Comments (14)
Interesting, I've been having similar ideas. In particular, providing multiple files to generate.py to exclude model reloading on every iteration. But i'd prefer expanding the glob
since it allows more flexibility in case I just want a fraction of files in a directory. Good job on using try / catch to handle corrupted files nicely. I didn't experience crashes since I was always using the COCO dataset, but for other cases it may be handy. The extra padding hack might also be useful for some people. All in all, good job and good luck with further improvements!
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I implemented the try.catch thing because I had a corrupted image in the COCO dataset and it crashed my training..
We could use a regex to select files in a directory also.
I don't like having so many forks everywhere, maybe we should merge our projects one time or another
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Hmm, I've had no problems with COCO dataset.
Expanding glob
would be more familiar to an average user than regex.
Merging would result in some conflicts in generate.py
. What's the problem with having separate forks?
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throw in the end of train.py a line to upload the model to the sharing repo 💯
jokes aside really nice work guys, can't wait to get a gpu able to train some 512px models.
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Ahaha, that's a good one! But that way the repo would be quickly filled up with all sorts of #*@! :)
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@6o6o Ok I'm not a pythoner I didn't know glob :) .
I just added tiling in my fork
For example 2x2 tiling :
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So how big is the resolution on tour card? This implementation seems a lot less memory hungry than the others.
Mind sharing the storm model on the model report? It's quite cool.
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I have a GTX 980 4Gb (on Windows 10, might be better on Ubuntu) and can process up to 1.1 MPx images (something like 1400x730 in 16/9 format).
What is the storm model ?
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I mean the model you used for that blue picture.
Isn't it based on a storm ? It's impressive how little artifacts there are,tiling seems to work really well.
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Yes ok I course but I'm gonna train a better one next week
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Yes it's a storm sorry for not answering. The tiling is good for some images but not that good because it can't recognize shapes that are present in the whole image : in the above example at the right you can see two interpretation of the same pile of ground
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For thoses interested in the video ! #70
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How are you combining the tiles?
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Just a blending of RGB channels. I thought about coding some blending preserving the edges, but I was pleased with the result of a basic blending !
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Related Issues (20)
- kanagawa
- the result is dark when it is used for super resolution
- Is there a style size?
- why do you set batch_size equal 1 HOT 1
- OutOfMemoryError when generate on Azure ND6 VM
- RuntimeError: CUDA environment is not correctly set up
- cupy.cuda.compiler.CompileException: nvrtc: error: failed to load builtins HOT 5
- LC_RPATH @executable_path error or libnvrtc-builtins.dylib error HOT 5
- How much is the proper training epoch?
- hello! can you code Fast Neural Style Transfer with Arbitrary Style ? HOT 6
- Memory error without GPU. HOT 1
- ValueError: test argument is not supported anymore. Use chainer.using_config HOT 5
- error in run train.py
- Style transfer between any two images - iOS App needs beta tester HOT 6
- volatile argument is not supported anymore. Use chainer.using_config HOT 10
- Output size few pixels smaller
- Artx - iOS App that transfer styles between any two images HOT 4
- how to download vgg16 and setup it ?
- error in sh setup_model.sh HOT 1
- RuntimeError: CUDA environment is not correctly set up HOT 1
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