Comments (10)
xiaojidan
commented on August 27, 2024
9
hi @cszn can you release your training codes? I find that your all projects lost the training codes, I think you should release them so that we use your training methods and cite your paper
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cszn
commented on August 27, 2024
3
@xuanzhangyang @xiaojidan @QueeneTam @usmankhalid-developers https://github.com/cszn/SRMD/tree/master/TrainingCodes
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QueeneTam
commented on August 27, 2024
@cszn hi, I am a student who are doing a project about medical imageing, and I have a keen desire on your training code, hope that I can get your response from Email. Thanks a lot!
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usmankhalid-developers
commented on August 27, 2024
@cszn please mail the training code as i would like to use it in my research. Thanks
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cszn
commented on August 27, 2024
I will release the training code in this month. You may refer to the training code of FFDNet for now since they share the same idea.
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cszn
commented on August 27, 2024
A PyTorch Implementation of SRMD
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oobbppoo
commented on August 27, 2024
@cszn Hi,will you release the training code with Matconvnet version?
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JayMarx
commented on August 27, 2024
@cszn Would you please release the training code? Thanks!
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Fatma-ALbluwi
commented on August 27, 2024
Hi, will you release the training code with Matconvnet version? Thanks!
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cszn
commented on August 27, 2024
new training code based on PyTorch https://github.com/cszn/KAIR
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Related Issues (20)
- data_augmentation function HOT 5
- model_train Not enough input arguments
- issue about test data
- problems when using `res = vl_srmd_matlab(net, input)` HOT 2
- How to generate LR images with different spatially variant degradations? HOT 1
- Errors in the testing process HOT 4
- resulting image
- degradation model
- A little question about network trainning HOT 3
- Question about Experiments on Bicubic Degradation
- fine-tuning or Training with new data sets HOT 2
- Problem about "4.4 Experiments on Spatially Variant Degradation"
- Your Results in New Super-Resolution Benchmarks
- can not find vl_argparse in utilities HOT 1
- how to realise PCA? HOT 2
- how to get net.meta.P HOT 1
- Grid search strategy HOT 2
- Training Datasets HOT 2
- SRMD_non_blind.mat
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