Ow I see,
I have already done something similar but the results were not convincing.
Maybe I'll try it again using the masks.

Thanks for the good ideas, i'll try them out.
What do you mean by the last part? How can I create patches of defects using the segmentation data?
You mean by classical methods?

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

I have a lot of good images with no defects.
And very few images with defects.

In order to train a good object detector for this task, a huge amount of defects will be needed.

It could be..
However, such models require the data I am seeking

Thanks for the reply.
Yes classical methods did not generalize well on foreign data.
I don't have experience on GANs and wanted some experienced opinions.

I have some leads now, I'll try them out and get back to your comment if they did not work out.

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

Already tried these methods, they did not help much