RTMDet vs YOLO11
Compare RTMDet and YOLO11 side-by-side.
Compare RTMDet vs YOLO11 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
RTMDet vs YOLO11 Comparison Table
Evals updated October 13, 2026Pricing updated October 23, 2026
| Property | RTMDet | YOLO11 |
|---|---|---|
| Organization | OpenMMLab | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Dec 2022 | Sep 2024 |
| Context Window | — | — |
| Parameters | 4.8M-94.9M | 2.6M-56.9M |
| License | GPL v3 | AGPL 3.0 |
| Commercial use | commercial use requires open-sourcing your code or a commercial license | commercial use needs a license (included on Roboflow cloud) |
| COCO Benchmark object detection, mAP 50:95 on COCO val2017 | ||
| Best size | Not benchmarked | 53.6XL · 56.9M params |
| Smallest size | Not benchmarked | 38.6Nano · 2.6M params |
| mAP range | Not benchmarked | 38.6–53.6across 5 sizes |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | Not available | 38.6640×640 · 2.6M params |
| Small | Not available | 45.5640×640 · 9.4M params |
| Medium | Not available | 50.5640×640 · 20.1M params |
| Large | Not available | 52.2640×640 · 25.3M params |
| XL | Not available | 53.6640×640 · 56.9M params |
| COCO val2017 mAP 50:95 and parameter counts from leaderboard.roboflow.com. Size names don't line up across model families, so compare sizes with similar parameter counts. The live demo runs some of these sizes at other resolutions, which were not benchmarked. | ||
| Vision Tasks | ||
| Object Detection | Supported | Demo (COCO) |
| Instance Segmentation | Not listed | Demo (COCO) |
| Model Features | ||
| Real-Time Vision | Supported | Supported |
RTMDet vs YOLO11: Overview
RTMDet is a real-time object detection model developed by OpenMMLab, released in December 2022 under the GPL-3.0 license. It adopts a single-stage detection architecture with large-kernel depthwise convolution in both the backbone and neck, enabling it to capture long-range spatial dependencies without the computational cost of full self-attention. The model family spans from RTMDet-tiny to RTMDet-x, covering a wide range of speed-accuracy operating points.
RTMDet-x achieves 52.6% AP on COCO at 114 FPS on an NVIDIA 3090 GPU. The architecture supports instance segmentation and rotated object detection variants. RTMDet is included in the OpenMMLab ecosystem and is well suited for applications requiring fast, accurate detection with flexible model sizing.
YOLO11 is an object detection and multi-task vision model developed by Ultralytics, released in September 2024 under the AGPL-3.0 license. It is the latest generation in the Ultralytics YOLO series and supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a single unified framework. YOLO11 introduces architectural refinements that improve accuracy while reducing parameter count compared to YOLOv8 at equivalent model sizes.
YOLO11 is available in five model sizes from Nano to Extra Large and is deployable through the Ultralytics Python package, Roboflow Inference, and export formats including ONNX, TensorRT, and CoreML. It supports fine-tuning on custom datasets through the standard Ultralytics training API.
