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

is it normal for a slow convergence? #14

Open
kuaitoukid opened this issue May 11, 2017 · 2 comments
Open

is it normal for a slow convergence? #14

kuaitoukid opened this issue May 11, 2017 · 2 comments

Comments

@kuaitoukid
Copy link

I am trying to apply this method to detect other objects, however, even there is only one training sample without augmentation operation, this framework is hard to convergence? Is it normal? I follow the training method for people segmentation, and only train the 8-2 and lstm model.

@isn4
Copy link

isn4 commented Jul 24, 2017

@bernard24 I've noticed slow convergence as well with the plants segmentation. I've got about 2000 training images and when training for 100,000 iterations it's taking roughly 48 hours to get through only 60,000 iterations.

@bernard24
Copy link
Owner

Not sure if this is your case, but as far as I have seen, convergence is significantly harder to achieve if the instances are very different among them (e.g. one experiment in the paper is on segmenting people, but segmenting simultaneously people and other objects becomes harder). In any case, starting training with a low number of iterations for the ConvLSTM (e.g. 2) and increasing it only when it achieves convergence, seems to help.

@isn4 do you mean that the time per iteration is too slow?

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