We present YOLOBench, a benchmark comprised of 550+ YOLO-based object detection models on 4 different datasets and 4 different embedded hardware platforms (x86 CPU, ARM CPU, Nvidia GPU, NPU). We collect accuracy and latency numbers for a variety of YOLO-based one-stage detectors at different model scales by performing a fair, controlled comparison of these detectors with a fixed training environment (code and training hyperparameters). Pareto-optimality analysis of the collected data reveals that, if modern detection heads and training techniques are incorporated into the learning process, multiple architectures of the YOLO series achieve a good accuracy-latency trade-off, including older models like YOLOv3 and YOLOv4. We also evaluate training-free accuracy estimators used in neural architecture search on YOLOBench and demonstrate that, while most state-of-the-art zero-cost accuracy estimators are outperformed by a simple baseline like MAC count, some of them can be effectively used to predict Pareto-optimal detection models. We showcase that by using a zero-cost proxy to identify a YOLO architecture competitive against a state-of-the-art YOLOv8 model on a Raspberry Pi 4 CPU. The code and data are available at https://github.com/Deeplite/deeplite-torch-zoo

通过在4个不同的数据集和4个不同的嵌入式硬件平台（x86 CPU，ARM CPU，Nvidia GPU，NPU）上进行公平、可控的比较，收集了550多个基于YOLO的目标检测模型的准确性和延迟数据，并通过帕累托优化分析表明，基于YOLO系列的多种架构都可在准确性和延迟之间取得良好的平衡，包括YOLOv3和YOLOv4等旧模型。在YOLOBench上评估了神经架构搜索中的无成本准确性估计器，并证明虽然大多数最先进的零成本准确性估计器被简单的基准（如MAC计数）超越，但其中一些可以有效地用于预测帕累托最优的检测模型。通过在树莓派4 CPU上使用零成本代理，展示了一种与最先进的YOLOv8模型具有竞争力的YOLO架构。

YOLOBench: 嵌入式系统上高效目标检测器的基准测试