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 1.11.0
- Python 3.6
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.yml <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
└── src <- Source code for use in this project.
├── __init__.py <- Makes src a Python module
│
├── train.py <- Run this to train.
│
├── test.py <- Run this to test.
│
├── pipeline <- Code for downloading or loading data
│ ├── data.py
│ └── download_data.py
│
├── options <- Files for command line options
│ └── base_options.py
│
├── models <- Code for defining the network structure and loss functions
│ ├── network.py
│ └── losses.py
│
└── utils <- Utility files, including scripts for visualisation
Project based on the cookiecutter data science project template. #cookiecutterdatascience