Comments (6)
I'm glad you're interested in my research.
You can try my newly uploaded code(FixCaps_HAM-29.ipynb).
There are two factors that extremely influence the performance.
One is the preprocessing of the data, the relevant code is derived from[32](C. Zhao etal, "Dermoscopy Image Classification Based on StyleGAN and DenseNet2"). The data for the test set were randomly drawn from a specially selected set of 4501 samples(like the "lesion_id_HAM_0000005").
Another major factor is the random initialization of the weights. David Picard's work (" Torch.manual_seed is all you need On the Influence of Random Seeds") should help you.
And it is undeniable that the final choice of hyperparameters has a certain impact on the final performance of the model.
The robustness of the model does need to be improved, and recently I've been experimenting with new ideas. However, due to the difficulties of various trifles, the progress of the research is not particularly smooth. If I have further results, I will publish them on github or in a new paper.
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We can communicate by email ([email protected]).
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I have identified the factors that cause model instability: FractionalMax pooling, Squash function, random seeds, BatchSize, etc., and have overcome these flaws. I will post the code when the paper on FixCaps-V2 is published.
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Hey can i get tensorflow version of fixcaps model?
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Sorry, we currently have no plans to provide a TF version.
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Related Issues (7)
- Question about dynamic routing with BatchSize > 1 for HAM10000 FixCaps model HOT 1
- Issue on how i find the files train525orig , 525_train , 501_train etc . . . . HOT 1
- Inaccurate number of parameters from torchsummary
- HAM10000 Checkpoint Files
- Questions regarding the augmented data set
- Test Set HOT 4
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