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An end-to-end computer vision project for wildfire smoke detection.
make dirs
to create the missing parts of the directory structure described below.make virtualenv
to create a python virtual environment. Skip if using conda or some other env manager.
source env/bin/activate
to activate the virtualenv.make requirements
to install required python packages.data/raw
.dvc add data/raw
dvc repro
or make reproduce
make pre-commit-install
├── LICENSE
├── Makefile <- Makefile with commands like `make dirs` or `make clean`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ ├── raw.dvc <- DVC file that tracks the raw data
│ └── raw <- The original, immutable data dump
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. A naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│ └── metrics.txt <- Relevant metrics after evaluating the model.
│ └── training_metrics.txt <- Relevant metrics from training the model.
│
├── 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
│ │ ├── great_expectations <- Folder containing data integrity check files
│ │ ├── make_dataset.py
│ │ └── data_validation.py <- Script to run data integrity checks
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ predictions
│ ├── predict_model.py
│ └── train_model.py
├── dvc.lock <- The version definition of each dependency, stage, and output from the
│ data pipeline.
└── dvc.yaml <- Defining the data pipeline stages, dependencies, and outputs.
Project based on the cookiecutter data science project template. #cookiecutterdatascience
To create a project like this, just go to https://dagshub.com/repo/create and select the Cookiecutter DVC project template.
Made with 🐶 by DAGsHub.
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