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

- Product: ultralytics (https://whatsnew.fyi/product/ultralytics)
- Vendor: ultralytics
- Date: 2026-08-28
- Version: v8.4.132
- Original notes: https://github.com/ultralytics/ultralytics/releases/tag/v8.4.132
- Permalink: https://whatsnew.fyi/product/ultralytics/releases/v8.4.132

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- **added** — 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** — NMS now uses the optimized torchvision path on supported Ascend NPU setups
- **changed** — Fraction-based sampling for local classification datasets now selects images across classes instead of taking a class-ordered prefix
- **changed** — Ray Tune now uses its public context and reporting APIs
- **changed** — ClearML, Comet, TensorBoard, MLflow, Ray Tune, and W&B instructions now better match current callback behavior
- **changed** — MLflow environment-variable handling is more flexible
- **changed** — TensorBoard setup is clearly documented as optional
- **changed** — Documentation now more accurately describes NMS-free detection, segmentation, pose, and OBB models
- **changed** — Export-format fallbacks, quantization limitations, Hailo behavior, detection limits, and expected accuracy trade-offs are now clarified
- **changed** — Added guidance for using format-specific backend classes and explains input-layout, autograd, and post-processing differences
- **changed** — RT-DETR documentation recommends disabling deterministic mode on CUDA
- **changed** — Copy-Paste augmentation is correctly documented for both segment and OBB tasks
- **changed** — Classification color-jitter settings are clarified when automatic augmentation is disabled
- **fixed** — Repeated NMS time-limit warnings on Ascend NPU
- **fixed** — Incomplete batches on Ascend NPU from producing incorrect mAP results
- **fixed** — Classification fraction sampling to select representative subsets
- **fixed** — IMX exports no longer reject end-to-end models at the exporter entry point
- **fixed** — CLA workflow matching now accepts signature comments with surrounding whitespace or text
- **removed** — Neptune integration and settings retired following the service shutdown

##### 🌟 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

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