Compare YOLOv5 vs YOLOv8 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.
YOLOv5 vs YOLOv8 Comparison Table
| Property | YOLOv5 | YOLOv8 |
|---|---|---|
| Organization | Ultralytics | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Jan 2020 | Jan 2023 |
| Context Window | — | — |
| Parameters | 1.9M-86.7M | 3.2M-68.2M |
| 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 | Not benchmarked | 52.9XL · 68.2M params |
| Smallest size | Not benchmarked | 36.5Nano · 3.2M params |
| mAP range | Not benchmarked | 36.5–52.9across 5 sizes |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | Not available | 36.5640×640 · 3.2M params |
| Small | Not available | 44.1640×640 · 11.2M params |
| Medium | Not available | 49.2640×640 · 25.9M params |
| Large | Not available | 51.8640×640 · 43.7M params |
| XL | Not available | 52.9640×640 · 68.2M 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) |
| Model Features | ||
Both support 1 more feature | ||
YOLOv5 vs YOLOv8: Overview
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.
YOLOv8 is an object detection and multi-task vision model developed by Ultralytics, released in January 2023 under the AGPL-3.0 license. It succeeds YOLOv5 and introduces an anchor-free detection head, a new C2f module for improved gradient flow, and a decoupled head that separates classification and regression tasks. These changes improve both accuracy and training efficiency compared to earlier Ultralytics models.
YOLOv8 supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a unified codebase. It is available in five sizes from Nano to Extra Large and exports to ONNX, TensorRT, CoreML, and other formats. YOLOv8 is one of the most widely adopted detection models in production and is directly supported by Roboflow Inference for custom model training and deployment.
