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

Batch size ablation results #5

Open
Yingdong-Hu opened this issue Mar 31, 2021 · 2 comments
Open

Batch size ablation results #5

Yingdong-Hu opened this issue Mar 31, 2021 · 2 comments
Assignees

Comments

@Yingdong-Hu
Copy link

Hello, thanks for your great work.
Can you provide additional ablations obtained using different batch size ? (e.g. smaller batch size 512/256, instead of the 1024 reported in paper)
I vary the training batch size but I find that the final result vary a lot.

@impiga
Copy link
Collaborator

impiga commented Apr 4, 2021

Hi, @Alxead .
From our experience, a "sqrt" scheduling method should be used to adjust the learning rate.
As our default setting, the learning rate for batch size 1024 is: 1024 / 256 * 1 = 4.
With sqrt scheduling, the learning rate for batch size 512 should be: 4 * sqrt(512 / 1024) = 2.828. We can modify the train script with '--base-lr 1.414' to achieve this.

@ramchandracheke
Copy link

Hi,
Thank you for your contribution. I was thinking that did you use learning rate decay as the learning rate is quite high and it should reduce as network converges.
Thanks,
Ram

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

3 participants