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gradientic t1_jc430rz wrote

Not really direct answer to your question, but the general problem you are trying to solve is called image anomaly detection, there are well know approaches that try to solve this problem, some of them in an unsupervised manner (assuming that you have a significant dataset of images without anomalies - learn inlier look for outliers) - check https://towardsdatascience.com/an-effective-approach-for-image-anomaly-detection-7b1d08a9935b for some ideas and pointers (sry if pointing obvious things)

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Tekno-12345 OP t1_jc5vjac wrote

My problem with AD models is that they are very sensitive, even if trained on noise.
Any small diversion will be detected as defective.

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HarissaForte t1_jc6sser wrote

> Any small diversion will be detected as defective.

If this small diversion is smaller than the diversion within your train dataset (+ the noise) then it should not be detected.

Did you try different splits or a CV ?

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Tekno-12345 OP t1_jc9oymg wrote

You're right, I'll try to incorporate more representative data.
I'll also try different splits
Thanks

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