Compare YOLOv10 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.
YOLOv10 vs YOLOv5 Comparison Table
| Property | YOLOv10 | YOLOv5 |
|---|---|---|
| Organization | THU-MIG | Ultralytics |
| Category | open | open |
| Modality | vision | vision |
| Release Date | May 2024 | Jan 2020 |
| Context Window | — | — |
| Parameters | 2.3M-29.5M | 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 · 31.7M params | Not benchmarked |
| Smallest size | 38.0Nano · 2.8M params | Not benchmarked |
| mAP range | 38.0–53.6across 5 sizes | Not benchmarked |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | 38.0640×640 · 2.8M params | Not available |
| Small | 45.7640×640 · 8.1M params | Not available |
| Medium | 50.3640×640 · 16.5M params | Not available |
| Large | 52.3640×640 · 25.8M params | Not available |
| XL | 53.6640×640 · 31.7M 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. | ||
| Vision Tasks | ||
| Object Detection | Demo (COCO) | Supported |
| Model Features | ||
| Real-Time Vision | Not listed | Supported |
YOLOv10 vs YOLOv5: Overview
YOLOv10 is a real-time end-to-end object detection model developed by THU-MIG at Tsinghua University, released in May 2024 under the AGPL-3.0 license. It introduces consistent dual assignments during training — using both one-to-many and one-to-one label assignment strategies — to eliminate the need for non-maximum suppression at inference time while maintaining competitive accuracy. This end-to-end design reduces inference latency compared to NMS-dependent detectors at similar accuracy levels.
YOLOv10-B achieves 52.7% AP on COCO with 46% lower latency than YOLOv9-C at comparable performance. The model is available in six sizes from Nano to Extra Large, built on the Ultralytics framework, and exportable to ONNX, TensorRT, and CoreML. YOLOv10 is suited for latency-sensitive deployment scenarios where post-processing overhead is a constraint.
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.
