Faster R-CNN vs RF-DETR
We don't benchmark Faster R-CNN or RF-DETR on Vision Evals. Faster R-CNN is open-weight under MIT: free for commercial use. RF-DETR is open-weight under Apache 2.0: free for commercial use (XL and 2XL: PML 1.0, included on Roboflow cloud).
Compare Faster R-CNN vs RF-DETR 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.
Faster R-CNN vs RF-DETR Comparison Table
| Property | Faster R-CNN | RF-DETR |
|---|---|---|
| Organization | Microsoft | Roboflow |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Jun 2015 | Mar 2025 |
| Context Window | — | — |
| Parameters | 41.8M | 30.5M-126.9M |
| License | MIT | Apache 2.0 |
| Commercial use | free for commercial use | free for commercial use (XL and 2XL: PML 1.0, included on Roboflow cloud) |
| COCO Benchmark object detection, mAP 50:95 on COCO val2017 | ||
| Best size | Not benchmarked | 59.92XL · 126.9M params |
| Smallest size | Not benchmarked | 48.4Nano · 30.5M params |
| mAP range | Not benchmarked | 48.4–59.9across 6 sizes |
| Model Sizes COCO mAP 50:95, tested resolution and params per size | ||
| Nano | Not available | 48.4384×384 · 30.5M params |
| Small | Not available | 53.0512×512 · 32.1M params |
| Medium | Not available | 54.8576×576 · 33.7M params |
| Large | Not available | 56.3704×704 · 33.9M params |
| XL | Not available | 58.5700×700 · 126.4M params |
| 2XL | Not available | 59.9880×880 · 126.9M params |
| 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 | Supported | Demo (COCO) |
| Model Features | ||
| Real-Time Vision | Not listed | Supported |
Faster R-CNN vs RF-DETR: Overview
Faster R-CNN is an object detection model introduced by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun at Microsoft Research, published at NIPS in June 2015. It advances upon Fast R-CNN and R-CNN by introducing the Region Proposal Network (RPN), a fully convolutional network that shares features with the detection network and generates object proposals at negligible additional cost. This makes Faster R-CNN the first near-real-time deep learning object detector based on region proposals.
Faster R-CNN achieves strong detection accuracy on PASCAL VOC and MS COCO at the time of release. It remains a widely referenced architecture in computer vision research and is available through Meta's Detectron2 framework as a maintained PyTorch implementation. It is most appropriate for offline or server-side inference tasks where accuracy is prioritized over latency, as its two-stage pipeline carries higher inference cost than single-stage detectors.
RF-DETR is a real-time transformer-based object detection model developed by Roboflow, with code and weights first released in March 2025. Its largest size, 2XL, reaches 59.9 mAP 50:95 on Microsoft COCO val2017, the highest of the 60 detectors on leaderboard.roboflow.com. It is 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.
