Compare YOLOv10 vs YOLOv9 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 YOLOv9 Comparison Table
| Property | YOLOv10 | YOLOv9 |
|---|---|---|
| Organization | THU-MIG | Academia Sinica |
| Category | open | open |
| Modality | vision | vision |
| Release Date | May 2024 | Feb 2024 |
| Context Window | — | — |
| Parameters | 2.3M-29.5M | 2.0M-57.3M |
| License | AGPL 3.0 | GPL v3 |
| Commercial use | commercial use needs a license (included on Roboflow cloud) | commercial use means releasing your source code under GPL-3.0 |
| 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 |
| Instance Segmentation | Not listed | Supported |
| Model Features | ||
| Real-Time Vision | Not listed | Supported |
YOLOv10 vs YOLOv9: 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.
YOLOv9 is a real-time object detection model developed by Chien-Yao Wang and Hong-Yuan Mark Liao at Academia Sinica, released in February 2024 under the GPL-3.0 license. It introduces Programmable Gradient Information (PGI), a mechanism that preserves complete input information through auxiliary reversible branches during training to address information loss in deep network layers. It also introduces the Generalized Efficient Layer Aggregation Network (GELAN), which achieves better parameter utilization compared to prior CSP-based designs.
YOLOv9-C achieves 53.0% AP on COCO with 42% fewer parameters and 21% less computation than YOLOv8-C at comparable accuracy. YOLOv9-E achieves 55.6% AP. The model is deployable through Roboflow Inference and supports fine-tuning via the standard training pipeline in the official repository.
