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v8.3.0 - New YOLO11 Models Release (#76)

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@UltralyticsAssistant UltralyticsAssistant released this 29 Sep 17:59
0f20d14

🌟 Summary

Ultralytics YOLO11 is here! Building on the YOLOv8 foundation with R&D by @Laughing-q and @glenn-jocher in ultralytics/ultralytics#16539, YOLO11 offers cutting-edge improvements in accuracy, speed, and efficiency, redefining what's possible in real-time object detection and computer vision tasks.

YOLO11 Performance Plots

πŸ“Š Key Highlights

  • πŸš€ YOLO11 Model Unveiled: A significant upgrade over YOLOv8, YOLO11 is now the default model with enhanced architecture and optimized pipelines.
  • πŸ“š Revamped Documentation: Clearer, more detailed guides, examples, and resources to help users transition seamlessly to YOLO11.
  • πŸ› οΈ Streamlined CI & Dockerfiles: All continuous integration files and Docker environments are optimized for YOLO11, ensuring smooth workflows.
  • πŸ”„ Augmentation & Blocks Upgraded: New augmentations and block modules boost performance metrics across various tasks.
  • πŸ”§ YOLO11-Specific Configurations: Tailored model configuration files to get the most out of YOLO11's advanced features.

🎯 Purpose & Impact

  • Top-Tier Performance: YOLO11 delivers better accuracy with fewer parameters, enhancing real-time object detection and efficiency for your AI needs.
  • Versatility in Computer Vision Tasks: Supports a broader range of tasks, including object detection, instance segmentation, pose estimation, and oriented bounding box detection, adaptable across edge to cloud environments.
  • Easy Adoption: With updated resources, tutorials, and an intuitive model structure, developers can quickly adopt and maximize YOLO11's capabilities.

What's Changed

New Contributors

  • @Y-T-G made their first contribution in #65

Full Changelog: v8.2.0...v8.3.0