ultralytics 8.3.66

v8.3.66 - `ultralytics 8.3.66` add Rockchip RKNN export in `tutorial.ipynb` (#18848)

on Python PyPI

🌟 Summary

The v8.3.66 release introduces support for Rockchip RKNN export, enhances hardware compatibility, refines documentation, and fixes several bugs, marking a significant step for developers working on edge AI and cross-platform deployments.

📊 Key Changes

• ✨ Rockchip RKNN Support: Added the ability to export YOLO models to the RKNN format for deployment on Rockchip devices. Includes support for key parameters such as imgsz, batch, and name.
• 📄 Integration Documentation:
  • Rockchip RKNN: Expanded instructions, performance benchmarks, and FAQs for smoother deployment.
  • Seeed Studio reCamera: Introduced documentation for using YOLO models with the reCamera for edge AI, including ONNX and cvimodel exports.
• 🚀 Optimizations and Fixes:
  • Streamlined RKNN export code for better clarity and reliability.
  • Fixed ONNX model path issue to resolve export naming conflicts.
  • Enhanced debugging during ONNXRuntime CUDA initialization.
  • Improved label class validation logic to prevent dataset misconfigurations.
  • Updated Albumentations' ImageCompression augmentation range for higher realism.
• 📦 Testing Enhancements:
  • Added CI support for Ubuntu ARM64 builds, enhancing platform compatibility for ARM-based environments.
• 🔧 Code Improvements:
  • Introduced a custom TQDM class for consistent progress bar functionality.
  • Refactored unused arguments in modules like TorchVision and Index.
  • Adjusted optimizer logic during training for better performance in DDP setups.

🎯 Purpose & Impact

• 🚀 Expanded Hardware Reach: Rockchip RKNN and Seeed Studio reCamera integration allow effortless deployment on specialized hardware, facilitating edge AI applications like real-time object detection and energy-efficient designs.
• 🔗 Enhanced Usability: Rich documentation, benchmarks, and FAQs guide developers through complex setups, broadening accessibility for newcomers.
• ✅ More Robust Exports: RKNN and ONNX updates improve compatibility and prevent export errors, reducing troubleshooting time for developers.
• ⚡ Performance Gains: Augmentation and label validation improve model robustness and reduce errors during training and deployment across datasets and hardware.
• 🛠 Streamlined Development: Refactors simplify code maintenance while maintaining compatibility, fostering a cleaner codebase.

What's Changed

• Update thumbnail for Rockchip RKNN integration page by @lakshanthad in #18787
• Cleanup TorchVision related functions by @Y-T-G in #18790
• Fix IMX onnx model path by @Laughing-q in #18813
• New Seeedstudio reCamera Docs page by @RizwanMunawar in #18801
• Add https://youtu.be/8gePl_Zcs5c to docs by @RizwanMunawar in #18824
• Fix missing IMX500 export decorator by @glenn-jocher in #18823
• Fix spelling by @glenn-jocher in #18827
• Fix dataset category indexes check by @Laughing-q in #18840
• Ubuntu ARM GitHub CI runners by @glenn-jocher in #18762
• Fix automatic optimizer LR with DDP training by @Laughing-q in #18842
• Update inference.cpp [bug in case of cudaEnable = false] by @pmermigkas in #18831
• RKNN export clean up by @Laughing-q in #18841
• Move task=classify with mode=track warning to tracker on_predict_start by @Laughing-q in #18837
• Use TQDM class by @glenn-jocher in #18846
• Fix Albumentations ImageCompression quality_range arg by @glenn-jocher in #18847
• ultralytics 8.3.66 add Rockchip RKNN export in tutorial.ipynb by @RizwanMunawar in #18848

New Contributors

• @pmermigkas made their first contribution in #18831

Full Changelog: v8.3.65...v8.3.66