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

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.

That clue needs a limit: presentation can also change how a real scratch appears. Movement in the image isn't enough to reclassify the sample as acceptable.

Correct. I meant a reason to investigate the imaging, not a new defect label. Physical acceptance stays with the agreed inspection method.

@ChloeAli0222 Should uncertain images be treated as acceptable, or kept somewhere separate while their status is established?

Retain their sample identities and undecided status. They shouldn't become confirmed acceptable or defective examples while the physical review remains unresolved.

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

I had a separate inspection setup where the 'better lighting' folder used easier samples too. Its name implied a lighting conclusion that the comparison didn't support.

What did you keep beside each image? I can manage sample ID and settings, but I don't want a form nobody fills in

On my setup we kept sample ID, exposure, lighting arrangement and presentation condition. Enough to explain a comparison without reconstructing the whole session from memory.

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?

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.

Keep physical sample identity in mind when forming evaluation sets. Different images of a sample used for tuning don't provide an independent sample-level evaluation.

I'll keep all images of one sample together when we split tuning and evaluation sets. Our file names alone wouldn't have caught that overlap

I've settled the review method: physical reference checks plus controlled image comparisons. That answers where to start; whether our inspection concept is suitable still depends on the resulting evaluation

That gives the evaluation a sound question to answer. Suitability remains dependent on those checks, as you've made clear.