Given a dataset of 2D dashboard camera images, State Farm is challenging Kagglers to classify each driver's behavior. Are they driving attentively, wearing their seatbelt, or taking a selfie with their friends in the backseat?
Check Notebook for an overview of the implementation
- Developed by the Visual Geometry Group at the University of Oxford.
- It is class of very deep Convolutional Networks for large-scale Visual Recognition tasks.
- Won the first and the second places in the localisation and classification tasks respectively at the ImageNet ILSVRC-2014 contest.
- Developed by Microsoft Research.
- Won 1sth place in classification tasks at the ImageNet ILSVRC-2015 contest.
- The residual learning framework makes is easier to optimize much deeper networks, while maintaining a relativaly low complexity.
- Regular neural networks tend to decrease in accuracy at large depths, due to information degradation.
- Residual learning introduce skip connections, which allow information flow into the deeper layers and enable us to have deeper networks with better accuracy.
