# An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions

Source: [An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions](<https://www.sota2.com/research/paper/68fa65c71e209131cea0c73e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:35:37.665Z

Authors: Sercan T\\"urkmen, Janne Heikkil\\"a

Published: 2019-02-20T09:50:47.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on Cityscapes (val) (mIoU only)](<https://www.sota2.com/research/sota/semantic-segmentation-on-cityscapes-val-miou-only.md>): Semantic segmentation; Cityscapes (val); mIoU: 71.3.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1902.07476>)

[PDF](<https://arxiv.org/pdf/1902.07476.pdf>)