ultralytics 8.3.69

v8.3.69 - `ultralytics 8.3.69` New Results `to_sql()` method for SQL format (#18921)

on Python PyPI

🌟 Summary

The Ultralytics v8.3.69 release introduces enhanced integration for data export, including a new to_sql() method for saving model results directly into an SQL database. This version also continues refining the documentation, stability, and benchmarking experience to provide a smoother user workflow. 🚀

📊 Key Changes

• New SQL Export Capability: Users can now use the to_sql() method to store YOLO model inference results directly in an SQL database for organization and analysis. 🗄️
• Generalized Export Options: Expanded export methods for results, adding to_df, to_csv, to_xml, and to_json for improved compatibility with different formats.
• Improved Documentation:
  • Added dynamic performance visualization charts to model documentation for more engaging and intuitive comparisons. 📈
  • Simplified and clarified YOLOv3 documentation tables for better readability. 📚
• Benchmark Enhancements:
  • Strengthened validation for input sizes, ensuring square images are required for benchmarking. 🖼️
  • Modified logging to lessen verbosity and improve user-friendliness during prediction and validation tasks. 💡
• Fixes and Stability:
  • Corrected edge cases in AutoBatch with better RT-DETR compatibility. ✅
  • Implemented model deep copy for profiling tasks to ensure unmodified behavior during GFLOP measurements. 🔒
• CI Pipeline Adjustments:
  • Temporarily disabled Windows CI and Raspberry Pi CI workflows for maintenance, ensuring smoother ongoing operations. 🛠️

🎯 Purpose & Impact

• Purpose:
  • The to_sql() function provides seamless integration with relational databases, making it easier to organize, query, and analyze results within existing workflows.
  • Enhanced export flexibility supports various use cases and workflows, from technical development to high-level reporting.
  • Improvements in benchmarking and documentation provide clarity for researchers and developers determining model performance and deployment strategies.
• Impact:
  • For Developers: Effortlessly manage results with SQL integration, while enjoying a more streamlined benchmarking process.
  • For Researchers: Leverage clearer documentation and performance visualizations for easier evaluation of model trade-offs.
  • For General Users: Reduced complexity and improved tools make interacting with the platform more intuitive and accessible. 🌟

This release continues to strengthen both backend functionality and user experience, paving the way for effective use of YOLO and supporting tools across diverse projects! 🎉

What's Changed

• Fix YOLOv3 table by @glenn-jocher in #18902
• Add Docs models JS charts by @glenn-jocher in #18905
• Simplify build_docs.py by @glenn-jocher in #18910
• Fix AutoBatch when working with RT-DETR models by @Laughing-q in #18912
• Add PP-YOLOE+ params and flops data by @Laughing-q in #18911
• Temporarily disable Raspberry Pi CI due to maintenance by @lakshanthad in #18923
• Fix Docs edit button links by @glenn-jocher in #18932
• Add imgsz check and improve logs for benchmarks by @Y-T-G in #18917
• ultralytics 8.3.69 New Results to_sql() method for SQL format by @RizwanMunawar in #18921

Full Changelog: v8.3.68...v8.3.69