The images bring with it their own unique set of problems. I was using it to help analyze UIs (before and after images) to determine if the changes I made were better or worse, but after using it for awhile I realized that it favored the second image in the comparison to an extent that made it difficult to tell which it thought was better. I suppose it's being trained on before and afters and generally the afters are always better!
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