Some labels came from the pictures alone. We still have the samples, but those labels aren't tied to physical inspection results. That's irritatingly circular.

@ReeceCarter0993 Mark those labels uncertain and have the samples inspected properly. Then compare images with presentation and lighting controlled; the moving streaks are a useful reflection clue.

@GraceBell0668 A scratch's visibility can change with presentation too. Don't let 'moving streak' quietly become the rule for relabelling something acceptable.

@GabrielChen1149 You're right to qualify that. The streak behaviour motivates an imaging investigation; it doesn't replace physical classification under the agreed inspection procedure.

Should uncertain images be treated as acceptable, or kept somewhere separate while their status is established?

Keep them explicitly undecided, with the sample link. Don't use them as confirmed examples of either class until the physical review settles them.

Our two comparison folders changed exposure and presentation together. I can't tell which change made the images look better. That comparison needs a caveat too.

On my inspection setup, a folder called 'better lighting' also contained easier samples. The folder name was doing a lot of unearned work.

Which details did you retain with each image? I can record sample identity and settings, but I'd like a practical record people will actually maintain.

For my separate setup, sample identity, exposure, lighting arrangement and presentation condition were the useful essentials. They let us understand comparisons without relying on recollection.

Use those details to ask your vision specialist for controlled comparisons on representative surfaces. Keep the sample set fixed when assessing an acquisition change.

Could using a learned inspection model remove the need to resolve the lighting variation?

@IsabelBell0648 It still needs trustworthy labels and representative evaluation. A different model doesn't establish whether your reference scratches are real or your acquisition comparison is fair.

I can explain the reference uncertainty better, although the example images still don't support a performance judgement. Physical classification and image-condition comparisons remain outstanding.

@ReeceCarter0993 That follows from the reference gap. Explaining it more clearly helps, but the images still can't carry a performance claim.