Training detail and performance for VN_DGCNN on ModelNet 40.

[GostInShell]

Thanks for the wonderful code!

With the SGD optimizer (initial lr=0.1) and StepLR scheduler, I achieve the result 73.4% on SO3/SO3. Moreover, I find the training process rather unstable with large fluctuation, comparing to the normal DGCNN and PointNet. Is this usual? I doubt if I run the code correctly. Could you also provide the training log or the environment?

[FlyingGiraffe]

Hi, thanks for the question.

Here's a log for training on aligned data run.log -- as time goes by it's a bit hard for me to find all the logs from the past... For the learning rate, could you please verify that you use the default lr=0.001, as it is multiplied by 100 in the SGD optimizer (see here). This update on the optimizer is due to a previous issue.

Best,
Congyue

[GostInShell]

Thanks for the quick reply!

The learning rate is set correctly. Then I can only assume it may be due to the environmental setting. Could you provide the environment.yaml please?

Thanks for the log file. I'll try on the aligned data.

[FlyingGiraffe]

To be honest I don't think the issue is in the environment... I've checked the code today and noticed that the SGD optimizer didn't have a weight decay -- already updated now. I merged VN_DGCNN into the VN_PointNet code framework before pushing it to GitHub, and thus a lot of settings may be different from the original DGCNN framework. I'll continue to check for the settings, and in the meantime, perhaps I can also push the VN_DGCNN code within the original DGCNN framework, which was what I've been using to generate all the results in the paper.

[GostInShell]

I've rerun the network with the weight decay. While the result still doesn't seem right. The accuracy stuck at ~60% since the 50th epoch. Thanks a lot for the careful check and looking forward to the update.

[FlyingGiraffe]

Hi, I've uploaded the separated version to this repo, together with the pretrained classification models. You can probably try this.

[GostInShell]

I'm retraining the new repo and the performance seems on track this time! Thanks a lot!