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Question about data augmentation #5
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@yuanshuai220 Hi, I am reproducing the paper result recently. The training data provided is a tiny sample, you can collect the BSD200, T91 and general100 total 391 images as your training dataset using generate_train_lap_pry.m. I get the training datasets size of (11712, 1, 32, 32). After 200 epochs, I get average psnr 31.32 on Set5 for 4X. After several test, I find that the training datasets play a important roles in resluts. The more richer training datasets is, the better result you will get. Meanwhile data augmentation is also important, you can add scaling, rotation and flipping function in generate_train_lap_pry.m script by yourself. |
@CasdDesnDR I agree with you. If the trianing data is not enough, the nerual network will overfit with the training set. So the performance on test set is not good. I will add rotation and flipping in the |
@yuanshuai220 @CasdDesnDR Please refer https://github.com/twtygqyy/pytorch-SRResNet/blob/master/data/generate_train_srresnet.m for adding flipping and rotation |
@twtygqyy Hi, Thank you for sharing your code. I want to know why you convert RGB images into YCbCr colour space and only use the Y channel information. How about the results directly using all RGB channels? |
Hi @baiyancheng20, I followed the LapSRN paper for the implementation. Actually, you can check https://github.com/twtygqyy/pytorch-SRResNet which I used RGB image as inputs. |
@twtygqyy Hi, Thank you for sharing your LAPSRN code. I took your pytorch code from Git-hub and executed it. It works only for grayscale images. |
@sriprabhar Hi, I understand that you tried to overfit the network on a small dataset. How is the loss looks like in your training? Did it converge well? |
Thanks for your response.
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Hi, |
@sriprabhar Hi, I think the way how you generate the h5 is correct, while you will probably get to many small patches out of 1080x1080 because of the huge size. (3GB is not that big, TBH : ) ) A quick way to solve this is to change the stride when you run the matlab code for generation. |
@sriprabhar also the result you plotted makes sense to me. Cause the image you tested might not be the exactly same one as you used in training. Grab one image from h5 which you used for training, and see if the result looks better. |
Thank you for your response, I will give the training patch and try. |
Hi @sriprabhar you can have a look at section 5.1 in this paper Fast and Accurate Image Super-Resolution Using A Combined Loss. They compared the difference between training with Y and RGB for SR. |
Thanks for your code, it helps me a lot. But I have some questions about data augmentation. In the
generate_train_lap_pry.m
, you only used downsizing to make more training data. While in the paper, the author augments the training data in three ways, scaling, rotation and flipping. Your performance is better than the paper, but your training data only has 7488 examples. I'm confused about it.The text was updated successfully, but these errors were encountered: