Ultralytics YOLO

🌟 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