ultralytics 8.4.65

v8.4.65 - QNN channel-last + semantic ArgMax performance improvements (#24790)

on Python PyPI

🌟 Summary

v8.4.65 is mainly a mobile inference performance release 📱⚡, with the biggest upgrade improving QNN exports for Snapdragon NPUs and speeding up semantic segmentation outputs by moving costly work inside the model graph.

📊 Key Changes

• 🚀 Major QNN export performance upgrade in PR #24790 by @glenn-jocher

  • QNN exports now use channel-last (NHWC) input, which better matches Qualcomm Hexagon NPU hardware and camera-native image layouts.
  • This avoids extra data reordering at inference time, reducing overhead on both the app side and the NPU side.
  • Implemented through new export wrappers like QNNModel instead of fragile post-export graph editing.

• 🧠 Semantic segmentation exports are much faster on-device

  • For QNN and CoreML semantic models, export now embeds ArgMax / class-map generation directly in the graph.
  • Instead of returning large floating-point logits that apps must decode afterward, exported models can now return a compact per-pixel class map directly.
  • New ClassMapModel wrapper handles this behavior during export.

• 📱 Better mobile runtime compatibility

  • Semantic predict/validation code was updated so Ultralytics can correctly handle both:
    • traditional logits outputs
    • new exported class-map outputs
  • This makes the faster export behavior work more cleanly across deployment and evaluation workflows.

• 📉 Measured impact highlighted in docs

  • QNN semantic performance became much more stable, replacing previously erratic decoding times with far more predictable behavior.
  • CoreML semantic exports also saw notable end-to-end speedups from in-graph class-map generation.

• 🛠️ RKNN export improvements

  • RKNN export now properly supports FP16 intermediate ONNX export.
  • Added RKNN INT8 export test coverage to improve reliability.
  • RKNN export docs/tables were updated accordingly.

• 🐳 Docker build reliability improvements

  • Dockerfiles now use more cache-conscious install patterns and extra cleanup steps to reduce disk pressure during builds.
  • The Docker workflow was simplified by removing an extra assistant-trigger step.

• 📚 Docs and export guidance improvements

  • Expanded and refreshed docs for CoreML and QNN, including clearer deployment guidance and performance notes.
  • Export docs now better explain YOLO26 end-to-end detection output formats.
  • Tracking docs now clarify support for OBB models as well.
  • Rust inference docs were updated to ultralytics-inference version 0.0.19.

• ✅ CI and workflow fixes

  • Docs redeploys now trigger more reliably when documentation changes land on main.
  • CUDA training tests are now explicitly skipped on Jetson devices, making CI behavior cleaner and easier to maintain.

🎯 Purpose & Impact

• ⚡ Faster mobile inference on Snapdragon devices
  Users deploying YOLO on Qualcomm hardware should see lower overhead and better real-world efficiency, especially in camera-based apps where image buffers are already channel-last.

• 🧩 Less app-side postprocessing work
  By returning semantic class maps directly from exported models, apps no longer need to spend as much CPU time decoding huge segmentation outputs.

• 📈 Better and more stable semantic segmentation performance
  This is especially important for real-time mobile experiences, where unpredictable postprocessing delays can cause lag or jitter.

• 🔒 More robust export pipeline
  Replacing manual ONNX graph surgery with clean wrapper modules makes exports easier to maintain and less error-prone over time.

• 📱🍎 Benefits extend beyond QNN
  CoreML semantic exports also gain from the same in-graph class-map idea, so Apple-device deployment gets a speed boost too.

• 🛠️ Improved reliability for deployment workflows
  Better RKNN testing, cleaner Docker builds, and more dependable docs publishing all reduce friction for developers working across edge and production environments.

• 👥 Broad user impact

  • Mobile and edge developers get the biggest benefit from this release.
  • Semantic segmentation users should see the clearest speed and usability gains.
  • General users also benefit from clearer docs, cleaner CI, and more reliable export behavior.

In short: v8.4.65 is a strong deployment-focused release 🎉, with the standout improvement being faster, more hardware-friendly QNN exports and smarter semantic segmentation outputs for mobile AI.

What's Changed

• Improve skipping training on Jetson Test CIs by @lakshanthad in #24557
• Trigger docs redeploys on changes by @glenn-jocher in #24784
• Replace Enterprise '50+ members' with custom team size in Platform docs by @mykolaxboiko in #24785
• Normalize YAML docs links by @glenn-jocher in #24788
• Fix docs deploy change detection by @glenn-jocher in #24787
• Improve docs, comments, and correctness by @glenn-jocher in #24789
• Enable RKNN FP16 export and add RKNN INT8 test by @Laughing-q in #24762
• Remove Docker workflow assistant trigger by @glenn-jocher in #24783
• docs: ultralytics-inference version to 0.0.19 in rust docs by @onuralpszr in #24793
• QNN channel-last + semantic ArgMax performance improvements by @glenn-jocher in #24790

Full Changelog: v8.4.64...v8.4.65