Best results in ModelNet40 about pointnet2_pytorch HOT 6 CLOSED

The default parameters should do that. train/train_cls.py will train an MSG model on modelnet40.

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This repo matches the performance from the paper. If I recall correctly, the only hyper-parameter you need to change is the number of points (to 10k) for that.

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@erikwijmans Thanks for your reply! My training results do match the paper, but it is still 0.x% accuracy gap with the paper. And I find that, in your implementation, the architecture seems to be pruned.

        self.SA_modules.append(
            PointnetSAModuleMSG(
                npoint=512,
                radii=[0.1, 0.2, 0.4],
                nsamples=[32, 64, 128],
                `mlps=[[input_channels, 64], [input_channels, 128],` 
################### in the paper, it seem to be a three layer mlp for per sub-pointnet
                      [input_channels, 128]],
                use_xyz=use_xyz
            )
        )

I want to know if you do this on purpose?
Thanks!

from pointnet2_pytorch.

I have played around with the architectures a fair amount. Makes sense to change them back to the ones given in Charles' repo, I will make that change.

from pointnet2_pytorch.

@erikwijmans How to test the model for modelnet40? Could you give some tips to re-implement the experiment result in the paper?

from pointnet2_pytorch.

@erikwijmans Thanks for your reply! My training results do match the paper, but it is still 0.x% accuracy gap with the paper. And I find that, in your implementation, the architecture seems to be pruned.

        self.SA_modules.append(
            PointnetSAModuleMSG(
                npoint=512,
                radii=[0.1, 0.2, 0.4],
                nsamples=[32, 64, 128],
                `mlps=[[input_channels, 64], [input_channels, 128],` 
################### in the paper, it seem to be a three layer mlp for per sub-pointnet
                      [input_channels, 128]],
                use_xyz=use_xyz
            )
        )

I want to know if you do this on purpose?
Thanks!

Hi, sorry to bother. Did you match the paper's accuracy with the newest arch? I run the code and only got 0.9023 with the default hyper-parameters.

from pointnet2_pytorch.