🌟 Summary

Ultralytics v8.3.54 delivers a significant overhaul in the Streamlit-based real-time inference solution, making it easier for users to perform live predictions with a better interface. It also introduces enhancements around exporting flexibility for OpenVINO models, updates to documentation for YOLOv11 use, and streamlines development and compatibility workflows.

📊 Key Changes

• 🚀 Revamped Streamlit Inference Tool: Streamlit apps now feature an all-new Inference class.
  • Sidebar for quick video source, model selection, and settings like confidence thresholds.
  • Support for webcam and video uploads for real-time predictions and visualizations.
  • Enhanced interactivity with class selection, live FPS monitoring, and tracking features.
• 📦 OpenVINO Export Enhancements:
  • Added support for dynamic shapes, expanding deployment flexibility.
  • Unified argument ordering (batch, dynamic, etc.) across multiple export formats.
• 📖 YOLOv11 Documentation Updates: Updated guides to reflect the latest YOLOv11 usage in region counting.
• 🐍 Python Workflow Updates: Minimum Python version for CI workflows updated to 3.9 for compatibility alignment.
• 🌐 ONNXRuntime Example for RTDETR:
  • Added an example for deploying RTDETR models with ONNXRuntime in Python.
• ⚙️ Dependency Updates: Updated GitHub Actions setup-uv workflow to v5 to improve caching and build processes.

🎯 Purpose & Impact

• Better User Experience with Streamlit:
  • Easier navigation and configuration for real-time inference tasks. 🖥️
  • Developers and beginners alike can now perform live inference with minimal setup.
• Deployment Flexibility: Support for dynamic OpenVINO exports ensures models work smoothly across various scenarios and hardware configurations. 🧩
• Clearer Documentation: The shift to YOLOv11 references builds clarity and trust for users working with region-based object counting. 📘
• Future-Proofing Development:
  • Updating Python versions ensures long-term ecosystem compatibility. 🔧
• ONNXRuntime Examples: Simplifies adopting RTDETR models for developers using ONNXRuntime in Python, with clear setup and usage guidance. 🚀
• Faster CI/CD Pipelines: Updated dependencies in GitHub workflows boost speed and efficiency. ⚡

This release is ideal for users looking for a blend of usability in inference workflows and robustness in model deployment workflows! 🌟

What's Changed

• Add dynamic to approved OpenVINO export args by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18353
• Bump astral-sh/setup-uv from 4 to 5 in /.github/workflows by @dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/18358
• Update YOLOv8 to YOLO11 in region-counting.md by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18360
• Min CI Python 3.9 from 3.8 by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18355
• [Example] RTDETR-ONNXRuntime-Python by @semihhdemirel in https://github.com/ultralytics/ultralytics/pull/18369
• ultralytics 8.3.54 New Streamlit inference Solution by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18316

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.53...v8.3.54