nothinglo / Deep-Photo-Enhancer

TensorFlow implementation of the CVPR 2018 spotlight paper, Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs
MIT License
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Test in my own dataset got 'blue' result. #41

Closed JustinhoCHN closed 5 years ago

JustinhoCHN commented 5 years ago

Hello @nothinglo , I've trained the model with unsupervised hdr dataset for about 100 epochs, if I use the test image from fivek dataset, I got pretty good result: (GT/Predicted) 5k1 5k2

But when I test it with my own dataset, the result looks like this: my_result

Some regions in those test images are changed to blue, I suspected that there're so many blue sky in the training dataset (both in input and label_HDR).

And I tested these images in the demo website, they look like this: demo_result

Here're ground truth images: gt

The next step I want to improve the result will be adding more own dataset to finetune the model. Anyone has any idea about why it become so blue and how to solve it? Thanks in advanced.

By the way, here's my loss curve: #40

liulizhou commented 5 years ago

have you solved the problem ?

JustinhoCHN commented 5 years ago

@liulizhou nope, you are the first reply.... maybe it's a dataset domain missmatch problem.

liulizhou commented 5 years ago

@JustinhoCHN I also train the program, now 130 epoch. Next i will test it and maybe i will have the same problem.

liulizhou commented 5 years ago

@JustinhoCHN now i have not test it, but your result may be currect. I think you should check the ouput channel, if the output is RGB, you should change it to BGR and then show it with opencv (the result is like this)... maybe it's not.

JustinhoCHN commented 5 years ago

@liulizhou if you done training, you can use my image to test it, one of my friend's result like this: qq 20190125152844

xuanzhangyang commented 5 years ago

@JustinhoCHN Yes, it‘s my result^v^...

liulizhou commented 5 years ago

Hi @JustinhoCHN @xuanzhangyang i test two own images and the result is like this. the first result and ground truth. a-gittest01 gittest01 the second result and ground truth. a-frame30_test frame30_test

JustinhoCHN commented 5 years ago

@liulizhou seems faces turn to yellow, white background or sky turn to blue, and bringing artifacts.

liulizhou commented 5 years ago

@JustinhoCHN Yes, we should remove the artifacts. i have no idea why this happen. I test DPED and HDRNET, and the results also bring artifacts.

ExplorerLGD commented 5 years ago

我发现在物体的边缘位置,会出现一些模糊,很影响效果

xiaozhi2015 commented 5 years ago

Here is my result. image

I'd like to ask, do you know how to expand the inference dimension to 2048? @JustinhoCHN @liulizhou I'll appreciate it very much for your answers.

nothinglo commented 5 years ago

@JustinhoCHN I do think it is dataset domain mismatch problem. @xiaozhi2015 I just uploaded the inference models and the code I used in my demo website.

xiaozhi2015 commented 5 years ago

Much thx for your great work! @nothinglo

TriLoo commented 5 years ago

Have you resolved this problem? i.e., face turned to yellow, and white background turned to blue ? @liulizhou