# FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation

Source: [FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6ee21e209131cea42a17>)

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

Data updated: 2025-10-26T01:08:40.604Z

Authors: Zhuoran Xiong, Marihan Amein, Olivier Therrien, Warren J. Gross, Brett H. Meyer

Published: 2023-03-28T21:44:48.000Z

## Abstract

We present FMAS, a fast multi-objective neural architecture search framework for semantic segmentation. FMAS subsamples the structure and pre-trained parameters of DeepLabV3+, without fine-tuning, dramatically reducing training time during search. To further reduce candidate evaluation time, we use a subset of the validation dataset during the search. Only the final, Pareto non-dominated, candidates are ultimately fine-tuned using the complete training set. We evaluate FMAS by searching for models that effectively trade accuracy and computational cost on the PASCAL VOC 2012 dataset. FMAS finds competitive designs quickly, e.g., taking just 0.5 GPU days to discover a DeepLabV3+ variant that reduces FLOPs and parameters by 10$\\%$ and 20$\\%$ respectively, for less than 3$\\%$ increased error. We also search on an edge device called GAP8 and use its latency as the metric. FMAS is capable of finding 2.2$\\times$ faster network with 7.61$\\%$ MIoU loss.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.16322>)

[PDF](<https://arxiv.org/pdf/2303.16322.pdf>)