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Update dependency ultralytics to v8.3.54 #124

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This PR contains the following updates:

Package Change Age Adoption Passing Confidence
ultralytics (changelog) 8.3.49 -> 8.3.54 age adoption passing confidence

Release Notes

ultralytics/ultralytics (ultralytics)

v8.3.54: - ultralytics 8.3.54 New Streamlit inference Solution (#​18316)

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🌟 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 YOLO11 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.
  • 📖 YOLO11 Documentation Updates: Updated guides to reflect the latest YOLO11 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 YOLO11 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

Full Changelog: ultralytics/ultralytics@v8.3.53...v8.3.54

v8.3.53: - ultralytics 8.3.53 New Export argument validation (#​18185)

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🌟 Summary

The v8.3.53 release introduces enhanced argument validation during model export to improve error handling and reduce user confusion, alongside other updates focusing on Dockerfile improvements for NVIDIA Jetson devices and internal code enhancements. 🚀

📊 Key Changes

Primary Feature: Enhanced Export Argument Validation
  • ✅ Introduced a mechanism to check whether export arguments are valid for specific formats (e.g., ONNX, TensorRT).
  • 🚫 Previously unsupported or incompatible arguments (e.g., int8 without required calibration data) will now raise clear errors.
Other Updates:
  • 🔧 JetPack Dockerfile Enhancements
    • JetPack 5: Updated base image, streamlined dependencies, and improved TensorRT compatibility.
    • JetPack 6: Removed unnecessary ONNX Runtime GPU package references for cleaner setup.
  • 🛠️ Improved settings.update() Validation: Ensures proper handling of input types and keys for user settings.
  • 🧹 Code Cleanup: Improved internal structures such as string representations for configuration objects (JSONDict) and URL handling (clean_url), improving performance and readability.

🎯 Purpose & Impact

  • Export Validation Improvements

    • 🚀 Provides users with immediate feedback on invalid export configurations.
    • 💪 Reduces confusion by preventing potentially misleading silent failures during export.
    • 🛡️ Ensures more reliable model deployment by enforcing compatibility checks early.

  • Jetson Dockerfile Updates

    • 🖥️ Increased compatibility with updated JetPack versions for NVIDIA Jetson devices.
    • 🔨 Streamlined setup for AI model training and deployment with YOLO on Jetsons.

  • User-Friendly Enhancements

    • 💡 Easier troubleshooting with clearer error messages for user settings and export configurations.
    • 📜 Simpler and more maintainable project codebase with reduced clutter in utilities and configuration processing.

This release strongly benefits both developers configuring their models for export and users building YOLO models on NVIDIA platforms, ensuring smoother workflows and better system compatibility. 🚦

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.52...v8.3.53

v8.3.52: - ultralytics 8.3.52 AutoBatch CUDA computation improvements (#​18291)

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🌟 Summary

Version 8.3.52 focuses on enhanced CUDA memory management for improved performance, with additional updates to documentation, compatibility for NVIDIA Jetson devices, and refined functionality for YOLO models. 🚀

📊 Key Changes

  • 🚀 New cuda_memory_usage Utility: Introduced a tool for dynamic monitoring and management of CUDA memory during operations.
  • 💡 Improved Model Profiling: Integrated memory tracking into the profiling process to report GPU memory usage alongside performance stats.
  • 🔄 Enhanced Object Segmentation: Modified segment2box for precise bounding box calculations when segments extend beyond the image boundaries.
  • 📦 JetPack 6.1 Dockerfile Update: Added compatibility for NVIDIA Jetson Orin Nano Super Developer Kit with dependency upgrades and performance benchmarks.
  • 📖 Richer Documentation: Added a CIFAR-100 tutorial video, improved clarity on scale parameter for multiscale training, and updated ROS and NVIDIA Jetson guides.
  • 🧹 TFLite Example Cleanup: Removed unnecessary RGB-to-BGR conversions for simpler and clearer example usage.

🎯 Purpose & Impact

  • 🚀 Enhanced Performance: The cuda_memory_usage utility ensures more efficient GPU memory handling, reducing the risk of out-of-memory crashes during complex operations.
  • 📈 Model Optimization: Developers get richer profiling insights, aiding faster debugging and improving training/production readiness.
  • 🖼️ Robust Object Detection: Improved segmentation functionality provides accuracy even with challenging edge cases, making models more reliable.
  • 🤖 Wider Compatibility: Updating to JetPack 6.1 enables users to fully leverage NVIDIA Jetson’s latest hardware advancements (e.g., Orin Nano Super’s 67 TOPS).
  • 📚 Simplified Learning: Documentation improvements, including engaging tutorials and clarified parameters, lower the barrier to entry for both beginners and experts.
  • 🧑‍💻 Beginner-Friendly Examples: Streamlined TFLite examples ensure ease of adoption for new developers.

This release delivers meaningful improvements for developers working across GPU-heavy tasks, embedded systems, and edge AI deployments! 🚀

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.51...v8.3.52

v8.3.51: - ultralytics 8.3.51 AutoBach logspace fit and checks (#​18283)

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🌟 Summary

The Ultralytics v8.3.51 release introduces improved robustness for training batch size optimization, documentation enhancements, new features like a security alarm system, and updates to facilitate the transition from YOLOv8 to YOLO11. 🚀

📊 Key Changes

  • Improved Batch Size Calculation:
    • Automated batch size determination now uses logarithmic polynomial fitting for better accuracy. 🧮
    • Stricter checks ensure safe memory usage and prevent crashes due to misconfigurations. ✅
  • Hyperparameter Tuning:
    • Added default hyperparameter search spaces and clear examples in documentation for easier customization. 🛠️
    • Updated training process to improve reliability by using shell=True for subprocess execution. ⚙️
  • YOLO11 Integration:
    • Updated examples, references, and documentation to reflect the transition from YOLOv8 to YOLO11. 📚
    • Enhanced support for SAHI (Slicing Aided Hyper Inference) with YOLO11 models.
  • New Security Alarm System:
    • Added a ready-to-use, customizable security alarm system solution leveraging YOLO11. Includes email alerts when detections exceed thresholds. 🛡️
  • Expanded Export Options:
    • New formats supported, including MNN and Sony IMX500, enhancing deployment flexibility for diverse platforms. 🎉

🎯 Purpose & Impact

  • Optimized Performance:
    • The refined autobatch method improves training stability and GPU utilization across various devices, helping users achieve smoother workflows.
  • Enhanced Usability:
    • New documentation simplifies hyperparameter tuning for beginners and advanced users alike, reducing the learning curve.
    • Updates to SAHI and model examples make it easier to adopt YOLO11.
  • Greater Flexibility:
    • Broader export options and integration tools expand YOLO's adaptability for edge devices like IMX500.
  • Real-World Applications:
    • With the newly added Security Alarm System, users gain a powerful, practical monitoring tool ready for deployment in surveillance scenarios. 🚨

This release elevates Ultralytics by streamlining processes, expanding use cases, and improving reliability for developers and organizations. ⭐

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.50...v8.3.51

v8.3.50: - ultralytics 8.3.50 Enhanced segment resample (#​18171)

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🌟 Summary

Release v8.3.50 introduces improvements to segment resampling logic, enhanced model handling during training and validation, documentation updates, and bug fixes across multiple areas for increased flexibility, accuracy, and usability. 🚀📘

📊 Key Changes

  • Enhanced Segment Handling:
    • Segment resampling now dynamically adjusts the number of points based on the longest segment for better consistency. 🖌️
    • Empty segments during concatenation are gracefully handled to avoid errors.
  • Improved Validation & Model Workflow:
    • Validation callbacks for OBB models now work correctly during training. 🔄
    • Updates to fix validation warnings when using untrained model YAMLs.
  • Model Saving Updates:
    • Improved checkpoint handling when saving models to reduce initialization errors. 💾
  • Documentation Tweaks:
    • Added multimedia content (audio & video) to YOLO11 documentation for a richer learning experience. 🎧🎥
    • Cleaned up outdated entries (like the Sony IMX500) and enhanced clarity with new formatting and annotated argument types.
    • Internal docs configuration now supports cleaner URLs and auto-deployment enhancements. 🌐
  • Bug Fixes:
    • Fixed CUDA-related bugs in the SAM module for more consistent device handling. 🛠️
    • Adjustments to prevent crashes in scenarios with mixed device usage.

🎯 Purpose & Impact

  • ✅ Reliability Boost: The improved resampling logic ensures stable training and avoids breaking workflows when handling variable-length segments.
  • 📈 Performance Optimization: Better checkpoint and validation handling streamlines user workflows and minimizes potential runtime errors.
  • 🌍 Usability Improvements: Updated Docs and multimedia resources make discovering and using features more user-friendly for both beginners and experts.
  • 🚀 Cross-Device Consistency: Fixes in CUDA logic ensure model compatibility on both CPU and GPU systems, enhancing accessibility.
  • 🖹 Clean Documentation: Removing outdated content and refining resources helps users focus on the latest tools and avoid confusion.

This update is pivotal for developers and users working with segmentation models, large datasets, or seeking smoother workflows during benchmarking, training, and inference with YOLO models.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.49...v8.3.50

Configuration

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This PR was generated by Mend Renovate. View the repository job log.

@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 8e7c625 to 60ee997 Compare December 17, 2024 21:08
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.50 Update dependency ultralytics to v8.3.51 Dec 17, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 60ee997 to 874b64a Compare December 20, 2024 13:05
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.51 Update dependency ultralytics to v8.3.52 Dec 20, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 874b64a to 08674f5 Compare December 22, 2024 03:17
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.52 Update dependency ultralytics to v8.3.53 Dec 22, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 08674f5 to ba0bcb8 Compare December 24, 2024 14:09
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.53 Update dependency ultralytics to v8.3.54 Dec 24, 2024
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