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lzhengning / SubdivNet

Subdivision-based Mesh Convolutional Networks.
MIT License
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cannot reproduce results on Manifold40 #24

Closed lykius closed 2 years ago

lykius commented 2 years ago

Hi, I'm trying to reproduce your results on Manifold40.

I downloaded the dataset with the command sh scripts/manifold40/get_data.sh, which downloads the file Manifold40-MAPS-96-3.zip. I noticed in your instructions that it is possible to download a different version of Manifold40 "before remeshing". Which is the correct version to use for evaluation?

I noticed that the version in Manifold40-MAPS-96-3.zip contains approximatively 10 times the number of shapes contained in the original ModelNet40, why is that?

I run the command sh scripts/manifold40/get_pretrained.sh to obtain the checkpoint for the model and then I run the command sh scripts/manifold40/test.sh to perform evaluation. The evaluation runs fine but I get a very low accuracy. Do you have any idea of what I could have missed?

Thanks in advance, Luca.

lzhengning commented 2 years ago

Hi Luca @lykius,

  1. Use the scripts/manifold40/get_data.sh to download the processed dataset.
  2. We generated different meshes of a same shape as data augmentation. More details are provided in the sections about remeshing and data augmentation. A related discussion is here #11.
  3. In the previous released codes, the classification labels are determined by the order in file system. This leads to inconsistent labeling in different OS. I realized this a few weeks ago, and changed the labels to be ordered by the class names. Today I have updated the codes, scripts, and pretrained weights. Please update your local repository and download the newest weights before running the test script.

Feel free to reply if you have any more questions.

lykius commented 2 years ago

Hi @lzhengning,

Thanks a lot for your answer. I pulled the repo and re-downloaded the weights, now I get 90.9% accuracy without voting and 91.5% accuracy with voting. Does it sound right to you? The paper reports 91.2% and 91.5% respectively.

lzhengning commented 2 years ago

Yes, 90.9% (without voting) and 91.5% (with voting) are the performance of this provided checkpoint.

A network does not always achieve the highest accuracy both with and without voting. 91.2% and 91.5% are accuracies from two separated checkpoints in our experiments. Since 90.9% is quite close to the best, only the checkpoint with 91.5% voted accuracy is released for brevity.

lykius commented 2 years ago

Ok thanks! This closes the issue.