Compare YOLOv10 vs YOLOv12 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 YOLOv12 Comparison Table
| Property | YOLOv10 | YOLOv12 |
|---|---|---|
| Organization | THU-MIG | THU-MIG |
| Category | open | open |
| Modality | vision | vision |
| Release Date | May 2024 | Feb 2025 |
| Context Window | — | — |
| Parameters | 2.3M-29.5M | 2.6M-59.1M |
| 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 |
| Classification | Not listed | Supported |
| Instance Segmentation | Not listed | Supported |
| Pose Estimation | Not listed | Supported |
| Model Features | ||
| Real-Time Vision | Not listed | Supported |
YOLOv10 vs YOLOv12: 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.
YOLOv12 is an attention-centric real-time object detection model developed by researchers at Tsinghua University, with the arXiv paper published in February 2025 under the AGPL-3.0 license. It introduces an Area Attention module that partitions feature maps into regions and applies self-attention within each region, reducing the quadratic complexity of full self-attention while capturing long-range dependencies. It also incorporates R-ELAN for improved feature aggregation and scaled residual connections for training stability.
YOLOv12-L achieves 54.0% AP on COCO, while the YOLOv12-N variant achieves 40.5% mAP at 1.62ms latency on an NVIDIA T4 GPU. The model is built on the Ultralytics codebase, supporting detection, segmentation, and other standard YOLO tasks at competitive real-time speeds.
