python videos-to-images.py # converts videos to frames
python label.ipynb # create label dataframe
python split.ipynb # split into training and validation, oversample, ...

├── dfs
│   |── labels.pkl
│   |── master_training_no_temp.pkl
│   |── master_training_temp.pkl
│   |── testing.pkl
│   |── training_no_temp_os.pkl
│   |── training_no_temp.pkl
│   |── training_temp.pkl
│   |── validation_no_temp_os.pkl
│   |── validation_no_temp.pkl
│   └── validation_temp.pkl
├── images
├── predictions
│   |── BEST.csv # best prediction on test set, reaching 80.0% accuracy
│   └── other predictions
├── report
│   |── Assignement2_av3023_ha2605.pdf
│   └── models.png
├── surgery.videos.hernia
├── weights # all weights
├── label.ipynb # create label df
├── models.py # all models
├── README.md
├── split.ipynb # split data into training, validation and testing set with/without oversampling and temporal organization
├── train-predict.py # script to train and make predictions on test set
├── train-predict.sh # template to train and make predictions on test set
├── train-validate.py # script to train and validate model with argparse
├── train-validate.sh # template to train and validate model with argparse
├── utils.py # all useful functions for preprocessing, training, predicting, ...
├── video.phase.trainingData.clean.StudentVersion.csv # initial labeling data
└── videos-to-images.py # script to convert and save videos to images