ultralytics 8.3.70

v8.3.70 - `ultralytics 8.3.70` add `data` argument to Sony IMX500 export (#18852)

on Python PyPI

🌟 Summary

The v8.3.70 release brings feature enhancements with improved export functionalities, updated compatibility for PyTorch, and usability enhancements in benchmarking and documentation. 🚀

📊 Key Changes

• Sony IMX500 Export Update: Added support for the data argument, enabling dataset configuration during export for better control over quantization in formats like OpenVINO, TensorRT, and TF Lite. 📁
• Torch 2.6 Compatibility: Updated Torch-Torchvision mappings to ensure seamless functionality with the latest PyTorch update. 🔧
• Format-Specific Benchmarking: Introduced benchmarking support for individual formats (e.g., ONNX) to allow targeted performance evaluations. 📊
• NVIDIA DLA Support: Implemented support for running models on specific NVIDIA DLA cores—a key feature for specialized hardware optimization. 🖥️
• Improved numpy Stability: Pinned numpy version to prevent compatibility issues with OpenVINO and TFLite during CI tests. ✅
• Documentation Enhancements: Added tutorial videos and refined sections for clarity, aiding users and contributors. 📚

🎯 Purpose & Impact

• Improved Export Workflows:

  • Purpose: The data argument helps users customize exports with specific dataset configurations, simplifying quantization and compatibility for edge and on-premise deployment.
  • Impact: Makes exports more robust and adaptable to diverse workflows, ensuring higher-quality models with optimized performance.

• Torch Compatibility:

  • Purpose: Keep the framework current with the latest PyTorch improvements.
  • Impact: Allows users to leverage PyTorch 2.6's advancements without compatibility hiccups, maintaining a seamless experience.

• More Granular Benchmarking:

  • Purpose: Enable granular analysis of models' efficiency in specific formats like ONNX or TensorFlow Lite.
  • Impact: Helps developers fine-tune models for scenarios where particular formats are essential for deployment.

• DLA Optimization:

  • Purpose: Ensure efficient inference on NVIDIA's specialized hardware.
  • Impact: Reduces computational overhead and maximizes performance for users running models on NVIDIA DLA platforms.

• CI Stability with numpy:

  • Purpose: Prevent runtime or testing errors due to incompatible numpy versions.
  • Impact: Ensures reliable and predictable performance for developers and CI pipelines.

• Accessible Documentation:

  • Purpose: Make it easier for new contributors and users to onboard through visual and detailed guides.
  • Impact: Encourages community growth and simplifies the learning curve for both model and framework regulars.

🎉 This release is packed with features to empower smoother workflows, improve hardware compatibility, and promote user-friendly innovation! 🌟

What's Changed

• Update torchvision compatibility table for torch 2.6 by @glenn-jocher in #18935
• Add https://youtu.be/yMR7BgwHQ3g to docs by @RizwanMunawar in #18936
• Add support for single export format benchmark by @RizwanMunawar in #18740
• Remove YOLOv10 benchmarks from CI by @glenn-jocher in #18937
• mkdocs-ultralytics-plugin>=0.1.16 by @glenn-jocher in #18942
• Pin numpy<=2.1.1 to resolve failing --slow CI by @lakshanthad in #18943
• Add DLA specific core usage by @AbelHaro in #18930
• Minor Docs edits by @LexBarou in #18940
• Eliminate numpy<2.0.0 pin for OpenVINO on macOS by @glenn-jocher in #18945
• ultralytics 8.3.70 add data argument to Sony IMX500 export by @lakshanthad in #18852

New Contributors

• @LexBarou made their first contribution in #18940
• @AbelHaro made their first contribution in #18930

Full Changelog: v8.3.69...v8.3.70