YOLOv8
| Property | Value |
|---|---|
| Author | Ultralytics |
| License | AGPL-3.0 |
| Tasks | Object Detection, Segmentation, Classification, Pose Estimation |
What is YOLOv8?
YOLOv8 represents the latest advancement in Ultralytics' YOLO series, offering state-of-the-art performance in various computer vision tasks. This versatile model builds upon previous YOLO versions while introducing significant improvements in both speed and accuracy.
Implementation Details
The model comes in multiple variants (n, s, m, l, x) catering to different computational requirements. For instance, YOLOv8n (nano) achieves 37.3 mAP on COCO while maintaining high speed, while YOLOv8x delivers 53.9 mAP with increased computational complexity.
- Supports multiple tasks including detection, segmentation, classification, and pose estimation
- Offers CPU and GPU compatibility with ONNX and TensorRT optimization
- Includes comprehensive Python API and CLI interface
- Features real-time processing capabilities with high accuracy
Core Capabilities
- Object Detection with up to 80 pre-trained classes
- Instance Segmentation with pixel-level accuracy
- Pose Estimation supporting keypoint detection
- Image Classification with 1000 ImageNet classes
- Real-time tracking functionality
Frequently Asked Questions
Q: What makes this model unique?
YOLOv8 combines exceptional speed and accuracy while offering multiple task capabilities in a single framework. It's designed for both research and production environments with extensive optimization options.
Q: What are the recommended use cases?
The model excels in real-time applications including surveillance, autonomous systems, industrial inspection, and medical imaging. Its various sizes allow deployment from edge devices to high-performance servers.
