A dataset is not the process
Prototype images are often cleaner and less variable than the production line. Real systems encounter vibration, glare, dust, part variation, camera movement, new suppliers, and changing defect definitions.
Imaging is part of the model
Camera position, lens, exposure, trigger timing, and lighting frequently improve performance more than another training cycle. The acquisition system should be engineered with the same care as the neural network.
Errors have operational costs
A false reject may stop a line or create costly manual review. A false accept may create a quality escape. Evaluation must reflect those consequences, not only a single aggregate accuracy score.
Build the learning loop
Production systems need evidence capture, reviewer decisions, model versions, threshold history, and drift monitoring. Without that loop, performance decays while the organization loses confidence in the system.
Apply this thinking to a real system.
We can map the operating constraint, evidence requirement, and safest path to a representative proof.
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