Co-DETR vs YOLO-NAS
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Models in this comparison
Co-DETR vs YOLO-NAS Comparison Table
Evals updated September 5, 2026Pricing updated September 8, 2026
| Property | Co-DETR | YOLO-NAS |
|---|---|---|
| Organization | OpenMMLab | Deci AI |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Nov 2022 | May 2023 |
| Context Window | — | — |
| Parameters | 304M | |
| License | MIT | Custom |
| 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 | ||
Co-DETR vs YOLO-NAS: Overview
Co-DETR (Co-Deformable-DETR) is an object detection model developed by researchers at Sense-X and OpenMMLab, released in November 2022. It improves upon standard DETR-based detectors by introducing a collaborative hybrid assignment training scheme that enables the encoder to learn from multiple auxiliary heads simultaneously, alongside the primary one-to-one assignment used during inference. This auxiliary supervision significantly accelerates convergence and improves overall detection accuracy without adding inference cost.
Co-DETR is evaluated on the COCO benchmark, where it achieves 59.5% AP when applied to DINO-Deformable-DETR with a Swin-L backbone. With a ViT-L backbone it reaches 66.0% AP on COCO test-dev, outperforming prior methods at comparable model scales. It is suitable for high-accuracy object detection tasks where training efficiency and peak performance on standard benchmarks are priorities.
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.