对比11个版本YOLO在33个场景下的表现，验证新版本是否真更优
ODverse33: Is the New YOLO Version Always Better? A Multi Domain benchmark from YOLO v5 to v11
- 构建跨11大领域的33个数据集基准ODverse33
- 实测显示新版本并非在所有场景都优于旧版
- 为实时检测器开发提供多场景性能参考
YOLO系列模型被广泛用于各类场景的实时目标检测。随着新版本发布频率增加，核心问题浮现：新版本是否始终优于旧版？各版本的核心创新是什么？这些改进如何转化为实际性能提升？本文梳理了YOLOv1至YOLOv11的关键创新，提出综合性基准ODverse33，涵盖自动驾驶、农业、水下、医疗、游戏、工业、航拍、野生动物、零售、显微和安防等11个不同领域共33个数据集，并通过大量实验探索模型改进在真实多领域应用中的实际影响。本研究旨在为广大的目标检测使用者提供参考，并为未来实时检测器的发展提供依据。
原文摘要 · Abstract (English)
You Look Only Once (YOLO) models have been widely used for building real-time object detectors across various domains. With the increasing frequency of new YOLO versions being released, key questions arise. Are the newer versions always better than their previous versions? What are the core innovations in each YOLO version and how do these changes translate into real-world performance gains? In this paper, we summarize the key innovations from YOLOv1 to YOLOv11, introduce a comprehensive benchmark called ODverse33, which includes 33 datasets spanning 11 diverse domains (Autonomous driving, Agricultural, Underwater, Medical, Videogame, Industrial, Aerial, Wildlife, Retail, Microscopic, and Security), and explore the practical impact of model improvements in real-world, multi-domain applications through extensive experimental results. We hope this study can provide some guidance to the extensive users of object detection models and give some references for future real-time object detector development.
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