New Metric Speeds Up Neural Network Selection for Few-Class Datasets

While much effort in neural network development focuses on high-performance models for datasets with thousands of classes, many real-world applications operate with fewer than ten classes. This common scenario has received less attention regarding efficient model selection. Researchers have developed a new measure of classification difficulty, termed "few-class distinctiveness," which is derived from data-side properties. This metric enables significantly faster model selection for few-class datasets, outperforming traditional iterative training and testing by a factor of 6 to 29. Leveraging this insight, the study also demonstrates how to extend scaled model families below their smallest published versions, achieving greater efficiency at comparable accuracy. This is particularly beneficial for resource-constrained applications in mobile robotics, drones, and IoT, where models up to 42% smaller than existing solutions like YOLOv5-nano can be deployed without sacrificing performance.