Hello! Thanks for the great paper and code for AdaTarget, it is very interesting.
I've integrated in a model training framework here:
https://github.com/victorca25/traiNNer/tree/master/codes/models/modules/adatarget , because I was interested in evaluating it simultaneously with other strategies and it appears to work pretty well when paired together with a complex augmentation pipeline, like the ones used in BSRGAN and Real-ESRGAN, as well as other network architectures besides ESRGAN.
From my tests I can also confirm:
- it works with AMP enabled, so training models can be done consuming less VRAM (at least)
- it can potentially also work with other cases besides SR
- it works well when paired with losses that work with unaligned images (not only GAN)
I'll have to do more and longer tests, but it's very promissing. I left the initial training of the localization network for later, since I didn't find a good way to integrate it for now.
If I find something that is useful, I can let you know. Cheers!
Hello! Thanks for the great paper and code for AdaTarget, it is very interesting.
I've integrated in a model training framework here:
https://github.com/victorca25/traiNNer/tree/master/codes/models/modules/adatarget , because I was interested in evaluating it simultaneously with other strategies and it appears to work pretty well when paired together with a complex augmentation pipeline, like the ones used in BSRGAN and Real-ESRGAN, as well as other network architectures besides ESRGAN.
From my tests I can also confirm:
I'll have to do more and longer tests, but it's very promissing. I left the initial training of the localization network for later, since I didn't find a good way to integrate it for now.
If I find something that is useful, I can let you know. Cheers!