YOLOv5 model trained on a custom dataset to detect human faces and label them according to the face masks: mask on, off or weared incorrectly.
├── README.md <- The top-level README for developers using this project.
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├── GETTING_STARTED.rst <- About startin app
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├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
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├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
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├── Facemask_checking <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to handle data
│ │ └── create_yaml.py <- Creates .yaml file required for yolo training
│ │ └── get_annotations.py <- Read annotations as dataframe
│ │ └── get_labels.py <- Read labels and add to dataframe
│ │ └── parse_data.py <- Data parser
│ │ └── split_data.py <- Splits data into training and validation dirs
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── choose_loading.py <- Used to choose between training and loading model
│ │ └── choose_mode.py <- Choose mode to use
│ │ └── cv2_handling.py <- Cv2 script to detect masks on a video/camera
│ │ └── get_latest_weights.py <- Load latest weights
│ │ └── image_mode.py <- Script to detect masks on an image
│ │
│ │
│ ├── model <- Scripts to train/load model
│ │ │── train_model.py <- Train new model
│ │ ├── load_model.py <- Load existing weights to the model
│ │
└── tox.ini <- tox file with settings for running tox
Project based on the cookiecutter data science project template. #cookiecutterdatascience