ultralytics 8.4.132

v8.4.132 - Extend fraction to the test split (#25966)

on Python PyPI

🌟 Summary

🚀 Ultralytics v8.4.132 improves dataset efficiency, hardware compatibility, export workflows, and experiment-tracking documentation, with the headline feature being finer control over test-split downloads.

📊 Key Changes

• 🎯 Test-split control with fraction (PR #25966 — @fcakyon)

  • List-based fraction now supports train, validation, and test splits.
  • Use a third value to limit test data or set it to 0 to skip test-image downloads entirely.
  • Existing two-value lists remain compatible and continue using the full test split.
  • The same split-selection behavior is shared across training, validation, dataset conversion, and export calibration.

• ⚡ More efficient NDJSON workflows

  • Platform NDJSON runs no longer need to download unused test images, which can significantly reduce transfer time, storage use, and bandwidth during multi-dataset sweeps.

• 🩹 Corrected NMS and validation on Ascend NPU

  • NMS now uses the optimized torchvision path on supported Ascend NPU setups while continuing to avoid unsupported XPU behavior.
  • Fixes repeated NMS time-limit warnings and prevents incomplete batches from producing incorrect mAP results.

• 📚 Improved YOLO26 end-to-end and export guidance

  • Documentation now more accurately describes NMS-free detection, segmentation, pose, and OBB models.
  • Clarifies export-format fallbacks, quantization limitations, Hailo behavior, detection limits, and expected accuracy trade-offs.
  • Updates performance claims and explains when custom post-processing or NMS is still required.

• 🔄 Broader support for exported non-YOLO models

  • Documents loading generic exported models through YOLO() when task and imgsz are supplied explicitly.
  • Adds guidance for using format-specific backend classes and explains input-layout, autograd, and post-processing differences.
  • Corrects TensorFlow SavedModel, LiteRT, and numerical-parity documentation.

• 🧪 Better classification subset sampling

  • Fraction-based sampling for local classification datasets now selects images across classes instead of taking a class-ordered prefix.
  • Produces more representative subsets for quick experiments and validation.

• 📈 Updated experiment-tracking integrations

  • Ray Tune now uses its public context and reporting APIs.
  • ClearML, Comet, TensorBoard, MLflow, Ray Tune, and W&B instructions now better match current callback behavior.
  • Neptune integration and settings were retired following the service shutdown.
  • MLflow environment-variable handling is more flexible, and TensorBoard setup is clearly documented as optional.

• 🧩 Additional training and export corrections

  • RT-DETR documentation recommends disabling deterministic mode on CUDA, since its attention operation cannot provide fully deterministic backward training.
  • Copy-Paste augmentation is correctly documented for both segment and OBB tasks.
  • Classification color-jitter settings are clarified when automatic augmentation is disabled.
  • IMX exports no longer reject end-to-end models at the exporter entry point.
  • CLA workflow matching now accepts signature comments with surrounding whitespace or text.

🎯 Purpose & Impact

• 💾 Lower data-transfer costs: Users running Platform or NDJSON workflows can avoid downloading test data they do not need.
• ⏱️ Faster experimentation: Smaller, representative dataset subsets make tuning and iteration quicker without changing existing two-item fraction usage.
• 📊 More trustworthy metrics: Ascend NPU validation now processes complete batches, improving the reliability of reported mAP.
• 🚀 Easier deployment: Clearer YOLO26 export guidance helps users understand when NMS-free inference works and when a traditional NMS pipeline is necessary.
• 🌍 Better hardware coverage: NPU and export-path fixes improve compatibility across specialized accelerators and deployment formats.
• 🧭 Smoother integrations: Updated tracking documentation reduces setup confusion, while users migrating from Neptune should select an active integration such as ClearML, Comet, MLflow, TensorBoard, W&B, or the Ultralytics Platform.

What's Changed

• Match CLA signature comments by substring in the workflow gate by @glenn-jocher in #25970
• Fix NMS time-limit truncation and wrong mAP on Ascend NPU by @idwenhui in #25963
• Update experiment tracking callbacks and docs by @glenn-jocher in #25972
• Add https://youtu.be/rMllxg8ZLs8 to docs by @RizwanMunawar in #25975
• Document running non-YOLO exports through Ultralytics and correct parity claims by @raimbekovm in #25974
• Correct end-to-end detection guide claims and export fallback coverage by @raimbekovm in #25971
• Correct copy-paste task attribution and the export conf default claim by @raimbekovm in #25969
• Fix classification fraction sampling by @JESUSROYETH in #25968
• Recommend deterministic=False for RT-DETR training by @artest08 in #25965
• Extend fraction to the test split by @fcakyon in #25966

New Contributors

• @idwenhui made their first contribution in #25963

Full Changelog: v8.4.131...v8.4.132