| DEIM: DETR with Improved Matching for Fast Convergence | ✓ Link | 59.5 | 78 (T4) | DEIM-D-FINE-X+ | 2024-12-05 |
| D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement | ✓ Link | 59.3 | 78 (T4) | D-FINE-X+ | 2024-10-17 |
| YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications | ✓ Link | 57.2 | 26 | YOLOv6-L6(1280) | 2022-09-07 |
| 0/1 Deep Neural Networks via Block Coordinate Descent | | 57.1 | 124 (T4) | D-FINE-L+ | 2022-06-19 |
| Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection | ✓ Link | 56.9 | 31 | PRB-FPN6-E-ELAN(1280) | 2020-12-03 |
| YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors | ✓ Link | 56.8 | 36 | YOLOv7-E6E(1280) | 2022-07-06 |
| YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors | ✓ Link | 56.6 | 44 | YOLOv7-D6(1280) | 2022-07-06 |
| DEIM: DETR with Improved Matching for Fast Convergence | ✓ Link | 56.5 | 78 (T4) | DEIM-D-FINE-X | 2024-12-05 |
| DETRs Beat YOLOs on Real-time Object Detection | ✓ Link | 56.3 | 40 (T4) | RT-DETR-H(640) | 2023-04-17 |
| YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors | ✓ Link | 56 | 56 | YOLOv7-E6(1280) | 2022-07-06 |
| D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement | ✓ Link | 55.8 | 78 (T4) | D-FINE-X | 2024-10-17 |
| YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information | ✓ Link | 55.6 | | YOLOv9-E | 2024-02-21 |
| You Only Learn One Representation: Unified Network for Multiple Tasks | ✓ Link | 55.4 | 30 | YOLOR-D6 | 2021-05-10 |
| YOLOv12: A Breakdown of the Key Architectural Features | | 55.2 | 85 (T4) | YOLOv12x | 2025-02-20 |
| D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement | ✓ Link | 55.1 | 178 (T4) | D-FINE-M+ | 2024-10-17 |
| YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information | ✓ Link | 55.0 | | GELAN-E | 2024-02-21 |
| YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors | ✓ Link | 54.9 | 84 | YOLOv7-W6(1280) | 2022-07-06 |
| DETRs Beat YOLOs on Real-time Object Detection | ✓ Link | 54.8 | 74 (T4) | RT-DETR-X | 2023-04-17 |
| You Only Learn One Representation: Unified Network for Multiple Tasks | ✓ Link | 54.8 | 37 | YOLOR-E6 | 2021-05-10 |
| DEIM: DETR with Improved Matching for Fast Convergence | ✓ Link | 54.7 | 124 (T4) | DEIM-D-FINE-L | 2024-12-05 |
| YOLOv11: An Overview of the Key Architectural Enhancements | ✓ Link | 54.7 | 88 (T4) | YOLOv11x | 2024-10-23 |
| PP-YOLOE: An evolved version of YOLO | ✓ Link | 54.7 | 45 | PP-YOLOE+_X | 2022-03-30 |
| YOLOv10: Real-Time End-to-End Object Detection | ✓ Link | 54.4 | | YOLOv10-X | 2024-05-23 |
| DETRs Beat YOLOs on Real-time Object Detection | ✓ Link | 54.3 | 74 (T4) | RT-DETR-R101 | 2023-04-17 |
| You Only Learn One Representation: Unified Network for Multiple Tasks | ✓ Link | 54.1 | 47 | YOLOR-W6 | 2021-05-10 |
| D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement | ✓ Link | 54.0 | 124 (T4) | D-FINE-L | 2024-10-17 |
| PP-YOLOE: An evolved version of YOLO | ✓ Link | 54.0 | 78 | PP-YOLOE+_L(distillation) | 2022-03-30 |
| YOLOv12: A Breakdown of the Key Architectural Features | | 53.7 | 148 (T4) | YOLOv12l | 2025-02-20 |
| YOLOv11: An Overview of the Key Architectural Enhancements | ✓ Link | 53.4 | 161 (T4) | YOLOv11l | 2024-10-23 |
| YOLOv10: Real-Time End-to-End Object Detection | ✓ Link | 53.4 | | YOLOv10-L | 2024-05-23 |
| Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection | ✓ Link | 53.3 | 94 | PRB-FPN-MSP | 2020-12-03 |
| YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors | ✓ Link | 53.1 | 114 | YOLOv7-X | 2022-07-06 |
| DETRs Beat YOLOs on Real-time Object Detection | ✓ Link | 53.0 | 114 (T4) | RT-DETR-L | 2023-04-17 |
| You Only Learn One Representation: Unified Network for Multiple Tasks | ✓ Link | 53 | 49 | YOLOR-P6D | 2021-05-10 |
| YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information | ✓ Link | 53.0 | | YOLOv9-C | 2024-02-21 |
| PP-YOLOE: An evolved version of YOLO | ✓ Link | 52.9 | 78 | PP-YOLOE+_L | 2022-03-30 |
| YOLOv6 v3.0: A Full-Scale Reloading | ✓ Link | 52.8 | 98 | YOLOv6-L | 2023-01-13 |
| RTMDet: An Empirical Study of Designing Real-Time Object Detectors | ✓ Link | 52.8 | | RTMDet | 2022-12-14 |
| DEIM: DETR with Improved Matching for Fast Convergence | ✓ Link | 52.7 | 178 (T4) | DEIM-D-FINE-M | 2024-12-05 |
| YOLOv10: Real-Time End-to-End Object Detection | ✓ Link | 52.7 | | YOLOv10-B | 2024-05-23 |
| You Only Learn One Representation: Unified Network for Multiple Tasks | ✓ Link | 52.6 | 49 | YOLOR-P6 | 2021-05-10 |
| YOLOv12: A Breakdown of the Key Architectural Features | | 52.5 | 206 (T4) | YOLOv12m | 2025-02-20 |
| Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection | ✓ Link | 52.5 | 70 | PRB-FPN-ELAN | 2020-12-03 |
| YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information | ✓ Link | 52.5 | | GELAN-C | 2024-02-21 |
| D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement | ✓ Link | 52.3 | 178 (T4) | D-FINE-M | 2024-10-17 |
| Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection | ✓ Link | 51.8 | 113 | PRB-FPN-CSP | 2020-12-03 |
| YOLOv11: An Overview of the Key Architectural Enhancements | ✓ Link | 51.5 | 212 (T4) | YOLOv11m | 2024-10-23 |
| YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information | ✓ Link | 51.4 | | YOLOv9-M | 2024-02-21 |
| YOLOv10: Real-Time End-to-End Object Detection | ✓ Link | 51.3 | | YOLOv10-M | 2024-05-23 |
| Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detection | ✓ Link | 51.2 | | MAFYOLOm | 2024-07-05 |
| YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information | ✓ Link | 51.1 | | GELAN-M | 2024-02-21 |
| PP-YOLOE: An evolved version of YOLO | ✓ Link | 51.0 | 123 | YOLOv3 | 2022-03-30 |
| DAMO-YOLO : A Report on Real-Time Object Detection Design | ✓ Link | 50.8 | 126 | DAMO-YOLO-L | 2022-11-23 |
| D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement | ✓ Link | 50.7 | 287 (T4) | D-FINE-S+ | 2024-10-17 |
| YOLOX: Exceeding YOLO Series in 2021 | ✓ Link | 50.4 | 62.5 | YOLOv5-X | 2021-07-18 |
| YOLOv6 v3.0: A Full-Scale Reloading | ✓ Link | 50.3 | 98 | YOLOv6-S6(1280) | 2023-01-13 |
| YOLOv6 v3.0: A Full-Scale Reloading | ✓ Link | 50.0 | 175 | YOLOv6-M | 2023-01-13 |
| PP-YOLOE: An evolved version of YOLO | ✓ Link | 49.8 | | PP-YOLOE+_M | 2022-03-30 |
| DAMO-YOLO : A Report on Real-Time Object Detection Design | ✓ Link | 49.2 | 233 | DAMO-YOLO-M | 2022-11-23 |
| DEIM: DETR with Improved Matching for Fast Convergence | ✓ Link | 49.0 | 287 (T4) | DEIM-D-FINE-S | 2024-12-05 |
| D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement | ✓ Link | 48.5 | 287 (T4) | D-FINE-S | 2024-10-17 |
| YOLOv12: A Breakdown of the Key Architectural Features | | 48.0 | 383 (T4) | YOLOv12s | 2025-02-20 |
| Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detection | ✓ Link | 47.4 | | MAFYOLOs | 2024-07-05 |
| YOLOv11: An Overview of the Key Architectural Enhancements | ✓ Link | 47.0 | 400 (T4) | YOLOv11s | 2024-10-23 |
| YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information | ✓ Link | 46.8 | | YOLOv9-S | 2024-02-21 |
| YOLOv10: Real-Time End-to-End Object Detection | ✓ Link | 46.8 | | YOLOv10-S | 2024-05-23 |
| YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information | ✓ Link | 46.7 | | GELAN-S | 2024-02-21 |
| DAMO-YOLO : A Report on Real-Time Object Detection Design | ✓ Link | 46 | 325 | DAMO-YOLO-S | 2022-11-23 |
| YOLOv6 v3.0: A Full-Scale Reloading | ✓ Link | 45.0 | 339 | YOLOv6-S | 2023-01-13 |
| YOLOv4: Optimal Speed and Accuracy of Object Detection | ✓ Link | 43.5 | 23 | YOLOv4-L | 2020-04-23 |
| YOLOv4: Optimal Speed and Accuracy of Object Detection | ✓ Link | 43.0 | 31 | YOLOv4-M | 2020-04-23 |
| Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detection | ✓ Link | 42.4 | | MAFYOLOn | 2024-07-05 |
| DAMO-YOLO : A Report on Real-Time Object Detection Design | ✓ Link | 42 | 397 | DAMO-YOLO-T | 2022-11-23 |
| End-to-End Object Detection with Transformers | ✓ Link | 42 | 26 | Faster RCNN-FPN+ | 2020-05-26 |
| YOLOv4: Optimal Speed and Accuracy of Object Detection | ✓ Link | 41.2 | 38 | YOLOv4-S | 2020-04-23 |
| YOLOv12: A Breakdown of the Key Architectural Features | | 40.6 | 610 (T4) | YOLOv12n | 2025-02-20 |
| YOLOv11: An Overview of the Key Architectural Enhancements | ✓ Link | 39.5 | 667 (T4) | YOLOv11n | 2024-10-23 |
| YOLOv10: Real-Time End-to-End Object Detection | ✓ Link | 39.5 | | YOLOv10-N | 2024-05-23 |
| YOLOv6 v3.0: A Full-Scale Reloading | ✓ Link | 37.5 | 779 | YOLOv6-N | 2023-01-13 |
| YOLOv3: An Incremental Improvement | ✓ Link | 33.0 | | YOLOv3-L | 2018-04-08 |
| YOLOv5-6D: Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries | ✓ Link | 28.0 | 6.3 | YOLOv5n | 2024-03-22 |
| DEIM: DETR with Improved Matching for Fast Convergence | ✓ Link | | 78 (T4) | DEIM-D-FINE-X+ | 2024-12-05 |