You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This is really helpful code and very good documentation on how to use it.
I just have one small question. I trained the network on some images (I used just 8 images as a test). Based on the visual performance during training the network was producing great outputs after 100 epochs. However when I then tested the trained network on the same images I used in training - exampleOutput = p2p.translate(p2pModel, exampleInput) - the output was not as good as I expected. The quality of the output images was similar to what the network had been producing after about 60 epochs during training.
Is there something I am missing here that resulted in this behaviour?
Steve
The text was updated successfully, but these errors were encountered:
Hmm, the only thing I can think which might be the difference is that there is an "ARange" options to p2p.translate which defaults to 255, but this might not be correct for your input data.
This is really helpful code and very good documentation on how to use it.
I just have one small question. I trained the network on some images (I used just 8 images as a test). Based on the visual performance during training the network was producing great outputs after 100 epochs. However when I then tested the trained network on the same images I used in training - exampleOutput = p2p.translate(p2pModel, exampleInput) - the output was not as good as I expected. The quality of the output images was similar to what the network had been producing after about 60 epochs during training.
Is there something I am missing here that resulted in this behaviour?
Steve
The text was updated successfully, but these errors were encountered: