  Assigning a label to each pixel in an image, namely semantic segmentation,
has been an important task in computer vision, and has applications in
autonomous driving, robotic navigation, localization, and scene understanding.
Fully convolutional neural networks have proved to be a successful solution for
the task over the years but most of the work being done focuses primarily on
accuracy. In this paper, we present a computationally efficient approach to
semantic segmentation, while achieving a high mean intersection over union
(mIOU), 70.33% on Cityscapes challenge. The network proposed is capable of
running real-time on mobile devices. In addition, we make our code and model
weights publicly available.
