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Reproducing evaluation results #7

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Michiexb opened this issue Sep 1, 2021 · 10 comments
Open

Reproducing evaluation results #7

Michiexb opened this issue Sep 1, 2021 · 10 comments

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@Michiexb
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Michiexb commented Sep 1, 2021

Hi! I'm having some trouble with reproducing your evaluation results. I've run your eval bash file for all models with the checkpoint files that I downloaded from the link in the Readme, but the results are very different from the ones mentioned in your paper.

These are the accuracies I get:
beta_1: --- I get 25.30 % --- Should be 67.30 %
beta_2: --- I get 0.56 % ---- Should be 71.73 %
beta_4: --- I get 0.21 % ---- Should be 73.69 %
beta_8: --- I get 0.12 % ---- Should be 74.59 %
beta_16: -- I get 0.76 % ---- Should be 75.54 %
beta_32: -- I get 36.81 % --- Should be 76.18 %
beta_inf: -- I get 74.40 % --- Should be 76.27 %

Do you have any idea why this might be? Because I would love to use your INN, but of course would need a better accuracy than what I am getting now.

@ZY123-GOOD
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Hello, I wonder if you know how to solve this problem.

@Michiexb
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I didn't fully reproduce the results yet since my time is currently more important to me than the accuracy of the model, but I did train the model for 10 more epochs starting from the checkpoint file (by setting resume_checkpoint in the config to True), and that did give me 67.55% for the beta_8 model.
So probably, the checkpoints that they shared are not the fully trained checkpoints from the paper.

@ZY123-GOOD
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ZY123-GOOD commented Oct 18, 2021 via email

@RayDeeA
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RayDeeA commented Oct 18, 2021

Hi, how are evaluating the model?

@Michiexb
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The way that it's mentioned in the readme

@RayDeeA
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RayDeeA commented Oct 18, 2021

Ok, that is strange. I’ll clone and check.

@ZY123-GOOD
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Ok, that is strange. I’ll clone and check.

Thank you! Please check it. I am looking forward to using your INN in our new work.

@ZY123-GOOD
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Ok, that is strange. I’ll clone and check.

Today I also get that beta_1: --- I get 25.30 % --- Should be 67.30 %. Could you please correct it in your busy schedule?

@jenellefeather
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I know this is an old issue and its possible the repo is no longer being maintained, however I am getting the same low performance results for the downloaded checkpoints (36.948% for beta_32).

Is there any update on the discrepancy?

@craymichael
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craymichael commented Feb 25, 2024

I am also getting the same issue with the exact same accuracies reported in the issue.

Conda (23.1.0) Environment:

  • torch==1.7.1
  • FrEIA at tag v0.2
  • Python 3.9.18

The command I run to evaluate the model:

python -m ibinn_imagenet.eval.ibinn_imagenet_classifier \
    --model_file_path=checkpoints/beta_2.avg.pt \
    --evaluation=accuracy \
    --data_root_folder_val='/mnt/data/imagenet/' \
    --data_root_folder_train='/mnt/data/imagenet/' \
    --model_n_loss_dims_1d 3072 \
    --data_batch_size 32

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