Comments (4)
Hi,
The initial lr is 1e-4, decay at 7-13-20-28-36 epoches. We usually train 20 epoches for a CUFED5 model.
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The initial lr is 1e-4, decay at 7-13-20-28-36 epoches. We usually train 20 epoches for a CUFED5 model.
Is your description really complete and correct? You mean you usually train 20 epoches for a CUFED5 model,but your learning rate strategy continues to 36 epoches. By the way ,according to the paper, training should have two stages. Thank you!
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Hi,
We found the model converged within 15~16 epochs for CUFED5 (with --num_patch=300). So we used the max epoch of 20 in our experiments, though we initially set it to 36.
For the concern about two-stage training, we only employ SRA on CameraFusion dataset, as we wanna obtain 8K images but we do not have ground truth. For CUFED5, the training is one-stage as we can obtain the GT HR images in the datasets.
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Related Issues (20)
- How much memory does (train)test need(for CUFED5)? HOT 1
- question on dcsr.py HOT 5
- Question on patch_size HOT 1
- About 4x start HOT 5
- Hi,
- Question about 4X SR on CUFED5 HOT 7
- Sorry I'm asking stupid questions again HOT 3
- About patch_select HOT 2
- Pretrained model on CUFED5 HOT 2
- About CameraFusion dataset HOT 4
- train,val and test
- Hi,Can you provide the testset CUFED5 for the pre-train model?
- RuntimeError: CUDA error: device-side assert triggered HOT 11
- About quick start on 4k start HOT 2
- Question on evaluation in the main paper HOT 2
- Question on network architecture HOT 4
- Question on alignment.py HOT 4
- Question on SRA Loss implementation HOT 2
- cannot reproduce 8k result as shown in the paper HOT 5
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