/Driver-Distraction-Detection-using-ML

Driver Distraction Detection using Machine Learning

Primary LanguagePython

Driver-Distraction-Detection-using-ML

--------------------------------------------------------Important-------------------------------------------------------------

Dataset Source

The dataset will be downloaded from kaggle's website.

Dataset Preparation

  1. Download the images and drivers list into the "dataset" folder:
  2. Unzip both into the "dataset" folder so it looks like this:
  • distracted-drivers/
    • dataset/
      • imgs/
        • train/
        • test/
      • driver_imgs_list.csv

Running

This project uses [TensorFlow]. To run the code locally simply install the dependencies and run python main.py.

Model Development

  1. Tune your learning rate. If the loss diverges, the learning rate is probably too high. If it never learns, it might be too low.

  2. Good initialization is important. The initial values of weights can have a significant impact on learning. In general, you want the weights initialized based on the input and/or output dimensions to the layer (see Glorot or He initialization).

  3. Early stopping can help prevent overfitting but good regularization is also beneficial. L1 and L2 can be hard to tune but batch normalization and dropout are usually much easier to work with.