Ultralytics YOLO

🌟 Summary

Ultralytics v8.4.24 improves training reliability and clarity on Ultralytics Platform 🎯, while also aligning tuning defaults and docs with YOLO26 best practices 🚀.

📊 Key Changes

• (Most important) Better Platform training error surfacing 🛠️

  • Platform callback error handling now shows the actual server-side error message when available (instead of generic HTTP text).
  • If a Platform training session fails to register, users now get a clearer message:
    "Training will not be tracked on Platform".
  • After registration failure, Platform callbacks are disabled to avoid noisy follow-up errors.

• Version bump to 8.4.24 🔖

  • Package version updated from 8.4.23 to 8.4.24.

• Ray Tune search space updated to match modern YOLO26 ranges 📈

  • Important tuning ranges were corrected (like lr0, momentum, box, cls, scale).
  • Added missing parameters such as dfl and close_mosaic.
  • Docs/examples were synced so users tune with realistic defaults.

• YOLO26 naming adopted in Streamlit inference selector 🔄

  • Model options switched from yolo11* to yolo26* in the Streamlit solution UI.

• Rockchip RKNN docs refreshed with YOLO26 benchmarks 📘

  • Benchmark tables now reflect YOLO26 models and newer tested package versions.

• TF.js benchmarking disabled (temporary safeguard) ⚠️

  • TF.js export benchmarking is blocked due to a known protobuf dependency conflict, preventing confusing runtime failures.

• Platform docs and UX docs improvements 🧾

  • Clearer plan/GPU tier messaging (including Pro-only H200/B200 access).
  • Added real screenshots to key Platform docs pages.
  • Minor docs cleanup and formatting consistency fixes.

🎯 Purpose & Impact

• Faster troubleshooting for Platform users ✅
  More precise error messages reduce guesswork and support time when training jobs fail.

• Cleaner failure behavior 🧹
  Disabling callbacks after registration failure prevents repeated warning spam and gives a more stable user experience.

• Better model tuning outcomes 🎯
  Updated Ray Tune ranges help users avoid outdated search spaces that could hurt accuracy, especially with YOLO26 workflows.

• Stronger consistency across product and docs 📚
  YOLO26 naming, benchmark updates, and docs alignment make it easier for users to follow current recommended paths.

• Safer defaults for problematic integrations 🛡️
  TF.js benchmark disablement avoids known dependency traps until upstream compatibility improves.

What's Changed

• Fix Platform cloud deployments docs by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23939
• Missing screenshots placeholder updates by @t-hakobyan in https://github.com/ultralytics/ultralytics/pull/23932
• Add UTM enabled banner redirect by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23952
• Update Rockchip RKNN benchmarks with YOLO26 by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/23953
• Migrate model prefixes from YOLO11 → YOLO26 by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23954
• docs: remove duplicate Dataset Tabs heading by @amanharshx in https://github.com/ultralytics/ultralytics/pull/23940
• sync Ray Tune search space and docs with native tuner ranges by @raimbekovm in https://github.com/ultralytics/ultralytics/pull/23938
• Scope class badges to reference section by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23962
• ultralytics 8.4.24 Improve Platform train error surfacing by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23957

New Contributors

• @t-hakobyan made their first contribution in https://github.com/ultralytics/ultralytics/pull/23932
• @amanharshx made their first contribution in https://github.com/ultralytics/ultralytics/pull/23940

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.23...v8.4.24