Pyyan / Compare / Faster R-CNN vs YOLO26 vs YOLOv12 vs RTMDet
Faster R-CNN vs YOLO26 vs YOLOv12 vs RTMDet
4 of 5Object Detection · verified 13 Aug 2026
| Specification | Faster R-CNN | YOLO26 | YOLOv12 | RTMDet |
|---|---|---|---|---|
| Summary | The two-stage baseline, still a reference point. | The latest in the line most people mean by object detection. | Attention-centric YOLO, still widely deployed. | Pure throughput, and an MIT licence. |
| COCO mAP | ~42 | ~55 | ~55 | ~52 |
| Speed | Low | Very high | Very high | 300+ |
| Licence | MIT | AGPL-3.0 | AGPL-3.0 | MIT |
| Family | Two stage | Single stage | Single stage | Single stage |
| Open vocabulary | No | No | No | No |
| Released | 2015 | 2026 | 2025 | 2022 |
| Category | Object Detection | Object Detection | Object Detection | Object Detection |
| Official | Microsoft ↗ | Ultralytics ↗ | Ultralytics ↗ | OpenMMLab ↗ |
Highlighted rows are where these differ.
Faster R-CNN
- Region proposals then refinement
- Included as the historical baseline
Best for accuracy where latency does not matter.
YOLO26
- Strong on Jetson, Snapdragon and ARM CPUs
- AGPL-3.0: open-source your derivative work or buy a licence
Best for edge and mobile deployment.
YOLOv12
- Attention added to the classic single-stage design
- Same licensing consideration as YOLO26
Best for teams already on the YOLO toolchain.
RTMDet
- Wins on raw speed where accuracy is sufficient
- MIT, so no derivative-work obligation
Best for high frame-rate video.