Deep learning approach for wood classification using custom W_IMCNN model
├── 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.
│ └── raw <- The original, immutable data dump.
│
├── checkpoints <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── 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
│ └── lighting_logs <- Generated logs from lighting module
│
├── 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
│ │
│ ├── config <- Hydra configuration
│ │ ├── config.yaml <- Project configuration
│ │ ├── data <- Configuration for data modules
│ │ ├── models <- Configuration for models
│ │ └── modules <- Configuration for lighting modules
│ │
│ └── wood-classification <- Source code
│ ├── data <- Code for data modules
│ ├── models <- Code for models
│ └── modules <- Code for lighting modules
│
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience