RF-DETR vs YOLOv12
Compare RF-DETR and YOLOv12 side-by-side.
Compare RF-DETR 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.
Models in this comparison
RF-DETR vs YOLOv12 Comparison Table
Evals updated October 13, 2026Pricing updated October 22, 2026
| Property | RF-DETR | YOLOv12 |
|---|---|---|
| Organization | Roboflow | THU-MIG |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Mar 2025 | Feb 2025 |
| Context Window | — | — |
| Parameters | 30.5M-126.9M | 2.6M-59.1M |
| License | Apache 2.0 | AGPL 3.0 |
| Commercial use | free for commercial use (XL and 2XL: PML 1.0, included on Roboflow cloud) | commercial use needs a license (included on Roboflow cloud) |
| Model Sizes input resolution, COCO mAP 50:95 and params per size | ||
| Nano | 384×384 48.4 mAP 30.5M params | Not available |
| Small | 512×512 53.0 mAP 32.1M params | Not available |
| Medium | 576×576 54.8 mAP 33.7M params | Not available |
| Large | 704×704 56.3 mAP 33.9M params | Not available |
| XL | 700×700 58.5 mAP 126.4M params | Not available |
| 2XL | 880×880 59.9 mAP 126.9M 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 | Supported | Supported |
RF-DETR vs YOLOv12: Overview
RF-DETR is a real-time transformer-based object detection model developed by Roboflow, with code and weights first released in March 2025. RF-DETR Nano, Small, Medium, and Large are released under the Apache 2.0 license, so they're commercially usable out of the box: you can ship them in closed-source, commercial products, including self-hosted deployments, without paying a license fee or open-sourcing your code. RF-DETR XL and 2XL use the Roboflow Platform Model License (PML 1.0): commercial use is included when you deploy on Roboflow, and commercial self-hosting requires the Enterprise add-on. It is the first real-time model to exceed 60 AP on the Microsoft COCO benchmark, built on a DINOv2 vision transformer backbone with weight-sharing neural architecture search used to identify accuracy-latency trade-offs. The full family spans six sizes from Nano (30.5M parameters, 384×384 input) to 2XL (126.9M parameters, 880×880 input), with the accompanying research paper accepted to ICLR 2026.
RF-DETR is designed for strong domain adaptability, achieving state-of-the-art performance on RF100-VL, a benchmark measuring generalization to real-world object detection tasks across diverse domains. It is deployable through Roboflow Inference and supports fine-tuning on custom datasets, making it well suited for domain-specific applications with limited training data.
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.
