Comments (5)
Hi hermosayhl, firstly thank you for your interest in our work! The RGB variant of the low-level network is similar to the RAW, but you are correct that there are some key differences (see supplementary material of the paper). I will upload an RGB network for you.
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Hi hermosayhl, firstly thank you for your interest in our work! The RGB variant of the low-level network is similar to the RAW, but you are correct that there are some key differences (see supplementary material of the paper). I will upload an RGB network for you.
Thanks for your reply!
I have managed to run the codes with RGB image pairs although some hyber parameters are changed. As long as one can dedicated himself to the codes.
I'm not sure whether my changed version is correct. However, the initial trained results are pleasing so far.
After I finished training, I may upload the changed codes and pretrained models for all friends.
from curl.
Hi hermosayhl, firstly thank you for your interest in our work! The RGB variant of the low-level network is similar to the RAW, but you are correct that there are some key differences (see supplementary material of the paper). I will upload an RGB network for you.
Listed below are some of the images for testing:
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Good to hear that you got the RBG variant working. And thank you for contributing the code and trained models to the community. To clarify, in your output images, from left-to-right: the left most image is the input. Which of the other two images are the prediction and ground-truth?
from curl.
Good to hear that you got the RBG variant working. And thank you for contributing the code and trained models to the community. To clarify, in your output images, from left-to-right: the left most image is the input. Which of the other two images are the prediction and ground-truth?
Firstly
the images produced are: [input image, label image, enhanced image].
Secondly
The training process is coming to the end. I will change the code and trained models as soon as possible.
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Related Issues (20)
- how to train the raw data(unit16) to jpg without quality loss HOT 1
- FileNotFoundError: [Errno 2] No such file or directory: './pretrained_models/curl_validpsnr_23.073045286204017_validloss_0.0701291635632515_testpsnr_23.584083321292365_testloss_0.061363041400909424_epoch_510_model.pt' HOT 2
- it seems the batch size must be 1? HOT 1
- Problems when using images of size 256x256 HOT 2
- Lightroom image exporting HOT 2
- hi,is it normal for loss HOT 1
- RuntimeError: Given groups=1, weight of size [16, 3, 3, 3], expected input[1, 4, 343, 514] to have 3 channels, but got 4 channels instead HOT 1
- ZeroDivisionError: float division by zero HOT 2
- The weights and the architecture are not the same HOT 1
- running code without CUDA HOT 19
- Batch size > 1 version HOT 2
- Inference Pretrained Model with CPP scratch HOT 1
- inference of test images HOT 1
- Bigger Batch size HOT 1
- Can't reproduce results HOT 1
- Training/Validation/Test splits link is down HOT 1
- Doubt about results HOT 4
- 请教一下这块实现的原理 HOT 2
- dim HOT 1
- Pretrained model results
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