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)
- 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
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- 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,)
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- 环境配置 HOT 4
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- 训练数据集
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- There is an obvious difference between the results of reproduction and the paper
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from edsr-pytorch.