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juglab / n2v

This is the implementation of Noise2Void training.
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A Simple Question on Training Data #92

Closed JohnCraigPublic closed 4 years ago

JohnCraigPublic commented 4 years ago

In your example, you use one image for training and one image for validation. But in a real application, to get best performance, one should train on hundreds or thousands of images, correct?

JohnCraigPublic commented 4 years ago

I see, looking at the size of your data for the BSD68 example, that you use not just a few images, but many -- as I expected. So, I think it answers my question -- so closing the question...

tibuch commented 4 years ago

Eventually it depends on how much information one uses to train the network. If you have many small images or one gigantic image does not matter much. For training these images get split up into patches anyway. But in general you want to use as many pixels/information/images as possible to allow your network to generalize better over all possible images of your dataset.

JohnCraigPublic commented 4 years ago

OK, so not unlike any Deep Learning system. Just wanted to be sure. Thank you!