Compare YOLOv12 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.
YOLOv12 vs YOLOv9 Comparison Table
| Property | YOLOv12 | YOLOv9 |
|---|---|---|
| Organization | THU-MIG | Academia Sinica |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Feb 2025 | Feb 2024 |
| Context Window | — | — |
| Parameters | 2.6M-59.1M | 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 |
| Vision Tasks | ||
| Classification | Supported | Not listed |
| Pose Estimation | Supported | Not listed |
Both support 2 more tasks | ||
| Model Features | ||
Both support 1 more feature | ||
YOLOv12 vs YOLOv9: Overview
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.
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.
