mlsanityData to 7 Oct 2026

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

arXiv 2207.02696 ↗First posted 6 Jul 2022cs.CVcited by 978

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Cited by 22 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B and many other object detectors in speed and accuracy. Moreover, we train YOLOv7 only on MS COCO dataset from scratch without using any other datasets or pre-trained weights. Source code is released in https://github.com/WongKinYiu/yolov7.

Key facts. Cited by 978 papers in the map (data through 7 Oct 2026). Builds on 91 of its 98 references that are in the map. First posted on arXiv 6 Jul 2022.

Builds on

91 of 98 references.

References · 91

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  6. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  7. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  8. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  9. 2014Going Deeper with Convolutionscited by 4,231
  10. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  11. 2022A ConvNet for the 2020scited by 3,545
  12. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
38 more
  1. 2018YOLOv3: An Incremental Improvementcited by 2,652
  2. 2019Searching for MobileNetV3cited by 1,984
  3. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  4. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  5. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  6. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  7. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,361
  8. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,306
  9. 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,215
  10. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
  11. 2018Averaging Weights Leads to Wider Optima and Better Generalizationcited by 1,194
  12. 2019Objects as Pointscited by 1,172
  13. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,132
  14. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,127
  15. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
  16. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,018
  17. 2020Designing Network Design Spacescited by 954
  18. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 812
  19. 2022Exploring Plain Vision Transformer Backbones for Object Detectioncited by 723
  20. 2019Panoptic Feature Pyramid Networkscited by 644
  21. 2018Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNscited by 585
  22. 2022Vision Transformer Adapter for Dense Predictionscited by 564
  23. 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 539
  24. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
  25. 2020Sparse R-CNN: End-to-End Object Detection with Learnable Proposalscited by 494
  26. 2020FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Trackingcited by 489
  27. 2022DN-DETR: Accelerate DETR Training by Introducing Query DeNoisingcited by 486
  28. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 454
  29. 2017Deep Layer Aggregationcited by 436
  30. 2019GhostNet: More Features from Cheap Operationscited by 410
  31. 2020Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detectioncited by 408
  32. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  33. 2014Deeply-Supervised Netscited by 364
  34. 2017Snapshot Ensembles: Train 1, get M for freecited by 349
  35. 2019Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challengescited by 313
  36. 2019NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detectioncited by 289
  37. 2020MCUNet: Tiny Deep Learning on IoT Devicescited by 269
  38. 2020VarifocalNet: An IoU-aware Dense Object Detectorcited by 230

50 most cited of 91 All 91 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 38; 2023: 272; 2024: 317; 2025: 235; 2026: 116.

978 papers.

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  1. 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 526
  2. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 508
  3. 2024YOLO-World: Real-Time Open-Vocabulary Object Detectioncited by 498
  4. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 422
  5. 2024YOLOv11: An Overview of the Key Architectural Enhancementscited by 414
  6. 2025YOLOv12: Attention-Centric Real-Time Object Detectorscited by 300
  7. 2023Fast Segment Anythingcited by 286
  8. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 273
  9. 2023VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigationcited by 201
  10. 2022RTMDet: An Empirical Study of Designing Real-Time Object Detectorscited by 192
  11. 2023Grounded Text-to-Image Synthesis with Attention Refocusingcited by 163
  12. 2023Dense Text-to-Image Generation with Attention Modulationcited by 162
38 more
  1. 2023A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAScited by 149
  2. 2024TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videoscited by 105
  3. 2023Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous drivingcited by 83
  4. 2023Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion Modelscited by 76
  5. 2025YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detectioncited by 76
  6. 2024D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinementcited by 73
  7. 2025RF-DETR: Neural Architecture Search for Real-Time Detection Transformerscited by 70
  8. 2024MagMax: Leveraging Model Merging for Seamless Continual Learningcited by 67
  9. 2023YOLOv6 v3.0: A Full-Scale Reloadingcited by 58
  10. 2023Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Reviewcited by 57
  11. 2023PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathologycited by 57
  12. 2025YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perceptioncited by 57
  13. 2023Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanismcited by 51
  14. 2023Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applicationscited by 50
  15. 2023SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depthcited by 48
  16. 2023SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radarcited by 48
  17. 2026Ultralytics YOLO26: Unified Real-Time End-to-End Vision Modelscited by 47
  18. 2023MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learningcited by 46
  19. 2023Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Imagescited by 41
  20. 2023Advancing Surgical VQA with Scene Graph Knowledgecited by 41
  21. 2023DAP: A Dynamic Adversarial Patch for Evading Person Detectorscited by 40
  22. 2024DRCT: Saving Image Super-resolution away from Information Bottleneckcited by 40
  23. 2023FastPillars: A Deployment-friendly Pillar-based 3D Detectorcited by 39
  24. 2022DAMO-YOLO : A Report on Real-Time Object Detection Designcited by 38
  25. 2023MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusioncited by 37
  26. 2023Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETRcited by 36
  27. 2024LW-DETR: A Transformer Replacement to YOLO for Real-Time Detectioncited by 36
  28. 2025YOLOE: Real-Time Seeing Anythingcited by 36
  29. 2023Text2Layer: Layered Image Generation using Latent Diffusion Modelcited by 35
  30. 2024MS-DETR: Efficient DETR Training with Mixed Supervisioncited by 34
  31. 2023DribbleBot: Dynamic Legged Manipulation in the Wildcited by 33
  32. 2023YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detectioncited by 33
  33. 2024Disentangled 3D Scene Generation with Layout Learningcited by 33
  34. 2023Learning Emotion Representations from Verbal and Nonverbal Communicationcited by 31
  35. 2023PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domaincited by 31
  36. 2023ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasioncited by 31
  37. 2024RT-DETRv3: Real-time End-to-End Object Detection with Hierarchical Dense Positive Supervisioncited by 31
  38. 2023Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanismcited by 30

50 most cited of 978 All 978 (JSON)