ultralytics v8.4.133

🌟 Summary

Ultralytics 8.4.133 improves hyperparameter tuning convergence, speeds up inference preprocessing, expands detection metrics, and simplifies edge-device setup. 🚀

📊 Key Changes

• Smarter hyperparameter tuning — PR #25984 by @glenn-jocher

  • Replaces coordinate-by-coordinate crossover with fitness-weighted selection of complete, high-performing configurations.
  • Preserves useful relationships between hyperparameters instead of mixing them independently.
  • Mutates approximately half of the parameters in normalized search-space coordinates, allowing parameters that start at zero—such as degrees or shear—to evolve more effectively.
  • Gradually reduces mutation size when tuning stops finding better results, encouraging refinement after broad exploration.
  • Prevents duplicate candidates after clipping, rounding, or integer conversion, including small and discrete search spaces.
  • Ray Tune now defaults to Optuna multivariate TPE, with parallel-aware suggestions rather than independent random search.

• Faster predictor preprocessing — PR #25982 by @jahsef ⚡

  • Moves image channel reordering and tensor-contiguity operations from CPU-side NumPy processing to the inference device.
  • Preserves output values while reducing unnecessary CPU copies.
  • Reported benchmarks show approximately 2.2–3.1× faster preprocessing on an RTX 5080, with additional gains on CPU.

• Automatic channels-last CPU inference — PR #25983 by @JESUSROYETH

  • Enables channels-last memory layout automatically for native PyTorch inference and standalone validation on supported x86 Linux and Windows CPUs with oneDNN.
  • Keeps training defaults and unsupported platforms unchanged.
  • Explicit channels_last=True remains available for supported CPU and CUDA paths.
  • Saved models are converted back to a safe contiguous format and stale EMA data is cleared to improve compatibility.

• More accurate INT8 calibration subsets — PR #25978 by @JESUSROYETH

  • Fixes fraction handling during classification and detection INT8 export calibration.
  • Scalar fractions now apply directly to the selected calibration split, while list-based fractions retain train/validation/test behavior.
  • Prevents exports from unintentionally calibrating on an entire dataset when only a subset was requested.

• Size-specific mAP for custom detection datasets — PR #25981 by @fcakyon 📈

  • Custom detection datasets can now report small-, medium-, and large-object mAP when using save_json=True.
  • Builds temporary COCO-format annotations internally while preserving existing native metrics and prediction files.
  • Applies consistently during training validation, final-model validation, and standalone validation.

• Simpler edge-device installation

  • Raspberry Pi, Jetson, DGX Spark, DeepStream, and related guides now install the base ultralytics package instead of the larger [export] extra.
  • Export dependencies are installed automatically when an export is requested, reducing installation size and dependency conflicts.

• Improved Weights & Biases artifact control — PR #25985 by @glenn-jocher

  • W&B model artifact uploads now follow the existing training save argument.
  • save=False skips uploading the best checkpoint while retaining metrics and plots.
  • Default behavior remains unchanged with save=True.

• Package update

  • Version bumped to 8.4.133.

🎯 Purpose & Impact

• Better tuning results: Hyperparameter searches are more likely to preserve successful configurations, explore meaningful alternatives, and avoid wasting trials on duplicates. 🎯
• Faster inference: Device-side preprocessing can reduce latency, particularly for batched inference and CPU-bound pipelines.
• Broader performance optimization: Supported x86 CPU users may benefit from channels-last inference without changing their existing commands.
• More reliable model export: INT8 calibration now honors requested dataset fractions, improving calibration speed and reducing unexpected resource usage.
• Richer evaluation: Custom detection datasets can now receive object-size performance breakdowns similar to COCO evaluations.
• Easier edge deployment: Base installations are smaller and less prone to dependency conflicts, while export workflows remain available when needed.
• More control over experiment storage: W&B users can keep experiment tracking lightweight by disabling checkpoint saving with the standard save setting.

What's Changed

• Allow skipping W&B model artifacts by @fcakyon in https://github.com/ultralytics/ultralytics/pull/25979
• Remove [export] from edge-device install guides by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/25977
• Accelerate predictor preprocessing on inference devices by @jahsef in https://github.com/ultralytics/ultralytics/pull/25982
• Fix INT8 export calibration fractions by @JESUSROYETH in https://github.com/ultralytics/ultralytics/pull/25978
• Use save argument for W&B model artifacts by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25985
• Compute size-specific mAP for custom detection datasets by @fcakyon in https://github.com/ultralytics/ultralytics/pull/25981
• Enable channels-last by default for x86 CPU inference by @JESUSROYETH in https://github.com/ultralytics/ultralytics/pull/25983
• Improve hyperparameter Tuner mutation convergence by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/25984

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.132...v8.4.133