STResNet & STYOLO : A New Family of Compact Classification and Object Detection Models for MCUs

TL;DR

This paper introduces two new lightweight neural network families, STResNet and STYOLO, optimized for image classification and object detection on resource-constrained microcontrollers, achieving high accuracy with minimal parameters.

Contribution

The paper presents novel compact models, STResNet and STYOLO, that outperform existing lightweight architectures in accuracy and efficiency for edge devices.

Findings

STResNetMilli achieves 70.0% Top 1 accuracy with 3 million parameters.

STYOLOMicro and STYOLOMilli outperform YOLOv5n and YOLOX Nano in accuracy and efficiency.

Models are optimized for deployment on microcontrollers with limited resources.

Abstract

Recent advancements in lightweight neural networks have significantly improved the efficiency of deploying deep learning models on edge hardware. However, most existing architectures still trade accuracy for latency, which limits their applicability on microcontroller and neural processing unit based devices. In this work, we introduce two new model families, STResNet for image classification and STYOLO for object detection, jointly optimized for accuracy, efficiency, and memory footprint on resource constrained platforms. The proposed STResNet series, ranging from Nano to Tiny variants, achieves competitive ImageNet 1K accuracy within a four million parameter budget. Specifically, STResNetMilli attains 70.0 percent Top 1 accuracy with only three million parameters, outperforming MobileNetV1 and ShuffleNetV2 at comparable computational complexity. For object detection, STYOLOMicro and STYOLOMilli achieve 30.5 percent and 33.6 percent mean average precision, respectively, on the MS COCO dataset, surpassing YOLOv5n and YOLOX Nano in both accuracy and efficiency. Furthermore, when STResNetMilli is used as a backbone with the Ultralytics training environment.