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

Training time for ZF #192

Open
lynetcha opened this issue May 17, 2016 · 6 comments
Open

Training time for ZF #192

lynetcha opened this issue May 17, 2016 · 6 comments

Comments

@lynetcha
Copy link

lynetcha commented May 17, 2016

How long can I expect faster_rcnn to take to train ZF on VOC2007? I am getting about 4.5hours on a TitanX, which seems pretty long. (The training rate is 0.242s / iter)

@happyharrycn
Copy link

If you are using end2end training, have a good SSD and compiled Caffe with latest cuDNN, training should be much faster than 200 ms per iteration.

@Austriker
Copy link

@happyharrycn Which version of caffe do you use ? I tried to build the caffe fork of this repo with cuda 7.5 and cuDNN v5 and it didn't work.

@happyharrycn
Copy link

I am using the caffe main branch (cuDNN v5 is supported on May 17th). You will need to merge the RoI pooling and the smoothed l1 loss layers from Ross's caffe version.

@Austriker
Copy link

Austriker commented May 19, 2016

@happyharrycn I have done a PR 4163 that merge the both layers to caffe and I am working on porting the code to python 3.4 py-faster-rcnn fork.
I still have an issue with the test in my PR. I can't manage to solve it yet !

Can I have the link to Ross's caffe version please ?

@happyharrycn
Copy link

@Austriker I have commented on your PR 4163. It should compile and work now.

@Austriker
Copy link

@happyharrycn Great thank you ! I will test the python 3 port ! It will avoid a lot of maintenance problem having only one caffe !

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