Faster R-CNN vs YOLO-NAS
Compare Faster R-CNN and YOLO-NAS side-by-side.
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Models in this comparison
Faster R-CNN vs YOLO-NAS Comparison Table
Evals updated October 13, 2026Pricing updated October 25, 2026
| Property | Faster R-CNN | YOLO-NAS |
|---|---|---|
| Organization | Microsoft | Deci AI |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Jun 2015 | May 2023 |
| Context Window | — | — |
| Parameters | 41.8M | Unknown |
| License | MIT | Custom |
| Commercial use | free for commercial use | weights license restricts commercial use (Deci's consent needed) |
| Model Sizes input resolution per size variant | ||
| Small | Not available | 640×640 |
| Medium | Not available | 640×640 |
| Large | Not available | 640×640 |
| Vision Tasks | ||
| Object Detection | Supported | Demo (COCO) |
| Model Features | ||
| Real-Time Vision | Not listed | Supported |
Faster R-CNN vs YOLO-NAS: 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.
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.