Bachelor Thesis utilizing an artifical neural networks and discrete wavelet transform in order to implement a lossy image compression codec. Project based on paper "Variational image compression with a scale hyperprior"
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ ├── interim <- Intermediate data that has been transformed.
│ └── raw <- The original, immutable data dump.
│
├── external <- Python dependencies that cannot be installed via package manager
│
├── models <- Trained and serialized models, model predictions, or model summaries
│ └── models.csv <- File that translates training session id to the model params
│
├── requirements.txt <- 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
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ └── models <- Scripts to train models and then use trained models to make benchmarks
│ │
│ ├── networks <- module with different neural network architectures
│ ├── gym <- module with neural network training suite
│ ├── benchmarks <- module with neural network benchmarking suite
│ ├── benchmark_model.py
│ ├── train_model.py
│ └── update_config.py
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├── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
│
└── config.ini <- configuration file with the current network params
Project based on the cookiecutter data science project template. #cookiecutterdatascience