@LiamChen1186 I've found that some labels were assigned from the images themselves. The samples are still available, but those labels have no corresponding physical inspection results, making the reference 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

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.

@MinaArcher0424 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.

@GraceArcher0407 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.

@MinaArcher0424 Those records can support the vision specialist's comparison of image conditions. Holding the representative sample set constant will make an acquisition change easier to assess

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

@JamieBrown0904 I'll group images by physical sample before dividing the tuning and evaluation sets. The current filenames wouldn't reliably expose the same sample appearing in both.

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.

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