ultralytics 8.2.42

v8.2.42 - `ultralytics 8.2.42` NVIDIA TensorRT 10 default (#13943)

on Python PyPI

🌟 Summary

Release v8.2.42 of Ultralytics brings several critical updates to enhance the performance, security, and flexibility of the YOLO models, including Dockerfile improvements, support for OpenVINO C++ inference, and improved package dependencies.

📊 Key Changes

• Dockerfile Updates:
  • PyTorch CUDA version updated from 2.2.2 to 2.3.1.
  • Package installation improvements, switching nvidia-tensorrt to tensorrt.
  • Added unzip package for better compatibility.
• YOLOv10 Documentation:
  • Enhanced examples with Python and CLI commands for better usability.
• OpenVINO Integration:
  • Added support for YOLOv8 inference in C++ using OpenVINO and OpenCV APIs, complete with build instructions and usage examples.
• Dependency Management:
  • Updated dependency checks and installations to ensure compatibility with the latest packages.
  • Improved handling of TensorRT and other dependencies in different environments.
• Bug Fixes & Enhancements:
  • Fixes for the loss function computation to handle bounding boxes correctly.

🎯 Purpose & Impact

• Improved Performance:
  • Upgrading to PyTorch 2.3.1 with updated packages will leverage the latest performance and security enhancements.
• Better Compatibility:
  • Adding the unzip package and refining package installations improves overall compatibility and setup processes on various systems.
• Developer Flexibility:
  • The addition of the YOLOv8 OpenVINO C++ examples allows developers to integrate and leverage powerful YOLO models in their C++ projects, offering more flexibility and performance tuning options.
• Enhanced Usability:
  • Detailed documentation for running YOLOv10 models via Python and CLI facilitates ease of use for both new and experienced users.
• Security and Stability:
  • Ensuring that all packages are up-to-date reduces the risk of vulnerabilities and promotes more stable deployments.

This release is a significant step towards making the YOLO models more robust, easier to use, and performant in various environments. 🚀

What's Changed

• Add YOLOv8 OpenVINO C++ Inference example by @rlggyp in #13839
• Ultralytics TensorRT10 update by @glenn-jocher in #13933
• Dockerfile FROM pytorch/pytorch:2.3.1-cuda12.1-cudnn8-runtime by @glenn-jocher in #13937
• Add CLI commands for predict and train YOLOv10 models. by @RizwanMunawar in #13940
• ultralytics 8.2.42 NVIDIA TensorRT 10 default by @glenn-jocher in #13943

New Contributors

• @rlggyp made their first contribution in #13839

Full Changelog: v8.2.41...v8.2.42