Compare YOLO11 vs YOLOv5 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 YOLOv5 Comparison Table
| Property | YOLO11 | YOLOv5 |
|---|---|---|
| Organization | Ultralytics | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Sep 2024 | Jan 2020 |
| Context Window | — | — |
| Parameters | 2.6M-56.9M | 1.9M-86.7M |
| License | AGPL 3.0 | AGPL 3.0 |
| Commercial use | commercial use needs a license (included on Roboflow cloud) | commercial use needs a license (included on Roboflow cloud) |
| 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 | ||
| Object Detection | Demo (COCO) | Supported |
| Instance Segmentation | Demo (COCO) | Not listed |
| Model Features | ||
Both support 1 more feature | ||
YOLO11 vs YOLOv5: 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.
YOLOv5 is an object detection model developed by Ultralytics, released in June 2020 under the AGPL-3.0 license. It is implemented in PyTorch and introduced a more accessible and well-documented YOLO implementation compared to earlier Darknet-based versions, with an integrated training and export pipeline supporting a wide range of deployment targets. YOLOv5 uses a CSP backbone, PANet neck, and a single-stage detection head with anchor-based regression.
YOLOv5 is available in five sizes from Nano to Extra Large and supports export to ONNX, TensorRT, CoreML, and other formats. It is one of the most widely deployed object detection models in production environments and remains a common starting point for custom detection model training due to its documentation, community support, and compatibility with Roboflow Inference.
