Detectron2 vs YOLO11
Compare Detectron2 and YOLO11 side-by-side.
Compare Detectron2 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
Detectron2 vs YOLO11 Comparison Table
Evals updated October 13, 2026Pricing updated October 23, 2026
| Property | Detectron2 | YOLO11 |
|---|---|---|
| Organization | Meta | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Sep 2019 | Sep 2024 |
| Context Window | — | — |
| Parameters | Unknown | 2.6M-56.9M |
| License | Apache 2.0 | 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 |
| Semantic Segmentation | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| Real-Time Vision | Not listed | Supported |
Detectron2 vs YOLO11: Overview
Detectron2 is a computer vision model library developed by Facebook AI Research (Meta), released in September 2019. It serves as a comprehensive platform for object detection, instance segmentation, panoptic segmentation, keypoint detection, and DensePose, implemented in PyTorch. It is the successor to the original Detectron framework, which was written in Caffe2, and offers a more modular and extensible codebase designed for both research and production use.
Detectron2 includes implementations of Faster R-CNN, Mask R-CNN, RetinaNet, Cascade R-CNN, Panoptic FPN, and several other architectures. Its modular design allows components such as backbones, necks, and heads to be swapped independently, making it widely used as a baseline framework in academic research. It supports training on COCO-format datasets and integrates with standard distributed training setups.
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.
