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.

@NaomiAllen0332 A scratch's visibility can change with presentation too. Don't let 'moving streak' quietly become the rule for relabelling something acceptable.

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

So uncertain isn't the same as good? Where do those images go?

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.

@OliverAli0189 On my inspection setup, a folder called 'better lighting' also contained easier samples. The folder name was doing a lot of unearned work.

@KaiAli0193 What did you keep beside each image? I can manage sample ID and settings, but I don't want a form nobody fills in.

@OliverAli0189 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?

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.

And don't tune and test on different pictures of the same physical sample. That isn't the independence the result would suggest.

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.

@OliverAli0189 Report missed defects and false rejects separately on the held-out samples. One agreement percentage can hide which mistake your method is making.

@NaomiAllen0332 The reference problem is clearer, but I still can't judge inspection performance from our example images. The physical labels and image comparisons need more work.

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