YOLO-NAS vs YOLOv7
Compare YOLO-NAS and YOLOv7 side-by-side.
Compare YOLO-NAS vs YOLOv7 live
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Models in this comparison
YOLO-NAS vs YOLOv7 Comparison Table
Evals updated September 5, 2026Pricing updated September 20, 2026
| Property | YOLO-NAS | YOLOv7 |
|---|---|---|
| Organization | Deci AI | Academia Sinica |
| Category | open | open |
| Modality | vision | vision |
| Release Date | May 2023 | Jul 2022 |
| Context Window | — | — |
| Parameters | 6.2M-151.7M | |
| License | Custom | GPL v3 |
| Model Sizes input resolution per size variant | ||
| Small | 640×640 | |
| Medium | 640×640 | |
| Large | 640×640 | |
| Vision Tasks | ||
| Object Detection | Demo (COCO) | |
| Model Features | ||
| Real-Time Vision | ||
YOLO-NAS vs YOLOv7: Overview
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 Deci AI was acquired by NVIDIA in April 2024, and the super-gradients repository is no longer actively maintained by the original team.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.
YOLOv7 is a real-time object detection model developed by Chien-Yao Wang and Hong-Yuan Mark Liao at Academia Sinica, released in July 2022 under the GPL-3.0 license. It introduces Extended Efficient Layer Aggregation Networks (E-ELAN) for improved gradient flow in the backbone, and trainable bag-of-freebies techniques including coarse-to-fine lead guided label assignment and auxiliary heads that improve accuracy without adding inference cost.
YOLOv7 achieves 56.8% AP on COCO at 30 FPS on a V100 GPU at the time of release, establishing a strong accuracy-speed tradeoff among real-time detectors. It supports detection, instance segmentation, and pose estimation variants. YOLOv7 is deployable through Roboflow Inference and the standard training pipeline in the official repository.
