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

How to reproducing the zero-shot result 49.38% on ModelNet40 ? #3

Open
TangYuan96 opened this issue Nov 19, 2022 · 3 comments
Open

Comments

@TangYuan96
Copy link

First of all, thanks for sharing your outstanding work

I try to train zero-shot (python pretraining. py) on ModelNet40Align and ModelNet40Ply,

the results can only reach 36.71% and 32.70%

But the result in the paper is 49.38%.

Can you retrain and get 49.38% results? What should I pay attention to during the training

Thanks!

@tyhuang0428
Copy link
Owner

tyhuang0428 commented Nov 20, 2022

You said that you use the command "python pretraining. py", while I am not sure whether you are reproducing zero-shot classification or the image-depth pre-training.
If you mean zero-shot, you can use the following command,

python zeroshot.py --ckpt [pre-trained_ckpt_path]

Otherwise, your claimed results (36.71% and 32.70%) may come from the validation set of our pre-training, which is a little bit different from our zero-shot setting. The accuracy of the validation set can reach 42.83% during our pre-training. And we find that the batch size can significantly affect the pre-training.
See if these can help you.

@tyhuang0428
Copy link
Owner

We found a bug in the pre-training code, and have already fixed it in the latest commit. We wrongly rotate the CAD models in ShapeNet when rendering depth maps. Rotation is needed only in downstream tasks.

Sorry for the confusion. Please let us know if we can assist with anything else.

@Belta911
Copy link

Belta911 commented May 21, 2024

How can I reproduce the zero-shot reasoning on scanobjectnn to achieve an accuracy of 35.46, when I can only achieve 13%? And what changes do I need to make to the code?

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