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Driver Posture Classification

This is a PyTorch code for Driver Posture Classification task. We use the AUC Distracted Driver Dataset. The dataset was captured to develop the state-of-the-art in detection of distracted drivers. Here are some samples from the dataset:

The task is to classify an image to one of these pre-defined categories, namely "Drive Safe", "Talk Passenger", "Text Right", "Drink", and etc. We use a pretrained resnet34 model to achieve comparable performance with the orignal paper Real-time Distracted Driver Posture Classification. The classification accuracy is about 95%.

Usage

Requirements

  • python 3.5+
  • pytorch 0.4
  • visdom (optional)

Steps

  1. Download the dataset and its training and testing splits (train.csv and test.csv). Put them in a directory together.

  2. Clone the repository

    git clone https://github.com/husencd/DriverPostureClassification.git

    cd DriverPostureClassification

  3. Download the resnet model pretrained on ImageNet from pytorch official model urls.

    cd pretrained_models

    sh download.sh

  4. Now you can train/fine-tune the model

    cd ..

    python main.py [--model resnet] [--model_depth 34]

    If you want to monitor the training process, use visdom

    python -m visdom.server

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