ultralytics/ultralytics v8.3.146

v8.3.146 - `ultralytics 8.3.146` New COCO8-Grayscale dataset (#20827)

on GitHub

🌟 Summary

Ultralytics 8.3.146 introduces full support for grayscale object detection workflows, highlighted by the new COCO8-Grayscale dataset, a dedicated grayscale YOLO11n model, and comprehensive grayscale testing and documentation. 🖤📦

📊 Key Changes

• COCO8-Grayscale Dataset Added:
  • A small, single-channel (grayscale) version of the COCO8 dataset is now available for rapid testing and debugging.
  • Includes a YAML config, download script, and full documentation.
• Grayscale Model Support:
  • The new yolo11n-grayscale.pt model is now available for download and use.
  • Test suites updated to cover grayscale training, validation, and prediction.
• Documentation Enhancements:
  • New docs page for COCO8-Grayscale with usage examples, FAQs, and integration tips for YOLO11 and Ultralytics HUB.
  • Dataset indexes updated to include COCO8-Grayscale.
• Other Improvements:
  • Added a deprecation notice for Neural Magic integrations.
  • Improved analytics chart performance and visuals.
  • Enhanced code quality with better type hints and docstrings.
  • Various documentation cleanups and minor bug fixes.

🎯 Purpose & Impact

• Broader Research & Application:
  • Enables users to easily experiment with and benchmark grayscale object detection, which is valuable for medical imaging, industrial inspection, and other fields where grayscale data is common.
• Faster Prototyping:
  • The small COCO8-Grayscale dataset allows for quick pipeline checks before scaling to larger datasets, saving time and resources.
• Seamless Integration:
  • Full compatibility with YOLO11 and Ultralytics HUB ensures users can leverage cloud training, monitoring, and dataset management for grayscale projects.
• Improved User Experience:
  • Smoother analytics, clearer documentation, and robust model export/benchmarking processes make the platform more reliable and user-friendly.
• Future-Proofing:
  • Codebase improvements and deprecation notices help users and developers stay up-to-date and avoid deprecated tools.

In summary:
This release makes Ultralytics a more versatile platform for both color and grayscale object detection, while also delivering a range of usability, performance, and documentation improvements. 🚀

What's Changed

• Docs - adding neural-magic deprecation notice by @Burhan-Q in #20817
• Clean up: use {} instead of set([]) by @RizwanMunawar in #20803
• Update type hints and docstrings by @Laughing-q in #20812
• Reuse existing r_s variable for intersection check by @RizwanMunawar in #20829
• Remove duplicate warning in tracker docs by @Y-T-G in #20826
• Optimize analytics graph rendering by updating every X frames by @RizwanMunawar in #20823
• Remove save_crop from validation arguments by @Y-T-G in #20821
• Fix NoneType result when using ReID with CLI by @Y-T-G in #20814
• Refactor benchmark checks to use format names instead of indices by @lakshanthad in #20819
• Standardize YOLO-NAS docstrings with other models by @RizwanMunawar in #20824
• Fix uv always installing to --system environment by @Burhan-Q in #20837
• ultralytics 8.3.146 New COCO8-Grayscale dataset by @Laughing-q in #20827

Full Changelog: v8.3.145...v8.3.146