Home Introducing YOLO-NAS Pose – SOTA Performance Pose Estimation Using Neural Architecture Search

Introducing YOLO-NAS Pose – SOTA Performance Pose Estimation Using Neural Architecture Search

by Ant Sh
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Introducing YOLO-NAS Pose – SOTA Performance Pose Estimation Using Neural Architecture Search

Deci AI has recently introduced the YOLO-NAS Pose foundation model, which can be considered a leap in pose estimation technology.

Pose estimation plays a crucial role in computer vision, encompassing a wide range of important applications, including:

  • monitoring patient movements in healthcare
  • analyzing the performance of athletes in sports
  • creating seamless human-computer interfaces and improving robotic systems.

The pose estimation models take a processed camera image as the input and outputs information about key points. The key points detected are indexed by a part ID, with a confidence score between 0.0 and 1.0. The confidence score shows the probability that a key point exists in that position.

According to the developers of the model, its main advantages are:

✅ All its 4 model variants deliver a significant boost in accuracy compared to their equivalent YOLOv8 Pose variants, redefining industry benchmarks.

✅ Comparing across variants: YOLO-NAS Pose M variant outperforms YOLOv8 Pose L with 38.85% lower latency on Intel Xeon 4th gen CPUs and a 0.27 increase in [email protected] score. YOLO-NAS Pose S delivers 50.8% lower latency with similar accuracy.

✅ It executes two tasks simultaneously: detecting persons and estimating their poses in one swift pass, making it the go-to solution for applications requiring real time insights.

✅ Deployment-friendly architecture ensures effortless integration, especially with its one-line command for inference.

✅ It efficiently merges both object detection and pose estimation, providing a streamlined approach with an excellent accuracy/latency tradeoff.

✅ It’s highly suitable for tasks that involve environments with varying numbers of subjects—from solitary individuals to large crowds.