Comments (3)
We did not encountered such problem. May be try reducing learning rate, batch size.
from bipnet.
Hi, thanks for your wonderful work. I am also working on BurstSR and your codes have helped me a lot.
While I have some problems when I try to fine-tune an SR model on the BurstSR dataset. I firstly train the SR model on the Synthetic dataset and everything is OK. Then I fine-tune this model on the BurstSR dataset. The training loss keeps decreasing while the validation PSNR only grows at the beginning and drops gradually after it achieves the best result. It seems that the training set is overfitted.
Have you met the same problem? I would appreciate it a lot if you could help me figure this out.
Thanks.
Have you soveld this problem? Thanks!
from bipnet.
Have you solved this problem? Thanks
from bipnet.
Related Issues (20)
- The problem of test code and PSNR in Grayscale dataset HOT 1
- Color denoising metrics HOT 1
- About testing in low-light enhancement
- BursrSR real dataset training HOT 1
- Loss nan for BurstSR Track 2 training HOT 2
- No training code for denoting
- The Results on Track2 of Burst SR Cannot be Reproduced HOT 1
- About the code for the network
- Question for SR burst training
- Can not download trained model in Color Denoising
- Model class methods need to be overrided caused by Pytorch Lightning HOT 2
- Can't find grayscale_denoising_training.py under Burst de-noising file HOT 1
- There is no training code for burst denoising HOT 1
- About batch size in training HOT 2
- About training setting of real world BurstSR dataset HOT 3
- no training code for burst denoising and low light enhancement HOT 1
- The pre-trained model gets no paper points HOT 1
- New Super-Resolution Benchmarks
- The results in Grayscale dataset are 3dB less than the PSNR in paper HOT 2
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from bipnet.