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CycleGAN-Tensorflow

A Tensorflow implementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks using Eager Execution, tf.keras.layers, and tf.data.

Requirements:

  • Tensorflow 1.11

Thanks to the original authors PyTorch implementation for inspiration: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

Project Organization

β”œβ”€β”€ README.md
β”œβ”€β”€ requirements.txt   <- Use `pip install -r requirements.txt`
β”œβ”€β”€ setup.py           <- makes project pip installable (pip install -e .) so src can be imported
β”œβ”€β”€ LICENSE
└── src                <- Source code for use in this project
 Β Β  β”œβ”€β”€ __init__.py    <- Makes src a Python module
    β”‚
    β”œβ”€β”€ train.py       <- Run this to train
    β”‚
    β”œβ”€β”€ test.py        <- Run this to test
    β”‚
 Β Β  β”œβ”€β”€ data           <- Code for downloading or loading data  
 Β Β  β”‚Β Β  β”œβ”€β”€ data.py         <- Dataset class
 Β Β  β”‚Β Β  └── download_data.py
    β”‚
 Β Β  β”œβ”€β”€ models         <- Code for defining the network structure and loss functions
    β”‚Β Β  β”œβ”€β”€ cyclegan.py     <- CycleGAN model class
    β”‚Β Β  β”œβ”€β”€ networks.py
 Β Β  β”‚Β Β  └── losses.py
    β”‚
 Β Β  └── utils          <- Utility files
     Β Β  β”œβ”€β”€ options.py      <- Class for command line options
 Β Β   Β Β  └── image_history_buffer.py

Project organisation based on the cookiecutter data science project template. #cookiecutterdatascience

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A Tensorflow implementation of CycleGAN using Eager Execution, tf.keras.layers, and tf.data.

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