EdgeVisionBench

A Benchmark of Evolving Input Domains for Vision Applications at Edge

Conference paper (2023)

Authors

Qinglong Zhang Beijing Institute of Technology
Rui Han Beijing Institute of Technology
Chi Harold Liu Beijing Institute of Technology
Guoren Wang Beijing Institute of Technology
Lydia Y. Chen Data-Intensive Systems - EEMCS
Research Group
Data-Intensive Systems (EEMCS) (TU Delft)
Copyright: © 2023 Qinglong Zhang, Rui Han, Chi Harold Liu, Guoren Wang, Lydia Y. Chen
DOI
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https://doi.org/10.1109/ICDE55515.2023.00288
Benchmark Edge computing Evolving domains Vision applications
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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Data-Intensive Systems

Abstract

Vision applications powered by deep neural networks (DNNs) are widely deployed on edge devices and solve the learning tasks of incoming data streams whose class label and input feature continuously evolve, known as domain shift. Despite its prominent presence in real-world edge scenarios, existing benchmarks used by domain adaptation methods overlook evolving domains and under represent their shifts in label and feature distributions. To address this gap, we present EdgeVisionBench, a benchmark seeking to generate evolving domains of various types and reflect their realistic label and feature shifts encountered by edge-based vision applications. To facilitate evaluating domain adaptation methods on edge devices, we provide an open-source package that automates workload generation, contains popular DNN models and compression techniques, and standardizes evaluations with interactive interfaces. Code and datasets are available at https://github.com/LINC-BIT/EdgeVisionBench.