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Codebase for evaluation of deep generative models as presented in Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models

License: MIT License

Jupyter Notebook 98.27% Python 1.72% Shell 0.01%

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dgm-eval's Issues

Evaluation differences between open_clip and clip

Thanks for sharing this great repo with the community. I would like to point out one issue I'm encountering with the clip model you are using. There is a difference between the output of the clip model you are using and the one provided by openai. This impacts the results of the metrics you implemented. I'm getting lower values for some metrics using the openai version.

It would be great if you can double check.

Thanks in advance.

sFDD?

Hi, do you have the implementation of spatial FDD with Dinov2 feature?

ConvNeXt: v1 vs v2

ConvNeXt V2 introduce FCMAE self sup pretrain and gain the performance for 0.5~1.5% top1 acc.
And I'm suprised that the ConvNeXt perform so bad at this kind of task
I'm wondering if ConvNeXtV2 has better result?

is it ok to provide some data related to ConvNeXtV2
(or some tutorial on how to measure the performance with same data/method you guys used in other models?)

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