🌟 Summary
Ultralytics YOLO11 is here! Building on the YOLOv8 foundation with R&D by @Laughing-q and @glenn-jocher in #16539, YOLO11 offers cutting-edge improvements in accuracy, speed, and efficiency, redefining what's possible in real-time object detection and computer vision tasks.
📊 Key Highlights
- 🚀 YOLO11 Model Unveiled: A significant upgrade over YOLOv8, YOLO11 is now the default model with enhanced architecture and optimized pipelines.
- 📚 Revamped Documentation: Clearer, more detailed guides, examples, and resources to help users transition seamlessly to YOLO11.
- 🛠️ Streamlined CI & Dockerfiles: All continuous integration files and Docker environments are optimized for YOLO11, ensuring smooth workflows.
- 🔄 Augmentation & Blocks Upgraded: New augmentations and block modules boost performance metrics across various tasks.
- 🔧 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
- Update test_exports.py by @glenn-jocher in #16527
- Fix
hand-keypoints.yamlimage counts by @jk4e in #16528 - Update Dockerfile-python by @glenn-jocher in #16529
- Use
apt-getin Dockerfiles by @glenn-jocher in #16535 ultralytics 8.3.0YOLO11 Models Release by @glenn-jocher in #16539
Full Changelog: v8.2.103...v8.3.0