Comments (2)
Hi Danny,
Thanks for pointing out those missing details, I'll update the doc soon!
The train.py
is just meant to be an example script. Internally we use a more complex training platform but we try to provide details in the documentation. To answer your questions:
- ReduceLROnPlateau is only applied on the main loss
- We use a patience of 20 and a factor of 0.5, all the other parameters are kept to the default pytorch values.
It's definitely possible that this simple LR scheduling is not optimal and a better one could be found to train the networks faster. I performed some experiments (warm up, cosine annealing, etc...) but could not find a better alternative.
And yes, we did train the MS-SSIM networks from pre-trained MSE networks. Training is faster and more stable. We used a learning rate of 1e-5 for both optimizers.
Let me know if you have more questions!
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Thanks a lot Jean!
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