RF-DETR vs YOLO-NAS
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RF-DETR vs YOLO-NAS Comparison Table
Evals updated September 5, 2026Pricing updated September 8, 2026
| Property | RF-DETR | YOLO-NAS |
|---|---|---|
| Organization | Roboflow | Deci AI |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Mar 2025 | May 2023 |
| Context Window | — | — |
| Parameters | 30.5M-126.9M | |
| License | Apache 2.0 | Custom |
| Model Sizes input resolution per size variant | ||
| Nano | 384×384 | |
| Small | 512×512 | 640×640 |
| Medium | 576×576 | 640×640 |
| Large | 704×704 | 640×640 |
| XL | 700×700 | |
| 2XL | 880×880 | |
| Vision Tasks | ||
| Object Detection | Demo (COCO) | Demo (COCO) |
| Model Features | ||
| Real-Time Vision | ||
RF-DETR vs YOLO-NAS: Overview
RF-DETR is a real-time transformer-based object detection model developed by Roboflow, with code and weights first released in March 2025 under the Apache 2.0 license. 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.
YOLO-NAS is an object detection model developed by Deci AI, released in May 2023 as part of the super-gradients open-source training library. The architecture was generated using Deci's proprietary Neural Architecture Search technology, AutoNAC, which searches for network structures that balance accuracy and inference latency on target hardware. This produced three model sizes (small, medium, and large) featuring quantization-friendly blocks that reduce accuracy loss when converting weights to INT8 precision for deployment on edge devices and mobile hardware.
YOLO-NAS achieves competitive accuracy-latency tradeoffs against YOLOv5, YOLOv6, YOLOv7, and YOLOv8 on the Microsoft COCO benchmark at release, and ships with pretraining on Objects365 in addition to COCO. Note that YOLO-NAS uses a custom license: the surrounding super-gradients framework code is Apache-2.0, but the YOLO-NAS model weights are released under a separate non-commercial license that restricts production and commercial use. Teams evaluating YOLO-NAS for commercial applications should review the LICENSE.YOLONAS.md terms directly. Deci AI was acquired by NVIDIA in April 2024, and the super-gradients repository is no longer actively maintained by the original team. Users can still download and use the released weights, but no further updates or new variants are expected.