ODverse33: Is the New YOLO Version Always Better? A Multi Domain benchmark from YOLO v5 to v11
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 28 of its 52 references that are in the map. First posted on arXiv 20 Feb 2025.
Builds on
28 of 52 references.
References · 28
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2016Feature Pyramid Networks for Object Detectioncited by 5,036
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2018YOLOv3: An Incremental Improvementcited by 2,659
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
- 2016YOLO9000: Better, Faster, Strongercited by 1,431
- 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
- 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
- 2018Path Aggregation Network for Instance Segmentationcited by 798
- 2017DOTA: A Large-scale Dataset for Object Detection in Aerial Imagescited by 623
16 more
- 2019Object Detection in 20 Years: A Surveycited by 531
- 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 515
- 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 459
- 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 425
- 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
- 2019Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmarkcited by 340
- 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 275
- 2021OTA: Optimal Transport Assignment for Object Detectioncited by 178
- 2019Precise Detection in Densely Packed Scenescited by 63
- 2019SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Imagescited by 52
- 2020ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalitiescited by 38
- 2022HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detectioncited by 34
- 2021A Dataset And Benchmark Of Underwater Object Detection For Robot Pickingcited by 31
- 2021Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detectioncited by 30
- 2019Online PCB Defect Detector On A New PCB Defect Datasetcited by 25
- 2024A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10 in Kitchen Safetycited by 4
Built on by
Citing papers per year: 2025: 2.
2 papers.