Ultralytics YOLOv8 Tasks

YOLOv8 is an AI framework that supports multiple computer vision tasks. The framework can be used to perform detection, segmentation, obb, classification, and pose estimation. Each of these tasks has a different objective and use case.

Watch: Explore Ultralytics YOLO Tasks: Object Detection, Segmentation, OBB, Tracking, and Pose Estimation.

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

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

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

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

OBB

Oriented object detection goes a step further than regular object detection with introducing an extra angle to locate objects more accurate in an image. YOLOv8 can detect rotated objects in an image or video frame with high accuracy and speed.

Oriented Detection

Conclusion

YOLOv8 supports multiple tasks, including detection, segmentation, classification, oriented object detection 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.

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