New Release: Ultralytics v8.3.65

:rocket: New Release: Ultralytics v8.3.65 :tada:

We’re excited to announce the release of Ultralytics v8.3.65, packed with new features, enhancements, and fixes designed to expand capabilities and improve performance. Here’s a quick overview of what’s new:

:star2: Summary

With this release, YOLO models now support export to Rockchip’s RKNN format, enabling seamless AI deployment on Rockchip NPUs like the RK3588 and RK3566. We’ve also included key stability improvements, modernized parts of the codebase, and ensured compatibility with more platforms to enhance developer experience. :hammer_and_wrench::bulb:


:bar_chart: Key Features and Improvements

1. Rockchip RKNN Integration

  • Export YOLO models to the RKNN format for optimized use on Rockchip hardware.
  • New tools and documentation simplify deployment workflows for Rockchip devices.
  • Hardware inference is supported via rknn-toolkit2, with device compatibility checks included.

2. Stability and Performance Enhancements

  • Improved data loader robustness: Safely handles edge-case worker cleanup to reduce crashes. :white_check_mark:
  • CI support for macOS 15, ensuring seamless compatibility with the latest macOS environments.

3. Compatibility Updates

  • Dynamic management of numpy dependencies for NVIDIA Jetson devices (e.g., Jetson Nano), streamlining TensorRT integration for better reliability. :earth_africa:

4. Codebase Modernization

  • Replaced mutable Python set with immutable frozenset throughout the codebase, reducing potential bugs in multi-threaded environments and ensuring data integrity. :rocket:

5. Documentation Enhancements

  • Cleaned and maintained regex for consistent link conversion, improving usability and simplifying updates. :writing_hand:

:dart: Purpose & Impact

Purpose

  • Expand Ultralytics YOLO deployment capabilities to Rockchip’s embedded hardware.
  • Enhance stability across modules by addressing error-prone edge cases.
  • Optimize the toolset for faster, safer, and more reliable performance in real-world use cases.

Impact

  • :brain: RKNN Support: Easy export of YOLO models to Rockchip NPUs brings high-performance AI to embedded systems.
  • :white_check_mark: Stability: Improved robustness ensures fewer crashes and smoother user experiences.
  • :rocket: Performance Optimization: More reliable operations for developers working in multi-threaded or embedded environments.
  • :books: Documentation Quality: Clearer and more user-friendly resources to support new and existing features.

:hammer_and_wrench: What’s Changed

Here’s a full breakdown of the latest changes:


:link: Useful Links


:raised_hands: Let Us Hear from You!

We invite you to try out the new features and provide your valuable feedback. Your insights are vital for the continuous improvement of our tools. Explore these updates now, and let us know what you think!

Thank you for being part of the Ultralytics community :star2: Let’s keep making AI deployment smarter and faster together! :rocket: