Deci AI has introduced a new object detection model called YOLO-NAS. YOLO-NAS stands for “You Only Look Once – Neural Architecture Search” and is a game changer in object detection. This new model offers superior real-time object detection capabilities and production-ready performance.
AutoNAC™, Deci’s Neural Architecture Search technology, generated the YOLO-NAS model. This engine allows users to input tasks, data characteristics, inference environments, and performance goals. AutoNAC™ then guides the user to find the optimal architecture that achieves the best balance of accuracy and speed for a given application. The engine is not only data and hardware aware, but also considers other components in the inference stack, such as compilers and quantizers. YOLO-NAS offers cutting-edge performance with unmatched precision and speed performance. It outperforms other models such as YOLOv5, YOLOv6, YOLOv7 and YOLOv8 in terms of accuracy and speed. Compared to 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 technique for converting a floating-point model to an integer model. This allows for more efficient inference on hardware that supports integer arithmetic. When converted to the INT8 quantized version, YOLO-NAS suffers much less accuracy loss than all other models that lose 1-2 mAP points during quantization. These techniques culminate in an innovative architecture with superior object detection capabilities and top performance.
The architecture of YOLO-NAS is designed to be hardware and data agnostic, allowing it to run efficiently on various hardware platforms such as CPUs, GPUs, and accelerators. Additionally, the architecture is designed to be flexible and scalable, so it can be used in a variety of applications such as autonomous vehicles, security systems, and robotics.
Deci’s mission is to provide tools that help AI teams achieve efficient inference performance faster, and YOLO-NAS is a testament to that mission. By leveraging the power of AutoNAC™, Deci has developed a model that not only outperforms other models, but also considers various components within the inference stack. This approach results in an efficient, scalable, and flexible model, suitable for a wide variety of applications.
In conclusion, this is a game changer for object detection. Its superior real-time object detection capabilities and production-ready performance outperform other models and offer cutting-edge performance. His Deci mission to provide tools that help AI teams achieve efficient inference performance more quickly is evident in the development of YOLO-NAS. By harnessing the power of AutoNAC™, Deci has developed an efficient, scalable and flexible model, making it suitable for a wide variety of applications.
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Niharika is a technical consulting intern at Marktechpost. She is in her third year of undergraduate studies and is currently completing her Bachelor’s degree at the Indian Institute of Technology (IIT), Kharagpur. She is a very passionate person who has a keen interest in machine learning, data her science, AI and avid reader of the latest developments in these fields.
