ultralytics 8.4.110

v8.4.110 - Expand RKNN export support to all tasks (#25490)

on Python PyPI

🌟 Summary

🚀 Version 8.4.110 expands RKNN export to every supported YOLO task and improves GPU-friendly tensor image plotting, making Rockchip deployment more versatile and visualization more efficient.

📊 Key Changes

• Expanded RKNN export support by @glenn-jocher:

  • RKNN models can now be exported for:
    • Object detection
    • Instance segmentation
    • Classification
    • Pose estimation
    • Oriented bounding boxes (OBB)
    • Semantic segmentation
    • Depth estimation
  • Export smoke tests now cover all current task models using rknn-toolkit2.
  • Documentation and export tables now show task-specific model names and artifacts.
  • Package version bumped to 8.4.110.

• More efficient tensor-based plotting:

  • Results.plot() and Annotator now accept contiguous HWC BGR torch.Tensor images.
  • Tensor images can remain on their original device during mask compositing, avoiding unnecessary CPU transfers.
  • Conversion to NumPy is delayed until required by OpenCV, PIL, display, saving, or the returned result.

• Documentation improvements:

  • Corrected “setup” to “set up” in the DeepSparse tutorial.
  • Updated plotting API documentation to include tensor image support.
  • Added task-specific export configuration to classification, depth, OBB, pose, segmentation, and semantic segmentation pages.

🎯 Purpose & Impact

• Broader Rockchip NPU deployment 📱: Users can deploy more types of YOLO26 models on RKNN-compatible Rockchip hardware, rather than being limited to detection models.
• Simpler export workflows 🛠️: Task-specific documentation and artifact names make it clearer which model to export and how to use the resulting RKNN file.
• Improved visualization performance ⚡: GPU-backed tensor images can avoid an unnecessary image copy to the host during mask plotting, which may improve performance for accelerated pipelines.
• Better reliability ✅: Expanded validation confirms RKNN conversion and basic inference coverage across all seven supported tasks.
• No major behavior changes for existing users: NumPy image workflows remain supported, while tensor-based workflows gain improved device efficiency.

What's Changed

• Keep tensor images on-device during mask plotting by @glenn-jocher in #25487
• Fix grammar in DeepSparse tutorial by @gizembm in #25488
• Expand RKNN export support to all tasks by @glenn-jocher in #25490

New Contributors

• @gizembm made their first contribution in #25488

Full Changelog: v8.4.109...v8.4.110