ultralytics v8.4.132

v8.4.132

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

Added 1
  • List-based fraction now supports train, validation, and test splits with a third value to limit test data or skip test-image downloads entirely
Changed 12
  • NMS now uses the optimized torchvision path on supported Ascend NPU setups
  • Fraction-based sampling for local classification datasets now selects images across classes instead of taking a class-ordered prefix
  • 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
  • MLflow environment-variable handling is more flexible
  • TensorBoard setup is clearly documented as optional
Fixed 5
  • Repeated NMS time-limit warnings on Ascend NPU
  • Incomplete batches on Ascend NPU from producing incorrect mAP results
  • Classification fraction sampling to select representative subsets
  • 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
Removed 1
  • Neptune integration and settings retired following the service shutdown

From ultralytics

🌟 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
New Contributors

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.131...v8.4.132

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