MobileNet SSD v2 vs YOLO-NAS
Compare MobileNet SSD v2 and YOLO-NAS side-by-side.
Compare MobileNet SSD v2 vs YOLO-NAS 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.
Models in this comparison
MobileNet SSD v2 vs YOLO-NAS Comparison Table
Evals updated September 5, 2026Pricing updated September 8, 2026
| Property | MobileNet SSD v2 | YOLO-NAS |
|---|---|---|
| Organization | Deci AI | |
| Category | open | open |
| Modality | vision | vision |
| Release Date | Jan 2018 | May 2023 |
| Context Window | — | — |
| Parameters | 15.3M | |
| 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 | ||
MobileNet SSD v2 vs YOLO-NAS: Overview
MobileNet SSD v2 is a lightweight object detection model developed by Google Research, released in January 2018. It combines the MobileNetV2 backbone with the Single Shot MultiBox Detector (SSD) framework to produce a model optimized for inference on mobile and edge devices. MobileNetV2 introduces inverted residuals and linear bottlenecks to reduce computation while maintaining representational capacity compared to its predecessor.
MobileNet SSD v2 is designed for real-time on-device detection, making it suitable for mobile apps, embedded systems, and IoT devices. It performs object detection across a fixed set of categories and can be fine-tuned on custom datasets. It trades peak accuracy for reduced inference cost and model size relative to larger two-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.
