ultralytics 8.4.119

v8.4.119 - Enable OpenVINO NPU_TURBO for classification models on supported NPUs (#25784)

on Python PyPI

🌟 Summary

v8.4.119 improves Intel NPU classification performance with OpenVINO, strengthens detection and tracking reliability, expands Platform integration, and refreshes documentation and developer tooling. 🚀

📊 Key Changes

• ⚡ OpenVINO NPU_TURBO for classification

  • Enables NPU_TURBO automatically for classification models running on supported Intel NPU devices.
  • Applies only when the device advertises support, preserving existing behavior for other tasks, devices, and drivers.
  • Measured classification latency improvements of approximately 42–52% on an Intel Core Ultra 9 185H, with bit-identical accuracy across tested configurations.
  • Turbo mode is intentionally limited to classification because larger image sizes showed little benefit and could consume unnecessary power.

• 🔌 Platform SDK exposed from ultralytics

  • Python 3.11+ users can access Platform, AsyncPlatform, APIError, and APIConnectionError directly from the main package.
  • Imports are lazy, so the SDK is loaded only when needed.
  • Python 3.8–3.10 users receive a clear compatibility message when requesting Platform exports.

• 🛰️ More efficient Platform training callbacks

  • Subsequent metrics, telemetry, and model uploads now reuse the registered model ID instead of repeatedly resolving project and model names.
  • Reduces internal Platform traffic and improves reliability during long training runs.

• 🛡️ Improved tracking robustness

  • BYTETracker now ignores detections with zero or negative width or height before creating tracks, preventing invalid Kalman filter states.
  • Kalman filter operations were simplified to use direct slicing instead of unnecessary matrix operations.
  • Removed redundant array copies in multi-object tracking paths.

• 🎯 Safer detection export postprocessing

  • Detection top-k selection is now limited to the number of available anchors.
  • Prevents export failures on very small input sizes while preserving normal behavior for standard inputs.

• 🧩 Corrected CopyPaste augmentation probability

  • The CopyPaste transform now respects its configured probability in flip mode.
  • Previously, eligible images could receive the augmentation unconditionally.

• 📚 Documentation and usability updates

  • Updated supported-task banners to cover all seven supported tasks, including semantic segmentation and depth estimation.
  • Added the Cmd/Ctrl+Delete image-deletion shortcut to the Platform annotation guide.
  • Added a YOLO26 LiteRT export and mobile deployment tutorial video.
  • Restored light, dark, and system theme controls in the documentation.
  • Removed documentation embeds that did not render correctly on the live site.
  • Improved ASCII string checks using Python’s built-in string operation for faster annotation rendering.

• 🧰 CI maintenance

  • Updated the self-hosted runner cleanup action used across several CI jobs.

🎯 Purpose & Impact

• 🚀 Faster edge inference: Supported Intel NPU users running classification models should see substantially lower latency without sacrificing accuracy.
• 🔋 Better power awareness: Restricting turbo mode to classification avoids enabling a potentially costly optimization where it provides little practical benefit.
• 🤝 Simpler Platform development: Python 3.11+ applications can use Platform SDK functionality through familiar top-level ultralytics imports.
• 📈 More reliable training runs: Model ID reuse reduces repeated lookup work and supports smoother metric streaming and checkpoint uploads.
• 🛡️ Fewer runtime and export failures: Invalid tracking boxes and small-input detection exports are handled safely instead of producing corrupted states or indexing errors.
• 🎛️ More predictable augmentation: CopyPaste now behaves according to its documented probability setting.
• 🌐 Improved user experience: Refreshed documentation, working theme controls, clearer task coverage, and better Platform guidance make the ecosystem easier to use for both new and experienced users.

What's Changed

• Update supported tasks banner by @raimbekovm in #25788
• Document Platform image delete shortcut by @glenn-jocher in #25791
• Use model IDs for Platform training callbacks by @glenn-jocher in #25793
• Bump eviden-actions/clean-self-hosted-runner from v1.4.36 to v1.4.37 in /.github/workflows by @UltralyticsAssistant in #25797
• Add https://youtu.be/FmgrfmZlhpY to docs by @RizwanMunawar in #25795
• Expose Platform SDK from ultralytics by @glenn-jocher in #25800
• Restore dark theme palette in docs configuration by @onuralpszr in #25802
• Replace the per-character is_ascii loop with str.isascii() by @raimbekovm in #25801
• Remove the two docs embeds that do not render on the live site by @raimbekovm in #25792
• fix: add probability guard to CopyPaste in flip mode by @Rahulbiradar9 in #25796
• Clamp detection top-k to available anchors by @Dorablank in #25771
• Replace selection-matrix matmuls with direct slicing in the Kalman filter by @JESUSROYETH in #25770
• Reject zero/negative-height detections before track creation in BYTETracker by @JESUSROYETH in #25769
• Enable OpenVINO NPU_TURBO for classification models on supported NPUs by @synml in #25784

New Contributors

• @Dorablank made their first contribution in #25771

Full Changelog: v8.4.118...v8.4.119