Efficient Neural Network Model Selection for Few-Class Application Datasets

2026-06-17

AI Summary

This study addresses the inefficiency of neural network model selection in real-world scenarios involving few classes (<10). To tackle this challenge, the authors propose a data-centric metric for quantifying classification difficulty, introducing the novel concept of “few-class discriminability.” This enables the construction of an efficient evaluation framework that facilitates cross-model and cross-dataset comparisons without repeated training. Integrated with lightweight network scaling and cross-platform deployment strategies, the proposed method achieves rapid model selection in applications such as mobile robotics, drones, and IoT systems, accelerating the selection process by 6–29×. Furthermore, it yields a highly compact model that is 42% smaller than YOLOv5-nano while maintaining competitive accuracy and substantially reducing computational resource requirements.

Abstract

While much effort has focused on developing and benchmarking high-performance neural networks, less attention has been given to how dataset properties, known to practitioners, can guide efficient model selection. Neural models are typically evaluated on datasets with thousands of classes, yet many real-world applications involve fewer than ten. To address this understudied but common setting, we develop a measure of classification difficulty based on data-side properties and show how it enables more efficient model selection for few-class datasets, where traditional approaches are less effective. We term this phenomenon "few-class distinctiveness". Our metric allows comparison of models and datasets 6 to 29$\times$ faster than repeated training and testing. Leveraging this insight, we extend scaled model families below the smallest published models, achieving greater efficiency at similar accuracy, for example models up to 42% smaller than YOLOv5-nano for a mobile robot task. Targeting resource-constrained applications, we demonstrate few-class model selection across mobile robot, drone, and IoT scenarios, highlighting practical gains in efficiency without sacrificing performance.

Problem

• few-class
• model selection
• neural networks
• classification difficulty
• dataset properties

Innovation

• few-class distinctiveness
• efficient model selection
• classification difficulty metric
• resource-constrained applications
• scaled model families