A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation

• URL: http://arxiv.org/abs/2404.16266v2
• Date: Mon, 29 Apr 2024 01:39:37 GMT
• Title: A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation
• Authors: Yifan Zhao, Zhenyu Liang, Zhichao Lu, Ran Cheng,
• Abstract summary: Hardware-aware Neural Architecture (HW-NAS) tasks can be treated as black-box multi-objective optimization problems (MOPs) We introduce a tailored streamline to transform the task of HW-NAS for real-time semantic segmentation into standard MOPs. We present a benchmark test suite, CitySeg/MOP, fifteen MOPs derived from the Cityscapes dataset.
• Score: 22.707825213534125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As one of the emerging challenges in Automated Machine Learning, the Hardware-aware Neural Architecture Search (HW-NAS) tasks can be treated as black-box multi-objective optimization problems (MOPs). An important application of HW-NAS is real-time semantic segmentation, which plays a pivotal role in autonomous driving scenarios. The HW-NAS for real-time semantic segmentation inherently needs to balance multiple optimization objectives, including model accuracy, inference speed, and hardware-specific considerations. Despite its importance, benchmarks have yet to be developed to frame such a challenging task as multi-objective optimization. To bridge the gap, we introduce a tailored streamline to transform the task of HW-NAS for real-time semantic segmentation into standard MOPs. Building upon the streamline, we present a benchmark test suite, CitySeg/MOP, comprising fifteen MOPs derived from the Cityscapes dataset. The CitySeg/MOP test suite is integrated into the EvoXBench platform to provide seamless interfaces with various programming languages (e.g., Python and MATLAB) for instant fitness evaluations. We comprehensively assessed the CitySeg/MOP test suite on various multi-objective evolutionary algorithms, showcasing its versatility and practicality. Source codes are available at https://github.com/EMI-Group/evoxbench.

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