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style transfer code for generating stylized images using content and style loss from different layers, from different loss networks (vgg-16, vgg-19, inception-v1, inception-v2, inception-v3 trained on imagenet, inception-v3 trained on openimages, inception-v4

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singlasahil14/style-transfer

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style-transfer

style transfer code for generating stylized images using content and style loss from different layers, from different loss networks (vgg-16, vgg-19, inception-v1, inception-v2, inception-v3 trained on imagenet, inception-v3 trained on openimages, inception-v4)

Requirements

tensorflow r1.3

Documentation

Training Style Transfer Networks

./setup.sh
python fast_style.py --test-path data/content/ --train-path data/train/ --result-path test-fast/ --style-path data/style/wave.jpg

To access all command line arguments, run python fast_style.py --help

Stylizing a single image

python slow_style.py --style-path data/style/wave.jpg --content-path data/content/stata.jpg --result-path test-slow/

To access all command line arguments, run python slow_style.py --help

Printing model layers with tensor sizes

python print_model_layers.py --network inception-v4

To access all command line arguments, run python print_model_layers.py --help

Roadmap

  • Add explanatory comments
  • Expose more command-line arguments

Contributing

Please feel free to:

  • Create an issue
  • Open a Pull Request
  • Join the gitter chat
  • Share your success stories!

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style transfer code for generating stylized images using content and style loss from different layers, from different loss networks (vgg-16, vgg-19, inception-v1, inception-v2, inception-v3 trained on imagenet, inception-v3 trained on openimages, inception-v4

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