Skip to content
This repository has been archived by the owner on Mar 17, 2021. It is now read-only.

Ensure efficient IO when training on large sets of 2D images #185

Open
tvercaut opened this issue Jul 31, 2018 · 2 comments
Open

Ensure efficient IO when training on large sets of 2D images #185

tvercaut opened this issue Jul 31, 2018 · 2 comments
Assignees

Comments

@tvercaut
Copy link
Member

tvercaut commented Jul 31, 2018

As discussed with @atbenmurray and previously with @wyli and @luiscarlosgph, this is a follow up of cmiclab issue #205. We now have support for 2D images but it's rather crude and would benefit from being optimised for example by storing the images in a dedicated high-performance database (LMDB?).

The first task would be to look into the current state of the art in other TF-based projects for that.

@tvercaut
Copy link
Member Author

@atbenmurray
Copy link
Collaborator

Ok, I'll look into this today

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

No branches or pull requests

3 participants