A Practice and Consideration of AI Model Development via Once-for-All

—Breaking away from Model-First Development to Once-for-All AI Model Development—

Abstract

Once-for-All (OFA) is an AI model development method that allows a model (Supernet), a redundant representation of a base AI model (Base Model), to be trained only once to obtain models (Subnets) that are suitable for various devices in terms of accuracy, processing speed and number of parameters. In this paper, we address a road obstacle detection system consisting of multiple AI models, and apply OFA to each AI model. Finally, we succeed in obtaining the optimal Subnets for the entire system by considering the combination of the obtained Subnets.