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Existing algorithms even though are accurate but they do not focus\non utilizing the parameters of neural network efficiently. As a result they are\nhuge in terms of parameters and number of operations; hence slow too. In this\npaper, we propose a novel deep neural network architecture which allows it to\nlearn without any significant increase in number of parameters. Our network\nuses only 11.5 million parameters and 21.2 GFLOPs for processing an image of\nresolution 3x640x360. It gives state-of-the-art performance on CamVid and\ncomparable results on Cityscapes dataset. 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