Comments (5)
Hello! I use this code and the parameters in the paper to train on the GOPRO dataset. The results on the test set are lower than the results in the paper. If I want to accurately reproduce the results in the paper, what should I pay attention to?
Hi, What PSNR and SSIM have you achieved?
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Hello! I use this code and the parameters in the paper to train on the GOPRO dataset. The results on the test set are lower than the results in the paper. If I want to accurately reproduce the results in the paper, what should I pay attention to?
Hi, What PSNR and SSIM have you achieved?
PSNR 29.04 SSIM 0.870
from estrnn.
Hello! I use this code and the parameters in the paper to train on the GOPRO dataset. The results on the test set are lower than the results in the paper. If I want to accurately reproduce the results in the paper, what should I pay attention to?
Hi, What PSNR and SSIM have you achieved?
PSNR 29.04 SSIM 0.870
Hi, I use this code and the parameters in the paper to train on the GOPRO dataset. The LR=e-4, Loss=MSE, n_blocks=9, Channels=16X5=80, batchsize=4 but in the original paper, the batchsize = 8. The results on the test set are also lower than the results in the paper. My results: PSNR =29.1654 SSMI=0.8731. But in the original paper, ESTRNN(B9C80) model‘s results :PSNR = 30.79 ,SSMI=0.9016
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The code has been modified based on the previous version.
Try to only change n_blocks=9 and keep the others as default.
Also, it is worth noting that the calculation of metrics in the paper for all models is patch-based.
This version is image-based (the performance for all models will vary accordingly).
from estrnn.
The code has been modified based on the previous version.
Try to only change n_blocks=9 and keep the others as default.
Also, it is worth noting that the calculation of metrics in the paper for all models is patch-based.
This version is image-based (the performance for all models will vary accordingly).
Thank you for your reply. Due to my personal hardware environment is limited (single card 1080Ti, 12G), I set batchsize = 4, and change n_blocks=9, other parameters remain unchanged, the training results: PSNR = 30.72, SSMI = 0.9094. Considering that I reduced batchsize , the result was 0.05 lower than the paper. All in all, thank you very much for your work. I am reproducing the results of your BSD dataset. Thanks again
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Related Issues (20)
- about dataset HOT 3
- About pretrained model HOT 3
- About training time of BSD dataset HOT 2
- How to use with custom input ?? HOT 2
- NotImplementedError: There were no tensor arguments to this function HOT 6
- Are these artifacts expected? HOT 4
- Training problems HOT 2
- About training HOT 1
- Cannot download BSD dataset HOT 2
- Using pre-trained model to inference , the results is strange HOT 6
- About dataset HOT 1
- Difference of GMACs HOT 1
- Cannot download BSD HOT 8
- Issue on downloading BSD database HOT 4
- CPU HOT 2
- About Dataset HOT 1
- When i want to train in my own dataset to deblur, the value of loss run into nan HOT 1
- About test datasets settings HOT 1
- Is it possible to provide the full BSD with a RAW Baidu Cloud download link?
- Test Output to video
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