Mask R-CNN vs YOLO11
We don't benchmark Mask R-CNN or YOLO11 on Vision Evals. Mask R-CNN is open-weight under MIT: free for commercial use. YOLO11 is open-weight under AGPL 3.0: commercial use needs a license (included on Roboflow cloud).
Compare Mask R-CNN 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.
Mask R-CNN vs YOLO11 Comparison Table
| Property | Mask R-CNN | YOLO11 |
|---|---|---|
| Organization | Meta | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Oct 2017 | Sep 2024 |
| Context Window | — | — |
| Parameters | 44.4M | 2.6M-56.9M |
| License | MIT | AGPL 3.0 |
| Commercial use | free for commercial use | 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 | ||
| Instance Segmentation | Supported | Demo (COCO) |
| Object Detection | Supported | Demo (COCO) |
| Keypoint Detection | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| Real-Time Vision | Not listed | Supported |
Mask R-CNN vs YOLO11: Overview
Mask R-CNN is an instance segmentation model developed by Facebook AI Research (Meta), released in October 2017. It extends Faster R-CNN by adding a parallel branch that predicts binary segmentation masks for each detected object, independent of the classification and bounding box regression branches. A key contribution is RoIAlign, which replaces RoIPool with bilinear interpolation to preserve spatial correspondence between features and input pixels, significantly improving mask quality.
Mask R-CNN achieves strong performance on the COCO instance segmentation benchmark and supports keypoint detection as an additional output head. It remains a foundational architecture in instance segmentation and is available through Meta's Detectron2 framework. The model is most appropriate for tasks requiring pixel-level object delineation, such as medical imaging, autonomous driving, and industrial inspection.
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.
