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Ultralytics YOLOv8 Tasks
YOLOv8 is an AI framework that supports multiple computer vision tasks. The framework can be used to perform detection, segmentation, classification, and pose estimation. Each of these tasks has a different objective and use case.
Detection
Detection is the primary task supported by YOLOv8. It involves detecting objects in an image or video frame and drawing bounding boxes around them. The detected objects are classified into different categories based on their features. YOLOv8 can detect multiple objects in a single image or video frame with high accuracy and speed.
Detection Examples{ .md-button .md-button--primary}
Segmentation
Segmentation is a task that involves segmenting an image into different regions based on the content of the image. Each region is assigned a label based on its content. This task is useful in applications such as image segmentation and medical imaging. YOLOv8 uses a variant of the U-Net architecture to perform segmentation.
Segmentation Examples{ .md-button .md-button--primary}
Classification
Classification is a task that involves classifying an image into different categories. YOLOv8 can be used to classify images based on their content. It uses a variant of the EfficientNet architecture to perform classification.
Classification Examples{ .md-button .md-button--primary}
Pose
Pose/keypoint detection is a task that involves detecting specific points in an image or video frame. These points are referred to as keypoints and are used to track movement or pose estimation. YOLOv8 can detect keypoints in an image or video frame with high accuracy and speed.
Pose Examples{ .md-button .md-button--primary}
Conclusion
YOLOv8 supports multiple tasks, including detection, segmentation, classification, and keypoints detection. Each of these tasks has different objectives and use cases. By understanding the differences between these tasks, you can choose the appropriate task for your computer vision application.