Compact Neural Architecture Search for Local Climate Zones Classification

Abstract

State-of-the-art Computer Vision models achieve impressive performance but with an increasing complexity. Great advances have been made towards automatic model design, but accounting for model performance and low complexity is still an open challenge. In this study, we propose a neural architecture search strategy for high performance low complexity classification models, that combines an efficient search algorithm with mechanisms for reducing complexity. We tested our proposal on a real World remote sensing problem, the Local Climate Zone classification. The results show that our proposal achieves state-of-the-art performance, while being at least 91.8% more compact in terms of size and FLOPs.