mlsanityData to 7 Oct 2026

You Only Look Once: Unified, Real-Time Object Detection

arXiv 1506.02640 ↗First posted 8 Jun 2015cs.CVcited by 4,829

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Cited by 120 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset.

Key facts. Cited by 4,829 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2015.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 860; 2022: 870; 2023: 847; 2024: 892; 2025: 823; 2026: 536.

4,829 papers.

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  1. 2021Grounded Language-Image Pre-trainingcited by 1,147
  2. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
  3. 2021Transformers in Vision: A Surveycited by 1,009
  4. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  5. 2022Exploring Plain Vision Transformer Backbones for Object Detectioncited by 723
  6. 2021BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-Viewcited by 711
  7. 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulationcited by 699
  8. 2021DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queriescited by 655
  9. 2021RegionCLIP: Region-based Language-Image Pretrainingcited by 612
  10. 2022DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETRcited by 569
  11. 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 526
  12. 2021MViTv2: Improved Multiscale Vision Transformers for Classification and Detectioncited by 516
38 more
  1. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 508
  2. 2024YOLO-World: Real-Time Open-Vocabulary Object Detectioncited by 498
  3. 2021FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detectioncited by 444
  4. 2021Conditional DETR for Fast Training Convergencecited by 422
  5. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 422
  6. 2024YOLOv11: An Overview of the Key Architectural Enhancementscited by 414
  7. 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
  8. 2021Pix2seq: A Language Modeling Framework for Object Detectioncited by 345
  9. 2022Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Trackingcited by 345
  10. 2021Rethinking Coarse-to-Fine Approach in Single Image Deblurringcited by 324
  11. 2022Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Modelcited by 317
  12. 2021End-to-End Semi-Supervised Object Detection with Soft Teachercited by 315
  13. 2023SeeSR: Towards Semantics-Aware Real-World Image Super-Resolutioncited by 311
  14. 2025YOLOv12: Attention-Centric Real-Time Object Detectorscited by 300
  15. 2021Towards Open World Object Detectioncited by 297
  16. 2023ChatGPT for Robotics: Design Principles and Model Abilitiescited by 296
  17. 2022GLIPv2: Unifying Localization and Vision-Language Understandingcited by 275
  18. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  19. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 273
  20. 2023LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generationcited by 258
  21. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
  22. 2023Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylizationcited by 251
  23. 2021Is Pseudo-Lidar needed for Monocular 3D Object detection?cited by 248
  24. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  25. 2021Oriented R-CNN for Object Detectioncited by 230
  26. 2021LLVIP: A Visible-infrared Paired Dataset for Low-light Visioncited by 229
  27. 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
  28. 2025Video models are zero-shot learners and reasonerscited by 208
  29. 2021ReDet: A Rotation-equivariant Detector for Aerial Object Detectioncited by 202
  30. 2022RTMDet: An Empirical Study of Designing Real-Time Object Detectorscited by 192
  31. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  32. 2021Probabilistic and Geometric Depth: Detecting Objects in Perspectivecited by 190
  33. 2023Universal Instance Perception as Object Discovery and Retrievalcited by 190
  34. 2021Dynamic Head: Unifying Object Detection Heads with Attentionscited by 185
  35. 2021OTA: Optimal Transport Assignment for Object Detectioncited by 177
  36. 2021Fast Convergence of DETR with Spatially Modulated Co-Attentioncited by 176
  37. 2022DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detectioncited by 176
  38. 2024InstanceDiffusion: Instance-level Control for Image Generationcited by 175

50 most cited of 4,829 All 4,829 (JSON)