Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

documentation? #50

Open
McHaillet opened this issue May 11, 2023 · 1 comment
Open

documentation? #50

McHaillet opened this issue May 11, 2023 · 1 comment

Comments

@McHaillet
Copy link
Contributor

I was wondering if there is documentation on what the optimal training parameters are? For example, how many tomograms should I provide for network training (I now provided 3, from my dataset of 100)? Same for batch size, kernel size, etc. for which I stuck to the values in the README.

Any help would be appreciated!

@JulienMaufront
Copy link

JulienMaufront commented Jun 19, 2023

Hi,

Like Marten, I'm also interested in getting an idea of how many tomograms should be used on average for training. Initially, we were told to use all the data (up to 100 tomograms here). But this takes a considerable amount of time (6 days) and even with a loss value that tends to increase over the last few epochs.
It's very different from what Marten uses here, so I'd imagine a dozen tomograms would be already enough, isn't it ?

Thank you

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants