This pose estimation model skillfully and effectively detects individual movements while simultaneously estimating their poses, making it ideal for real-time applications on edge devices across industries.
Deci, the deep learning company harnessing AI to build AI, announced the launch of YOLO-NAS Pose, a groundbreaking pose estimation model generated with Deci’s cutting-edge Automated Neural Architecture Construction (AutoNAC) engine, the most advanced Neural Architecture Search (NAS)-based technology on the market. This revolutionary model is redefining capabilities in the technical pose estimation domain, demonstrating unparalleled accuracy and latency performance.
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Pose estimation, a computer vision technique enabling the precise determination of human or object positions in space, is a broad spectrum of sectors, including monitoring patient movements in healthcare, evaluating athletic performances, and crafting intuitive human-computer interfaces to enhance the capabilities of robotic systems. With superior latency-accuracy performance that eclipses other state-of-the-art models in the space, including YOLOv8 Pose, YOLO-NAS Pose holds the potential to transform industries, including healthcare, security, and beyond.
With four bespoke size variants (Nano, Small, Medium & Large), YOLO-NAS Pose addresses a wide range of computational needs. All four variants deliver significantly higher accuracy with similar or lower latency compared to their YOLOv8 Pose equivalent model variants. When comparing across variants, a significant boost in speed is evident. For example, the YOLO-NAS Pose M variant boasts 38% lower latency and achieves a +0.27 AP higher accuracy over YOLOV8 Pose L, measured on Intel Gen 4 Xeon CPUs. YOLO-NAS Pose excels at efficiently detecting objects while concurrently estimating their poses, making it the go-to solution for applications requiring real time insights.
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“We’re excited to introduce YOLO-NAS Pose, a testament to our commitment to pushing the boundaries of AI for practical, real-world applications,” said Yonatan Geifman, CEO and Co-Founder of Deci. “Our AutoNAC technology is the powerhouse behind this advancement, allowing us to consistently craft models that not only achieve new performance heights but also unlock transformative use cases. We believe this model will be a vital asset to developers in the field of pose estimation and look forward to seeing the innovative ways in which it will be employed.”
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