🌟 Summary

This update primarily fixes CoreML segmentation output handling, improves documentation, and enhances the usability of model features for developers and end users. 🔄✨

📊 Key Changes

• CoreML Segmentation Fix: Improved logic for processing segmentation model outputs in autobackend.py (fixed reverse order issue for specific use cases).
• Docker Update: Dockerfile upgraded to PyTorch 2.5.1 (with CUDA 12.4 and cuDNN 9), enabling improved compatibility and performance for Docker-based workflows. 🐳⚡
• Colab Integrations: Added Colab badges to various documentation pages for easy, hands-on experimentation with datasets and tutorials. 📚🔗
• Enhanced Auto-Annotation Documentation: Updated guides for segmentation auto-annotation, adding clarity around supported models and parameter configurations. 🖼️✅
• Bug Reporting Improvements: Adjusted GitHub issue templates to request full traceback info for better debugging efficiency. 🛠️🔍
• Standardized String Formatting: Converted strings to consistently use double-quoted f-strings for better code clarity and maintainability. 🖊️

🎯 Purpose & Impact

• CoreML Update:

  • 🛠 Purpose: Fix and streamline CoreML model support, specifically for segmentation outputs.
  • 🌟 Impact: Smoother deployment for Apple-device-specific workflows with reduced risk of errors in segmentation processing.

• Docker Upgrade:

  • 🚀 Purpose: Ensure containerized environments stay up-to-date and performant with compatibility fixes.
  • 🌟 Impact: Faster inference and training workflows with enhanced reliability.

• Colab Additions:

  • 🛠 Purpose: Enable effortless model experimentation with interactive tools directly from the documentation.
  • 🌟 Impact: Lowers the entry barrier for new users while improving developer productivity.

• Auto-Annotation Improvements:

  • 🎯 Purpose: Clarify how to use segmentation models like SAM and MobileSAM for large datasets.
  • 🌟 Impact: Saves time in dataset labeling by simplifying setup and enabling quick-start options.

• Standardized String Formatting:

  • 🖊️ Purpose: Improve code readability and ease of maintenance for developers.
  • 🛡 Impact: Cleaner, more professional code with improved developer experience.

• Bug Reporting Guidelines:

  • 🚨 Purpose: Collect more detailed user environment data to speed up issue resolution.
  • 🌍 Impact: Quicker turnaround in fixing bugs due to detailed diagnostic info.

No breaking changes in this release, ensuring smooth upgrades across workflows! 🛡💡

What's Changed

• Apply Ruff 0.9.0 by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18622
• Add new Colab Notebooks badges to Docs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18575
• Apply ruff==0.9.0 formatting by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18624
• Update val.md by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18645
• Update issue templates with better instructions by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/18346
• Dockerfile FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18650
• Add warning for task=classify with mode=track by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18621
• Warn and set task=detect and mode=track for task=track by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18620
• Add mobile-sam auto-annotation to segmentation datasets docs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18654
• ultralytics 8.3.60 Fix CoreML Segment inference by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/18649

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.59...v8.3.60