ultralytics 8.4.127

v8.4.127 - Load exported YOLO models with the correct task across all 20 formats (#25886)

on Python PyPI

🌟 Summary

🚀 v8.4.127 makes exported YOLO models reliably load with the correct task and model family, while improving deployment stability, training recovery, and dataset documentation.

📊 Key Changes

• Correct task detection for exported models across all 20 formats by @artest08

  • Model loading now reads the task and architecture information embedded in export metadata instead of guessing from the filename or directory path.
  • Fixes segmentation exports being loaded as detection models, which previously caused masks to disappear.
  • Fixes pose exports losing keypoint outputs when weights are moved outside their training directory.
  • Correctly routes RT-DETR exports to the appropriate predictor, avoiding incorrect interpretation of its output format.
  • Applies consistently through both the Python API and CLI, making exported models safer to copy, deploy, and rename. 🎯

• Improved OpenVINO inference reliability

  • Uses the appropriate throughput setting for single-device dynamic batching.
  • Adds a targeted fix for INT8 dynamic-shape segmentation faults on Intel AMX CPUs.
  • Reduces the risk of crashes in affected CPU inference and CI environments.

• Safer training resume behavior

  • Resuming a run no longer replaces checkpoint weights with a custom pretrained model.
  • Preserves the actual model state, optimizer, scaler, EMA, and epoch from last.pt.
  • Restores distillation model handling during resume. 🔄

• More robust result handling

  • Semantic segmentation and depth result indexing now preserves complete dense maps instead of accidentally returning only one image row.
  • Improves iteration and indexing behavior for depth and semantic results.

• Expanded CoreML export support

  • nms=True now works for segmentation and pose exports in addition to detection.
  • Keeps masks and keypoints aligned with the boxes retained after suppression. 🍎

• Tracking and YOLOE fixes

  • Corrects DeepOCSORT OCR appearance matching so zero-overlap objects are not incorrectly matched.
  • Aligns TrackTrack new-track prediction and confirmation behavior with its reference implementation.
  • Fixes YOLOE prompt-free segmentation training crashes during final validation.

• Checkpoint and training reproducibility improvements

  • Custom Albumentations transforms are serialized as portable text representations rather than live Python objects.
  • YOLO26 documentation now explains the two-stage Objects365 pretraining and COCO fine-tuning process, embedded training logs, and checkpoint code revisions. 📚

• More direct Ultralytics Platform dataset access

  • Documentation now links official dataset pages for all 52 publicly hosted datasets, including detection, segmentation, pose, depth, and OBB resources.
  • Users can preview samples, inspect statistics, clone datasets, annotate, train, and deploy through the Ultralytics Platform.

• Documentation media delivery improvements

  • Documentation images and animations now use the CMS assets CDN, improving link previews and reliability across tools such as Slack and Telegram.
  • Several demonstrations were upgraded from static images to videos.

🎯 Purpose & Impact

• More dependable deployment: Exported models now retain their intended behavior even when renamed or moved into a different folder—a common step in production workflows.
• Correct predictions for specialized tasks: Segmentation masks, pose keypoints, and RT-DETR outputs are now handled by the correct predictors automatically.
• Fewer interrupted training runs: Resume behavior is safer, and OpenVINO fixes reduce crashes on supported Intel hardware.
• Better portability: Checkpoints containing custom augmentations can be loaded and shared more safely, although the original transform objects may still be needed when resuming with those augmentations.
• Broader Apple deployment options: CoreML users can now export segmentation and pose models with integrated NMS processing.
• Easier experimentation: Clearer YOLO26 training documentation and checkpoint metadata make it simpler to understand, reproduce, and fine-tune official models.
• A smoother dataset workflow: Exact Platform links make it faster to move from dataset discovery to annotation, training, and deployment. 🚀

What's Changed

• Link all official Platform datasets from Docs by @glenn-jocher in #25892
• Use correct OpenVINO throughput hint by @glenn-jocher in #25893
• Serve the docs media from the CMS assets CDN by @raimbekovm in #25884
• Ignore custom pretrained weights when resuming training by @Ajaysingh-2003 in #25816
• Preserve dense maps when indexing one-result Results by @JESUSROYETH in #25880
• fix: pass last-observation IoU to DeepOCSORT OCR appearance fusion by @JESUSROYETH in #25875
• Align TrackTrack New-track prediction and confirmation with the reference by @JESUSROYETH in #25877
• Fix YOLOE prompt-free segmentation training crash and re-enable the CLIP tests on Python 3.12 by @raimbekovm in #25867
• Document YOLO26 Objects365 pretraining and training curves by @Y-T-G in #25881
• Pick the YOLOE model and validator by task in the shared trainer by @raimbekovm in #25871
• Rewrite the YOLOE docs page and label the evaluation protocol on both LVIS result pages by @raimbekovm in #25856
• Fix OpenVINO INT8 dynamic-shape segfault on Intel AMX CPUs and replace dead docs link by @glenn-jocher in #25898
• Support nms=True for CoreML Segment and Pose exports by @SergioAlmeida29 in #25873
• Serialize custom augmentations losslessly in checkpoints by @Rahulbiradar9 in #25891
• Accept Path filenames in imread by @Kagura-Ahad in #25888
• Fit TQDM output to terminal width by @Y-T-G in #25166
• Stream live progress bar updates to console log consumers by @Y-T-G in #25900
• Load exported YOLO models with the correct task across all 20 formats by @artest08 in #25886

New Contributors

• @Ajaysingh-2003 made their first contribution in #25816
• @Kagura-Ahad made their first contribution in #25888

Full Changelog: v8.4.126...v8.4.127