Compare DEIM 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.
DEIM vs RF-DETR Comparison Table
| Property | DEIM | RF-DETR |
|---|---|---|
| Organization | Intellindust AI Lab | Roboflow |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Dec 2024 | Mar 2025 |
| Context Window | — | — |
| Parameters | 4M-62M | 30.5M-126.9M |
| License | Apache 2.0 | 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 | ||
Both support 1 more feature | ||
DEIM vs RF-DETR: Overview
DEIM is a training framework for DETR-based object detection models released in December 2024 by researchers at Intellindust AI Lab, City University of Hong Kong, Great Bay University, and Hefei Normal University. It enhances existing real-time DETR architectures by improving the matcher used during training, enabling faster convergence and higher accuracy without modifying the inference architecture or adding computational overhead at deployment time. DEIM introduces two core techniques: Dense One-to-One (O2O) matching, which increases the number of positive matches per target, and Matchability-Aware Loss (MAL), which down-weights low-quality matches generated by the dense strategy. The paper was accepted at CVPR 2025.
When integrated with RT-DETR and D-FINE, DEIM consistently improves performance while reducing training time by up to 50%. Applied to RT-DETRv2, it achieves 53.2% AP with a single day of training on an NVIDIA 4090 GPU. DEIM-enhanced models including DEIM-D-FINE-L and DEIM-D-FINE-X achieve 54.7% and 56.5% AP at 124 and 78 FPS respectively on an NVIDIA T4 GPU. DEIM is released under the Apache 2.0 license. A successor, DEIMv2, was released in September 2025, adding DINOv3-based backbones and introducing ultra-lightweight variants (Pico, Femto, and Atto) for edge deployment.
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.
