mlsanityData to 14 Oct 2026

YOLOv10: Real-Time End-to-End Object Detection

arXiv 2405.14458 ↗First posted 23 May 2024cs.CVcited by 515

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Cited by 22 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. However, the reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs and adversely impacts the inference latency. Besides, the design of various components in YOLOs lacks the comprehensive and thorough inspection, resulting in noticeable computational redundancy and limiting the model's capability. It renders the suboptimal efficiency, along with considerable potential for performance improvements. In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and model architecture. To this end, we first present the consistent dual assignments for NMS-free training of YOLOs, which brings competitive performance and low inference latency simultaneously. Moreover, we introduce the holistic efficiency-accuracy driven model design strategy for YOLOs. We comprehensively optimize various components of YOLOs from both efficiency and accuracy perspectives, which greatly reduces the computational overhead and enhances the capability. The outcome of our effort is a new generation of YOLO series for real-time end-to-end object detection, dubbed YOLOv10. Extensive experiments show that YOLOv10 achieves state-of-the-art performance and efficiency across various model scales. For example, our YOLOv10-S is 1.8× faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8× smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46% less latency and 25% fewer parameters for the same performance.

Key facts. Cited by 515 papers in the map (data through 14 Oct 2026). Builds on 74 of its 82 references that are in the map. First posted on arXiv 23 May 2024.

Builds on

74 of 82 references.

References · 74

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  4. 2020End-to-End Object Detection with Transformerscited by 7,414
  5. 2017Mask R-CNNcited by 7,277
  6. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  7. 2016Layer Normalizationcited by 5,669
  8. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  9. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  10. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  11. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  12. 2022A ConvNet for the 2020scited by 3,579
38 more
  1. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
  2. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
  3. 2015Fast R-CNNcited by 2,933
  4. 2018YOLOv3: An Incremental Improvementcited by 2,659
  5. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  6. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  7. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  8. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,319
  9. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
  10. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  11. 2021CvT: Introducing Convolutions to Vision Transformerscited by 947
  12. 2018Path Aggregation Network for Instance Segmentationcited by 798
  13. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  14. 2020Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentationcited by 596
  15. 2022DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETRcited by 573
  16. 2017Understanding the Effective Receptive Field in Deep Convolutional Neural Networkscited by 553
  17. 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 540
  18. 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 533
  19. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 522
  20. 2020Sparse R-CNN: End-to-End Object Detection with Learnable Proposalscited by 495
  21. 2022DN-DETR: Accelerate DETR Training by Introducing Query DeNoisingcited by 488
  22. 2021LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inferencecited by 475
  23. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 459
  24. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 425
  25. 2021Conditional DETR for Fast Training Convergencecited by 424
  26. 2020Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detectioncited by 409
  27. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  28. 2021MOTR: End-to-End Multiple-Object Tracking with Transformercited by 381
  29. 2017Relation Networks for Object Detectioncited by 305
  30. 2022DETRs with Collaborative Hybrid Assignments Trainingcited by 274
  31. 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
  32. 2020Scaled-YOLOv4: Scaling Cross Stage Partial Networkcited by 211
  33. 2018Statistical Aspects of Wasserstein Distancescited by 203
  34. 2022RTMDet: An Empirical Study of Designing Real-Time Object Detectorscited by 195
  35. 2020FCOS: A simple and strong anchor-free object detectorcited by 171
  36. 2022DETRs with Hybrid Matchingcited by 150
  37. 2015End-to-end people detection in crowded scenescited by 140
  38. 2023RepViT: Revisiting Mobile CNN From ViT Perspectivecited by 123

50 most cited of 74 All 74 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 111; 2025: 216; 2026: 188.

515 papers.

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  1. 2024YOLOv11: An Overview of the Key Architectural Enhancementscited by 425
  2. 2025YOLOv12: Attention-Centric Real-Time Object Detectorscited by 305
  3. 2024MinerU: An Open-Source Solution for Precise Document Content Extractioncited by 267
  4. 2025MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsingcited by 116
  5. 2024RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformercited by 91
  6. 2025MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigmcited by 87
  7. 2024DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perceptioncited by 82
  8. 2025YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detectioncited by 79
  9. 2024D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinementcited by 74
  10. 2025YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perceptioncited by 58
  11. 2026Ultralytics YOLO26: Unified Real-Time End-to-End Vision Modelscited by 57
  12. 2024LW-DETR: A Transformer Replacement to YOLO for Real-Time Detectioncited by 36
38 more
  1. 2025YOLOE: Real-Time Seeing Anythingcited by 36
  2. 2024RT-DETRv3: Real-time End-to-End Object Detection with Hierarchical Dense Positive Supervisioncited by 32
  3. 2025FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Modelcited by 32
  4. 2025Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detectioncited by 31
  5. 2025Real-Time Object Detection Meets DINOv3cited by 27
  6. 2024[CLS] Token Tells Everything Needed for Training-free Efficient MLLMscited by 26
  7. 2025PP-DocLayout: A Unified Document Layout Detection Model to Accelerate Large-Scale Data Constructioncited by 26
  8. 2024Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Datasetcited by 25
  9. 2025GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generationcited by 24
  10. 2025VLA-AN: An Efficient and Onboard Vision-Language-Action Framework for Aerial Navigation in Complex Environmentscited by 23
  11. 2026DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Drivingcited by 21
  12. 2024YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versionscited by 18
  13. 2025Ultralytics YOLO Evolution: An Overview of YOLO27, YOLO26, YOLO11, YOLOv8, and YOLOv5 Object Detectors for Computer Vision and Pattern Recognitioncited by 18
  14. 2025UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagerycited by 15
  15. 2025YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Reviewcited by 15
  16. 2025LSNet: See Large, Focus Smallcited by 15
  17. 2024YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domaincited by 14
  18. 2024Enhancing Multi-Robot Semantic Navigation Through Multimodal Chain-of-Thought Score Collaborationcited by 14
  19. 2025A Large Scale Analysis of Gender Biases in Text-to-Image Generative Modelscited by 14
  20. 2025Event-based Tiny Object Detection: A Benchmark Dataset and Baselinecited by 14
  21. 2024HEIE: MLLM-Based Hierarchical Explainable AIGC Image Implausibility Evaluatorcited by 13
  22. 2024SEAL: Semantic Attention Learning for Long Video Representationcited by 13
  23. 2024PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generationcited by 13
  24. 2025FBRT-YOLO: Faster and Better for Real-Time Aerial Image Detectioncited by 13
  25. 2025VistaWise: Building Cost-Effective Agent with Cross-Modal Knowledge Graph for Minecraftcited by 13
  26. 2025MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patternscited by 13
  27. 2024YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Visioncited by 12
  28. 2024From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervisioncited by 12
  29. 2025ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wildcited by 12
  30. 2024Cross-Layer Feature Pyramid Transformer for Small Object Detection in Aerial Imagescited by 11
  31. 2024Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagerycited by 11
  32. 2024Online Episodic Memory Visual Query Localization with Egocentric Streaming Object Memorycited by 11
  33. 2024LoLI-Street: Benchmarking Low-Light Image Enhancement and Beyondcited by 10
  34. 2025M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detectioncited by 10
  35. 2026SpatialEdit: Benchmarking Fine-Grained Image Spatial Editingcited by 10
  36. 2025MV-SSM: Multi-View State Space Modeling for 3D Human Pose Estimationcited by 9
  37. 2024Prompt-Based Segmentation at Multiple Resolutions and Lighting Conditions using Segment Anything Model 2cited by 8
  38. 2025Real-time Traffic Accident Anticipation with Feature Reusecited by 8

50 most cited of 515 All 515 (JSON)