YOLOv8 is a state-of-the-art, real-time object detection and image segmentation model. It builds on the success of previous YOLO versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports a full range of vision AI tasks, including detection, segmentation, pose estimation, tracking, and classification.
YOLOv8 is an anchor-free model, which means it predicts directly the center of an object instead of the offset from a known anchor box. This reduces the number of box predictions, which speeds up Non-Maximum Suppression (NMS), a complicated post processing step that sifts through candidate detections after inference.
YOLOv8 is also built on a new architecture called MobileNetV3, which is designed for mobile and embedded devices. This makes YOLOv8 more efficient and portable than previous YOLO models, making it ideal for use in a wide range of applications, from edge devices to cloud APIs.
Here are some of the key features of YOLOv8:
- State-of-the-art object detection and image segmentation performance
- Fast and accurate
- Easy to use
- Supports a wide range of vision AI tasks
- Anchor-free model
- Built on MobileNetV3 architecture
YOLOv8 is a powerful and versatile model that can be used for a wide range of applications. If you are looking for a state-of-the-art object detection and image segmentation model that is fast, accurate, and easy to use, then YOLOv8 is a great choice.
Here are some of the use cases for YOLOv8:
- Self-driving cars
- Security cameras
- Medical imaging
- Retail inventory management
- Drones
- Robotics
- And more!