ultralytics 8.4.20

v8.4.20 - `ultralytics 8.4.20` Remove redundant hardcoded `tuner_callbacks` (#23772)

on Python PyPI

🌟 Summary

Ultralytics v8.4.20 is a stability-and-usability release focused on cleaner hyperparameter tuning, more reliable deployment/export workflows, and improved docs for both YOLO models and the Ultralytics Platform 🚀

📊 Key Changes

• (Most important) Ray Tune cleanup in current PR #23772 🧹

  • Removed hardcoded tuner_callbacks and dropped built-in W&B callback wiring from RunConfig in tuning.
  • Version bumped from 8.4.19 → 8.4.20.

• RKNN export reliability improved (#23802) 📦

  • Added ONNX version guard (onnx<1.19.0) and enforced ONNX opset cap (<=19) for RKNN conversion.
  • Explicitly supports smoother export paths for YOLO26 models on Rockchip toolchains.

• FastSAM prompt accuracy fix (#23766) 🎯

  • CLIP prompting now masks non-target neighboring segments before scoring.
  • Reduces false positives in overlapping/contained regions (for example, object-inside-background cases).

• ByteTracker consistency update (#23771) 🛠️

  • Optional score fusion now also applies in second-stage association (when fuse_score=True), aligning behavior across tracking stages.

• Better YAML error messages (#23767) ✅

  • Failed YAML loads now return clearer syntax errors with file context and validation guidance, instead of ambiguous fallback failures.

• Jetson/JetPack 6 stack refresh (#23788 + #23801) 🤖

  • Updated Jetson Docker/runtime stack to newer Torch/Torchvision/ONNX Runtime GPU versions.
  • Compatibility checks now recognize torch 2.10 + torchvision 0.25.

• Docs and platform improvements 📚

  • New SAM vs YOLO segmentation benchmark section includes YOLO26n-seg comparisons (#23782).
  • Platform docs expanded for pose skeleton templates and dataset versioning workflows (#23787, #23796).
  • YOLOE tutorial embed updated (#23798).
  • CI/workflow dependency/auth updates (#23763, #23799).

🎯 Purpose & Impact

• More robust tuning workflows ⚙️

  • By removing tightly coupled callback logic, Ray Tune runs are less fragile in mixed environments (especially distributed tuning setups).

• Fewer deployment surprises 🚀

  • RKNN export safeguards reduce version mismatch errors and manual troubleshooting for edge deployment users.

• Higher output quality in segmentation/tracking 👀

  • FastSAM text prompts become more trustworthy in crowded scenes.
  • ByteTracker behavior is more consistent when confidence fusion is enabled.

• Smoother setup for modern environments 🔧

  • Jetson and PyTorch compatibility updates reduce install/runtime friction for developers upgrading dependencies.

• Better onboarding and reproducibility 🧠

  • Clearer YAML errors help users fix configs faster.
  • Platform dataset versioning and skeleton-template docs improve team workflows and repeatable training.

What's Changed

• Use github.Auth.Token in merge action by @glenn-jocher in #23763
• Better YAML error introspection by @glenn-jocher in #23767
• Fix for #23765 FastSAM CLIP prompting by @orcinus in #23766
• Apply to ByteTracker second association by @ahmet-f-gumustas in #23771
• Update sam-3.md speed comparison by @Laughing-q in #23782
• Platform Skeleton templates docs by @glenn-jocher in #23787
• Update JetPack 6 Docker image with torch 2.10 by @lakshanthad in #23788
• Platform dataset versioning docs by @glenn-jocher in #23796
• Bump docker/login-action from 3 to 4 in /.github/workflows by @dependabot[bot] in #23799
• Add https://youtu.be/JcZsqUc8PMM to docs by @RizwanMunawar in #23798
• Update torch and torchvision compatibility table to latest by @lakshanthad in #23801
• Fix RKNN exports to support YOLO26 models by @lakshanthad in #23802
• ultralytics 8.4.20 Remove redundant hardcoded tuner_callbacks by @Laughing-q in #23772

New Contributors

• @orcinus made their first contribution in #23766

Full Changelog: v8.4.19...v8.4.20