality plan sits at 95% and the headline result is 93 %, this scenario lands almost exactly on the baseline at 93 %.
  • It computes the percentage of defects placed in the correct class and the point gap to your required classification accuracy. When the numbers land here, the stressed input is the lever to work; the walkthrough above shows exactly how much each output recovers as it climbs back toward the baseline.

Results at a glance

  • Defect classification accuracy: 93 % (headline result)
  • Gap to required classification accuracy: -25 points
  • Defects correctly classified: 186 count
  • Total defects in validation sample: 200 count

Run it with your numbers

  • To rerun this with your own numbers, open the live Defect Classification Accuracy calculator, set required classification accuracy per quality plan to your actual value, and adjust the remaining inputs to match your operation.

Last reviewed 2026-05-12.