Compare YOLO11 vs YOLOv9 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.
YOLO11 vs YOLOv9 Comparison Table
| Property | YOLO11 | YOLOv9 |
|---|---|---|
| Organization | Ultralytics | Academia Sinica |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Sep 2024 | Feb 2024 |
| Context Window | — | — |
| Parameters | 2.6M-56.9M | 2.0M-57.3M |
| License | AGPL 3.0 | GPL v3 |
| Commercial use | commercial use needs a license (included on Roboflow cloud) | commercial use means releasing your source code under GPL-3.0 |
| COCO Benchmark object detection, mAP 50:95 on COCO val2017 | ||
| Best size | 53.6XL · 56.9M params | Not benchmarked |
| Smallest size | 38.6Nano · 2.6M params | Not benchmarked |
| mAP range | 38.6–53.6across 5 sizes | Not benchmarked |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | 38.6640×640 · 2.6M params | Not available |
| Small | 45.5640×640 · 9.4M params | Not available |
| Medium | 50.5640×640 · 20.1M params | Not available |
| Large | 52.2640×640 · 25.3M params | Not available |
| XL | 53.6640×640 · 56.9M params | Not available |
| 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 | ||
| Instance Segmentation | Demo (COCO) | Supported |
| Object Detection | Demo (COCO) | Supported |
| Model Features | ||
Both support 1 more feature | ||
YOLO11 vs YOLOv9: Overview
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.
YOLOv9 is a real-time object detection model developed by Chien-Yao Wang and Hong-Yuan Mark Liao at Academia Sinica, released in February 2024 under the GPL-3.0 license. It introduces Programmable Gradient Information (PGI), a mechanism that preserves complete input information through auxiliary reversible branches during training to address information loss in deep network layers. It also introduces the Generalized Efficient Layer Aggregation Network (GELAN), which achieves better parameter utilization compared to prior CSP-based designs.
YOLOv9-C achieves 53.0% AP on COCO with 42% fewer parameters and 21% less computation than YOLOv8-C at comparable accuracy. YOLOv9-E achieves 55.6% AP. The model is deployable through Roboflow Inference and supports fine-tuning via the standard training pipeline in the official repository.
