We introduce MCUBench, a benchmark featuring over 100 YOLO-based object detection models evaluated on the VOC dataset across seven different MCUs. This benchmark provides detailed data on average precision, latency, RAM, and Flash usage for various input resolutions and YOLO-based one-stage detectors. By conducting a controlled comparison with a fixed training pipeline, we collect comprehensive performance metrics. Our Pareto-optimal analysis shows that integrating modern detection heads and training techniques allows various YOLO architectures, including legacy models like YOLOv3, to achieve a highly efficient tradeoff between mean Average Precision (mAP) and latency. MCUBench serves as a valuable tool for benchmarking the MCU performance of contemporary object detectors and aids in model selection based on specific constraints.

翻译：我们提出了MCUBench，这是一个包含超过100个基于YOLO的目标检测模型在VOC数据集上、于七种不同微控制器（MCU）上进行评估的基准测试。该基准测试提供了针对不同输入分辨率及基于YOLO的单阶段检测器的平均精度（AP）、延迟、RAM和Flash使用情况的详细数据。通过采用固定的训练流程进行受控比较，我们收集了全面的性能指标。我们的帕累托最优分析表明，集成现代检测头和训练技术使得包括YOLOv3等传统模型在内的多种YOLO架构，能够在平均精度均值（mAP）与延迟之间实现高效的权衡。MCUBench是评估当代目标检测器在MCU上性能的宝贵工具，并有助于根据特定约束条件进行模型选择。