Improve the README for applying and training models.
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@@ -19,8 +19,6 @@ The catch? The neural network is hallucinating details based on its training fro
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|Python Version| |License Type| |Project Stars|
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----
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1. Examples & Usage
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===================
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@@ -36,29 +34,37 @@ The default is to use ``--device=cpu``, if you have NVIDIA card setup with CUDA
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.. code:: bash
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# Run the super-resolution script for one or more images.
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# Run the super-resolution script for one image.
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python3 enhance.py example.png
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# Display output image that has `_enhanced.png` suffix.
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open example_enhanced.png
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# Also process multiple files with a single run.
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python3 enhance.py file1.jpg file2.jpg
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# Display output images that were given `_ne4x.png` suffix.
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open *_ne4x.png
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1.b) Training Super-Resolution
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------------------------------
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Pre-trained models are provided in the GitHub releases. Training your own is a delicate process that may require you to pick parameters based on your image dataset.
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.. code:: bash
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rm -f ne4x.pkl.bz2
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# Remove the model file as don't want to reload the data to fine-tune it.
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rm -f ne4x*.pkl.bz2
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python3.4 enhance.py --train --epochs=25 \
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--scales=2 --perceptual-layer=conv2_2 \
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--generator-block=16 --generator-filters=128 \
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--smoothness-weight=1e7 --adversary-weight=0.0
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# Pre-train the model using perceptual loss from paper [1] below.
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python3.4 enhance.py --train --scales=2 --epochs=50 \
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--perceptual-layer=conv2_2 --smoothness-weight=1e7 --adversary-weight=0.0 \
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--generator-blocks=4 --generator-filters=64
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# Train the model using an adversarial setup based on [4] below.
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python3.4 enhance.py --train --scales=2 --epochs=250 \
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--perceptual-layer=conv5_2 --smoothness-weight=2e4 --adversary-weight=1e3 \
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--generator-start=5 --discriminator-start=0 --adversarial-start=5 \
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--discriminator-size=64
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python3.4 enhance.py --train --epochs=250 \
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--scales=2 --perceptual-layer=conv5_2 \
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--smoothness-weight=5e4 --adversary-weight=2e2 \
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--generator-start=1 --discriminator-start=0 --adversarial-start=1
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.. image:: docs/BankLobby_example.gif
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+1
-1
@@ -504,7 +504,7 @@ if __name__ == "__main__":
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for filename in args.files:
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print(filename)
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out = enhancer.process(scipy.ndimage.imread(filename, mode='RGB'))
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out.save(os.path.splitext(filename)[0]+'_ne%ix-%s.png'%(2**args.scales, args.model))
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out.save(os.path.splitext(filename)[0]+'_ne%ix.png'%(2**args.scales))
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if args.files:
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print(ansi.ENDC)
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