Faceswap Forum

It occurred to me that maybe having too many faces, and not enough balance, could throw everything off. I figured it was better to have a LOT of "A" faces (more than "B") was a good thing, but then I recently started noticing the "halo", double face effect, and no matter how I tried to position the alingments, it wouldn't go away. I was wondering if this is caused from having not enough balance. In this example, my A faces are about 3,000, and my B Faces a little more than 2,000.

How does this unbalance effect the model? Thoughts?

I don't know what the ""halo", double face effect" is.

However, in wider terms, there is no benefit to loading up one side more than the other. There shouldn't be any obvious detrimental effect if they are not matched in volume, as long as the data is similarly distributed in terms of variety.

Thanks for the answer. I'll post a picture example of the double face effect.

It's basically where you can see both faces, with the "A" face giving a "halo"/outline effect in the form of a haze on the outside of the the "B" face. I can't tell why. I try to do test conversions as I train and it seems like it happens more as training goes on. Which is why I thought it's possibly from an imbalance of volume, where as the AI is seeing the "A" face more and therefore incorporating that strong presence in the conversion.