Overview

Keypoint detection, also referred to as “pose estimation” when used for humans or animals, enables you to identify specific points on an image.

For example, you can identify the orientation of a part on an assembly line with keypoint detection. This functionality could be used to ensure the orientation of the part is correct before moving to the next step in the assembly process. You could use keypoint detection to identify key points on a robotic arm, for use in measuring the envelope of the device. Finally, a common use case is human pose estimation, useful in exercise applications or factory workstation ergonomics.

You can estimate poses with YOLOv8's pose estimation model.

Use cases

Pose detection brings computer vision to a number of new possibilities. Some include:

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Sports Analytics: Analyzing athletes’ movements to improve performance and prevent injuries.

Health and Fitness: Monitoring exercises and providing feedback on form and posture.

Human-Computer Interaction: Enabling gesture-based control of devices.

Surveillance: Enhancing security systems by detecting and analyzing human activities.

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Performance

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Based on a variety of benchmarks the 4 YOLOv8 Pose Estimation models are slightly behind their newer counterparts, YOLO-NAS POSE S, M, and L. Overall, both models achieve novel accuracy in pose estimation.

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Use This Model

Label Data Automatically with YOLOv8 Pose Estimation

You can automatically label a dataset using YOLOv8 Pose Estimation with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use YOLOv8 Pose Estimation to train a computer vision model.

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