ultralytics 8.3.75

v8.3.75 - `ultralytics 8.3.75` Comet update to new `comet_ml.start()` API (#19187)

on Python PyPI

🌟 Summary

The v8.3.75 release includes robust updates for improved model export compatibility, user experience, and error handling across platforms, alongside enhanced documentation and integration refinements. 🚀

📊 Key Changes

• Enhanced CometML Integration:

  • Transitioned to the new comet_ml.start() API for smoother experiment handling.
  • Deprecated the COMET_MODE variable, introducing COMET_START_ONLINE for consistency.

• Export Function Updates:

  • Protobuf Dependency: Added support for protobuf>=5 for TensorFlow and TFLite exports, resolving compatibility issues.
  • Edge TPU and TF.js: Addressed platform-specific limitations for ARM64 and Linux exports to prevent unsupported configuration errors.

• Documentation Improvements:

  • Updated SAM auto-annotation, YOLOv8, and export format descriptions for clarity.
  • Redesigned inference examples to use accessible publicly hosted image URLs.

• New CLI Solutions:

  • Introduced practical use cases, including object counting, workout monitoring, queue analysis, and browser-based inference with Streamlit.

• Benchmarking Added:

  • Include new comparative performance metrics for popular object detection models like Gold-YOLO, YOLO-NAS, RTDETRv3, etc.

• Windows-Specific Fix:

  • Resolved an async file write bug to improve caching reliability on Windows systems.

• Improved Timing Precision:

  • Switched to time.perf_counter() for latency measurements, ensuring greater precision during benchmarking.

🎯 Purpose & Impact

• Improved Experiment Tracking:

  • Seamless CometML integration provides better environment consistency and logging during training processes.

• Enhanced Export Reliability:

  • Future-proofs TensorFlow and TFLite workflows while providing early error detection for ARM64/Linux users.

• Streamlined User Experience:

  • Updated documentation and example consistency ensure clarity, especially for beginners, minimizing friction during model setup and usage.

• Greater Platform Support:

  • Addressed critical Windows and platform-specific export edge cases, enhancing cross-platform usability.

• Better Model Insights:

  • Added benchmarks empower users to make informed decisions about which object detection models to implement based on accuracy, speed, and computational cost.

This release focuses heavily on improving reliability, usability, and documentation quality while resolving critical bugs and adding more tools for diverse real-world applications.

What's Changed

• Auto-annotate and SAM docs update by @Y-T-G in #19156
• Switch to perf_counter() for latency measurement by @Y-T-G in #19177
• Force protobuf>=5 for SavedModel export by @Y-T-G in #19206
• Fix Docker QEMU issues while building JetPack 6 Dockerfile by @lakshanthad in #19216
• Fix bus.jpg path in predict.md by @RizwanMunawar in #19203
• Update NMS description on export-args.md by @Buligon in #19211
• Add NMS related args to export-table.md by @Y-T-G in #19215
• Add Docs models benchmark by @Laughing-q in #19176
• Tasks Docs updates by @LexBarou in #19181
• YOLOv8 Docs updates by @LexBarou in #19182
• Add Solutions CLI usage in quickstart.md by @RizwanMunawar in #19160
• Fix edgetpu and tfjs exports for arm64 Linux by @lakshanthad in #19154
• Fix windows async np.save bug by @eric80739 in #19218
• Fix print() for ConfusionMatrix for Classify task by @Y-T-G in #19169
• Fix updating of best epoch during early stopping by @vfcosta in #19164
• ultralytics 8.3.75 Comet update to new comet_ml.start() API by @yaricom in #19187

New Contributors

• @vfcosta made their first contribution in #19164
• @eric80739 made their first contribution in #19218

Full Changelog: v8.3.74...v8.3.75