  Deep CNNs for semantic segmentation have high memory and run time
requirements. Various approaches have been proposed to make CNNs efficient like
grouped, shuffled, depth-wise separable convolutions. We study the
effectiveness of these techniques on a real-time semantic segmentation
architecture like ERFNet for improving run time by over 5X. We apply these
techniques to CNN layers partially or fully and evaluate the testing accuracies
on Cityscapes dataset. We obtain accuracy vs parameters/FLOPs trade offs,
giving accuracy scores for models that can run under specified runtime budgets.
We further propose a novel training procedure which starts out with a dense
convolution but gradually evolves towards a grouped convolution. We show that
our proposed training method and efficient architecture design can improve
accuracies by over 8% with depth wise separable convolutions applied on the
encoder of ERFNet and attaching a light weight decoder. This results in a model
which has a 5X improvement in FLOPs while only suffering a 4% degradation in
accuracy with respect to ERFNet.
