YOLO-NAS-Sat: A Small Object Detection Model for Edge Deployment

Deci is excited to present YOLO-NAS-Sat, the latest in our lineup of ultra-performant?foundation models, which includes?YOLO-NAS,?YOLO-NAS Pose, and?DeciSegs. Tailored for the accuracy demands of small object detection, YOLO-NAS-Sat serves a wide array of vital uses, from monitoring urban infrastructure and assessing changes in the environment to precision agriculture.

Available in four sizes—Small, Medium, Large, and X-large—this model is designed for peak performance in accuracy and speed on edge devices like NVIDIA’s Jetson Orin series. YOLO-NAS-Sat sets itself apart by delivering an exceptional accuracy-latency trade-off, outperforming established models like YOLOv8 in small object detection. For instance, when evaluated on the?DOTA 2.0?dataset, YOLO-NAS-Sat L achieves a 2.02x lower latency and a 6.99 higher mAP on the NVIDIA Jetson AGX ORIN with FP16 precision over YOLOV8.

YOLO-NAS-Sat’s superior performance is attributable to its innovative architecture, generated by AutoNAC, Deci’s Neural Architecture Search engine.If your application requires small object detection on edge devices, YOLO-NAS-Sat provides an off-the-shelf solution that can significantly accelerate your development process. Its specialized design and fine-tuning for small object detection ensure rapid deployment and optimal performance.

Continue reading here to explore YOLO-NAS-Sat’s architectural design, training process, and performance.