Ultralytics Files

🌟 Summary

Ultralytics v8.4.33 focuses on a key training reliability fix for end-to-end YOLO workflows, plus improvements to Ray Tune flexibility and CoreML export stability 🚀

📊 Key Changes

• 🔧 Major fix (current PR [#24074] by @Laughing-q): Resume-training for end-to-end models now restores loss state correctly
• Updated resume_training() to properly reinitialize and sync the model’s loss criterion when loading checkpoints.
• Restores internal loss update counters so one-to-one and one-to-many training behavior continues correctly after resume.

• Version bumped from 8.4.32 to 8.4.33.

• 🧠 Ray Tune upgrades (PR [#23946] by @lmycross): More search algorithms supported in YOLO26 tuning

• Added search_alg to model.tune(..., use_ray=True) with options like optuna, hyperopt, bohb, ax, nevergrad, zoopt, random, and more.
• Improved compatibility handling for algorithms with special search-space needs.
• Renamed internal trial-count handling to align on iterations (clearer API behavior).
• BOHB now automatically uses the appropriate scheduler (HyperBandForBOHB) instead of generic defaults.

• Added docs/tests and improved default search space typing (for example close_mosaic now uses integer sampling).

• 🍎 CoreML export fix (PR [#24078] by @glenn-jocher): Better detection export with NMS

• CoreML pipeline now receives explicit model output shape when exporting detection models with nms=True.
• Reduces shape-related export/runtime issues on Apple devices.

🎯 Purpose & Impact

• More reliable resumed training ✅
  If training is interrupted and resumed from a checkpoint, end-to-end models now continue with the correct loss progression instead of partially reset behavior. This is the most important user-facing fix in this release.

• Stronger hyperparameter tuning workflows 🎯
  YOLO26 users get more control over search strategy in Ray Tune, making large-scale tuning more adaptable to different infrastructure and optimization preferences.

• Smoother mobile/Apple deployment 📱
  CoreML detection exports with integrated NMS are more robust, lowering chances of deployment-time surprises.

• Bottom line: v8.4.33 is a stability + flexibility release—especially valuable for users resuming long trainings and teams doing advanced automated tuning.

What's Changed

• Add support for multiple ray tune search algorithms by @lmycross in https://github.com/ultralytics/ultralytics/pull/23946
• Pass CoreML output_shape arg by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/24078
• ultralytics 8.4.33 Progressive loss train resume fix by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/24074

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.32...v8.4.33