Comments (4)
Hello.
Do you mean that you want to reproduce the result on benchmark datasets (Set5, Set14, Urban100, B100) from the paper?
We use same models for DIV2K and benchmark evaluations, so you can easily get the results.
Please see these scripts!
If I misunderstood your question, let me know.
Thank you.
from edsr-pytorch.
Hello, Thank for your reply. Paper's benchmark evaluations are only on the Y channel. My understanding is first transfer bechemark datasets to the Ycbcr color space, and use only Y channel as input of network resulting in output of one channel. But your trained model's input are 3 channel. So is it means that you evaluate benchmarck datasets using the RGB image as input and transfering RGB output to Ycbcr for evaluating? I don't know if I make myself clear? Thank you very much!
from edsr-pytorch.
You understood it correctly.
We used RGB inputs and transformed outputs to YCbCr for evaluation.
from edsr-pytorch.
Ok, Thank you very much. Your work is great!
from edsr-pytorch.
Related Issues (20)
- About test HOT 3
- train和test中的指标不同
- PSNR is nan when test DIV2K data HOT 2
- Why does EDSR enlarge the image twice and only low pixel images work well? HOT 3
- Mean Shift Function for Gray Image dataset
- TypeError:list indices must be integers or slices, not tuple
- transfer learning / finetune HOT 1
- Why not use torchvision.transforms HOT 1
- where's baseline models? HOT 1
- PSNR and SSIM
- It is not working Correctly!
- ValueError: num_samples should be a positive integer value, but got num_samples=0 HOT 4
- ValueError: x and y must have same first dimension, but have shapes (0,) and (1,)
- Denoising model in "Residual Dense Network for Image Restoration"
- Not changing the speed by changing the amount of data
- 环境配置 HOT 4
- AttributeError: module 'torch.backends' has no attribute 'mps' HOT 1
- 训练数据集
- 怎么使用自定义的数据集训练? HOT 10
- AttributeError: '_MSDataLoaderIter' object has no attribute '_put_indices'
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from edsr-pytorch.