We suggest a 2D image segmentation model based on UNET algorithm to segment images with blossoming apple tree
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials (if many user development),
│ and a short `_` delimited description, e.g.
│ `1.0-jqp-initial_data_exploration.ipynb`.
│
├── 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.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
├── test-requirements.txt <- The requirements file for the test environment
│
├── setup.py <- makes project pip installable (pip install -e .) so blossom can be imported
├── blossom <- Source code for use in this project.
│ ├── __init__.py <- Makes blossom a Python module
│ │
│ ├── dataset <- 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 make predictions
│ │ └── deep_api.py <- Main script for the integration with DEEP API
│ │
│ └── tests <- Scripts to perfrom code testing
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Project based on the cookiecutter data science project template. #cookiecutterdatascience
