Deci AI has introduced a brand new object detection model called YOLO-NAS. YOLO-NAS stands for “You Only Look Once – Neural Architecture Search,” and it’s a game-changer in object detection. This latest model provides superior real-time object detection capabilities and production-ready performance.

Deci’s  Neural Architecture Search technology, AutoNAC™, generated the YOLO-NAS model. This engine lets users input tasks, data characteristics, inference environment, and performance targets. AutoNAC™ then guides the user to seek out the optimal architectures to offer one of the best possible balance between accuracy and speed for his or her specific application. This engine isn’t only data and hardware aware but in addition considers other components within the inference stack, equivalent to compilers and quantization. YOLO-NAS delivers state-of-the-art performance with unparalleled accuracy-speed performance. It outperforms other models, equivalent to YOLOv5, YOLOv6, YOLOv7, and YOLOv8, when it comes to accuracy and speed. In comparison with YOLOv8 and YOLOv7, YOLO-NAS is about 0.5 mAP points more accurate and 10-20% faster.

The architecture of YOLO-NAS employs quantization-aware blocks and selective quantization for optimized performance. Quantization is a method that converts floating-point models to integer models, which allows for more efficient inference on hardware that supports integer operations. When converted to the INT8 quantized version, YOLO-NAS experiences a much smaller precision drop than all other models that lose 1-2 mAP points during quantization. These techniques culminate in an modern architecture with superior object detection capabilities and top-notch performance.

YOLO-NAS’s architecture is designed to be hardware and data-agnostic, allowing it to run efficiently on various hardware platforms, including CPUs, GPUs, and accelerators. Moreover, the architecture is designed to be flexible and scalable, allowing it to be utilized in various applications, equivalent to autonomous vehicles, security systems, and robotics.

Deci’s mission is to offer AI teams with tools to assist them attain efficient inference performance more quickly, and YOLO-NAS is a testament to that mission. By leveraging the ability of AutoNAC™, Deci has developed a model that not only outperforms other models but in addition considers various components within the inference stack. This approach ends in an efficient, scalable, and versatile model, making it suitable for various applications.

In conclusion, it’s a game changer in object detection. Its superior real-time object detection capabilities and production-ready performance outperforms other models and delivers state-of-the-art performance. Deci’s mission to offer AI teams with tools to assist them attain efficient inference performance more quickly is obvious in the event of YOLO-NAS. By leveraging the ability of AutoNAC™, Deci has developed a model that’s efficient, scalable, and versatile, making it suitable for various applications.

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