Comments (6)
Hi @mbsariyildiz, sorry for the delay. I was just being lazy in cleaning things up. Here's the code for conversion. It only supports ResNet-50 for now. Let me know if you need the ResNet-200x2 model as well.
from deepmind-research.
Hi,
It seems the culprit was indeed the padding. With that fixed, I am able to get 74.6%! I am thinking of creating a repository for the PyTorch weights. Would you consider linking to it in the README so that people can easily find it?
from deepmind-research.
Hi, glad to know you managed to pinpoint the issue and managed to reproduce the results!
Sure, providing PyTorch-compatible weights could also be very useful. Feel free to reach out (maybe by email?) if you would like us to host the files, and we will add a link (crediting you of course) to the README.
Best,
The BYOL team.
from deepmind-research.
Hi,
Great. I need to convert the R200x2 weights and clean up the code a bit. Thanks again.
from deepmind-research.
Hello @ChigUr and the BYOL Team (@altche) !
Thanks a lot for sharing pretrained weights.
Is there any update on this? I'd be very glad if you could share the reproduced PyTorch implementation & weights.
from deepmind-research.
Hey @ChigUr, thanks a lot for sharing your converter! I need just ResNet-50 models. :)
from deepmind-research.
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from deepmind-research.