Facemask detection

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.

Project Organization

├── README.md                    <- The top-level README for developers using this project.
│
├── GETTING_STARTED.rst          <- About startin app
│
├── 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
│
├── 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

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Project based on the cookiecutter data science project template. #cookiecutterdatascience