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Update dependency ultralytics to v8.3.89 #124

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This PR contains the following updates:

Package Change Age Adoption Passing Confidence
ultralytics (changelog) 8.3.49 -> 8.3.89 age adoption passing confidence

Release Notes

ultralytics/ultralytics (ultralytics)

v8.3.89: - ultralytics 8.3.89 TensorFlow 2.19.0 compatibility updates (#​19668)

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🌟 Summary

The Ultralytics 8.3.89 release focuses on improving dependency management, enhancing compatibility with NVIDIA Jetson devices, and refining documentation for better usability. 🚀

📊 Key Changes

  • Dependency Management: Updated --index-strategy to unsafe-best-match for safer and more reliable package installations.
  • Jetson Compatibility: Adjusted TensorFlow.js versions for Jetson JetPack 4/5 to ensure seamless compatibility.
  • Documentation Updates: Standardized code examples across the repository to Python's interactive shell style (>>>) for improved clarity and consistency.
  • Stale Workflow Improvements: Enhanced GitHub workflows to better manage stale pull requests and issues.
  • Version Update: Incremented the Ultralytics version to 8.3.89.

🎯 Purpose & Impact

  • Enhanced Reliability: The updated dependency strategy minimizes potential conflicts during package installations, ensuring smoother setups. 🛠️
  • Broader Hardware Support: Improved TensorFlow.js compatibility ensures better performance on NVIDIA Jetson devices, benefiting edge AI applications. 🤖
  • Developer Experience: Standardized documentation makes it easier for developers to understand and implement examples, boosting productivity. 📚
  • Efficient Project Management: Improved stale workflows help maintain a cleaner and more organized repository. 🚀
  • Version Alignment: The version bump reflects these enhancements, signaling progress and stability.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.88...v8.3.89

v8.3.88: - ultralytics 8.3.88 Solutions refactor and improvements (#​18491)

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🌟 Summary

The v8.3.88 release of Ultralytics introduces significant enhancements and new features across its solutions, focusing on object detection, segmentation, privacy tools, and advanced analytics. These updates aim to provide more robust, versatile, and user-friendly tools for computer vision tasks.

📊 Key Changes

  • New Solutions Added:
    • ObjectBlurrer: Blurs detected objects for privacy protection.
    • ObjectCropper: Crops and saves detected objects for further analysis.
    • InstanceSegmentation: Enables segmentation with masks and annotations.
    • VisionEye: Simulates human observation by mapping objects to a vision anchor point.
  • Enhanced Analytics:
    • Improved support for various chart types (line, pie, bar, area) with better visualization and customization.
    • Unified structure for analytics results.
  • Refinements in Object Tracking:
    • Improved handling of bounding boxes and tracking across frames.
    • Enhanced region-based counting and queue management.
  • Bug Fixes:
    • Addressed bounding box offset issues in YOLOv8 C++ inference, ensuring accurate object detection and scaling.

🎯 Purpose & Impact

  • 🛡️ Privacy Protection: The ObjectBlurrer ensures sensitive information in images or videos is obscured, making it ideal for security and compliance use cases.
  • ✂️ Data Preparation: ObjectCropper simplifies dataset creation by extracting and saving detected objects.
  • 🎨 Advanced Visualization: InstanceSegmentation and VisionEye provide detailed insights into object relationships and spatial mapping, enhancing analytical capabilities.
  • 📈 Improved Analytics: The updated analytics tools allow users to derive actionable insights from data with visually appealing and informative charts.
  • 🚀 Enhanced Accuracy: Fixes in bounding box handling improve detection precision, benefiting applications like autonomous systems and surveillance.

These updates make Ultralytics solutions more powerful and adaptable, catering to a wide range of industries and use cases.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.87...v8.3.88

v8.3.87: - ultralytics 8.3.87 New Results.to_html method for inference outputs (#​19161)

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🌟 Summary

The v8.3.87 release introduces a new Results.to_html() method for exporting inference results in HTML format, alongside various usability, compatibility, and documentation improvements.

📊 Key Changes

  • HTML Export for Results: Added to_html() method to convert detection results into a web-friendly HTML format.
  • Documentation Enhancements: Improved clarity and consistency in method descriptions and added a dedicated page for the COCO128 dataset.
  • Compatibility Updates:
    • Added support for Ubuntu ARM CI runners and removed QEMU emulator for ARM64 Docker builds.
    • Restricted OpenVINO dependency to avoid version conflicts.
  • Memory Management: Optimized GPU memory clearing to trigger only when usage exceeds 90%.
  • Bug Fixes:
    • Resolved bounding box out-of-bounds issues in MNN examples.
    • Fixed multi-stream video result saving to avoid overwriting files.
  • Classification Model Updates: Adjusted FLOPs calculations for classification models to align with the default image size of 224 pixels.
  • Comet Integration: Added support for logging segmentation annotations in Comet.

🎯 Purpose & Impact

  • Improved Usability:
    • The to_html() method simplifies sharing and visualizing inference results in a browser-friendly format.
    • Enhanced documentation, including COCO128 details, aids developers in testing and debugging.
  • Broader Compatibility:
    • ARM support ensures better performance and inclusivity for ARM-based systems.
    • OpenVINO version constraints prevent compatibility issues.
  • Optimized Performance:
    • Smarter GPU memory management avoids unnecessary slowdowns during training.
    • Faster ARM64 Docker builds reduce build times from 8 minutes to 2 minutes.
  • Enhanced Functionality:
    • Segmentation support in Comet improves tracking and visualization for segmentation tasks.
    • Bug fixes ensure more reliable and accurate results across various use cases.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.86...v8.3.87

v8.3.86: - ultralytics 8.3.86 Refactor dataset YAML autodownload scripts (#​19579)

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🌟 Summary

The v8.3.86 release focuses on improving dataset handling, enhancing code consistency, and fixing minor issues to streamline workflows for users working with various datasets and models.

📊 Key Changes

  • Dataset YAML Refactor: Improved dataset configuration files for better readability and functionality.
    • Enhanced autodownload and conversion scripts for datasets like COCO, VOC, ImageNet, and more.
    • Unified string formatting to double quotes (") across YAML files.
    • Added detailed comments and docstrings for clarity.
  • UTF-8 Encoding Compliance: Updated all file operations to explicitly use UTF-8 encoding for better cross-platform compatibility.
  • Keypoint Loss Fix: Adjusted keypoint loss calculations to ensure only relevant dimensions are divided by stride tensors.
  • Documentation Updates:
    • Fixed example code in SAM 2 model documentation.
    • Embedded updated YouTube tutorials for YOLO11 training and batch inference.
  • Code Cleanup: Removed redundant imports and improved path handling using modern Python practices.

🎯 Purpose & Impact

  • Enhanced Dataset Handling:
    • Simplifies dataset preparation with improved autodownload and conversion scripts. 🛠️
    • Reduces errors with better file handling and clear documentation. 📚
  • Cross-Platform Compatibility:
    • Ensures consistent behavior across operating systems by enforcing UTF-8 encoding. 🌍
  • Improved Model Accuracy:
    • Fixes in keypoint loss calculations enhance precision in keypoint-based tasks. 🎯
  • User Experience:
    • Updated tutorials and documentation make it easier for users to train and deploy models effectively. 🎥
  • Code Maintainability:
    • Cleaner and more consistent codebase improves readability and reduces potential bugs. 🧹

This release is a quality-of-life update that enhances both backend functionality and user-facing resources, making it easier for developers and researchers to work with Ultralytics tools. 🚀

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.85...v8.3.86

v8.3.85: - ultralytics 8.3.85 TensoRT export max_shape fix (#​19541)

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🌟 Summary

This release, v8.3.85, focuses on improving TensorRT export functionality and refining ONNX segmentation examples for better performance and usability. 🚀

📊 Key Changes

TensorRT Updates (Main Focus of Release):
  • Fixed max_shape Calculation Bug: Resolved inconsistent calculations during TensorRT export with non-zero workspace values.
  • Improved Default Behavior: Ensured workspace settings default to 0 when not specified.
ONNX Segmentation Example Enhancements:
  • Streamlined Preprocessing/Postprocessing: Simplified the example to use a more efficient and flexible implementation, including key parameters like iou, imgsz, and conf.
  • Unified Confidence Threshold: Updated ONNX example to align with YOLO conventions for clarity (conf, iou).
  • Optimized Mask Handling: Enhanced segmentation mask functionality for better accuracy and resource efficiency.
  • Adaptive Backend Setup: Integrated GPU support automatically when available, improving ease of use.

🎯 Purpose & Impact

  • For TensorRT Users:

    • Purpose: Addressed critical bugs in TensorRT export needed for high-performance inference engines.
    • Impact: Accurate shape calculation ensures smoother deployments of dynamic models with TensorRT, preventing crashes or incorrect behavior. Users exporting YOLO models into .engine format, especially with non-zero workspaces, will experience stable exports.

  • For ONNX Developers:

    • Purpose: Simplify, optimize, and enhance usability of segmentation examples using ONNX Runtime.
    • Impact: Developers using ONNX Runtime benefit from faster setup, more intuitive parameter usage, and reliable mask-based object segmentation results.

  • General Improvements:

    • These updates improve the overall developer experience and model deployment across various inference backends, enabling seamless workflows for cutting-edge scenarios.

This update primarily strengthens export and inference capabilities 🎯, while making the process more robust for advanced and typical users alike.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.84...v8.3.85

v8.3.84: - ultralytics 8.3.84 Remove predictions with no masks (#​19537)

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🌟 Summary

The v8.3.84 release brings improvements to YOLO's segmentation handling, documentation clarity, and usability, with a focus on filtering invalid outputs and refining user guidance. 🎉

📊 Key Changes
  • 🚀 Segmentation Optimization: YOLO now filters out predictions with empty masks, resulting in more accurate and valuable outputs.
  • 📚 Updated Documentation Features:
    • Added a correct YouTube link for SAHI Tiled Inference for better instructional resources. 🎥
    • Improved code examples for consistent and clear understanding of critical tools like Colors class and merge_equals_args. ✨
  • ⚙️ Validation Enhancements: Restricted save_hybrid mode to only detection tasks, preventing incorrect usage and evaluation inaccuracies.
🎯 Purpose & Impact
  • 🧹 Cleaner Segmentations: By removing predictions lacking usable masks, segmentation results are more reliable and efficient. This streamlines workflows and avoids irrelevant data.
  • ✅ Easier Learning & Usage: Updated documentation improves the user experience through better resource links and clearer code examples, easing the learning curve for developers and users.
  • ⚠️ Prevents Missteps: Limiting save_hybrid ensures omission of scenarios that could lead to misinterpretation of validation outputs, particularly for non-detection models.

This update is all about boosting the quality and usability of YOLO tools, paving the way for more productive and error-free model usage! 🏆

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.83...v8.3.84

v8.3.83: - ultralytics 8.3.83 Revert saturation and value augmentation to relative shift (#​19515)

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🌟 Summary

Ultralytics release v8.3.83 focuses on refining image augmentation to make color transformations more natural while clarifying validation parameter requirements in the documentation. 🎉

📊 Key Changes

  • Image Augmentation Adjustments:

    • Reverted hue, saturation, and value (HSV) augmentation logic back to a relative shift approach for more natural-looking image transformations.
    • Fixed an issue with hue adjustments to align with the original implementation for improved consistency.
    • Ensured constraints, like maintaining pure white to avoid unnatural color changes, remain enforced. 🎨

  • Documentation Improvement:

    • Clarified the batch parameter for validation, specifying that it must be a positive integer to avoid confusion about unsupported functionality like AutoBatch in validation. 📚
🎯 Purpose & Impact
  • For Developers and Users:
    • 🎨 Enhanced Visual Realism: The relative shift method restores the natural look of augmented images, which is important for data augmentation and model training. This could improve model performance on tasks requiring realistic visual data.
    • 🛠 Accurate Transformations: Bug fixes ensure reliable and consistent adjustments during augmentation, preventing potential training issues.
    • 📚 Eased Configuration: Improved documentation reduces ambiguity in setting validation parameters, simplifying usage for both new and experienced users.

This release is a step forward in delivering both technical precision and user efficiency! 🚀

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.82...v8.3.83

v8.3.82: - ultralytics 8.3.82 ONNX dynamic and half export (#​19464)

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🌟 Summary

Ultralytics v8.3.82 enhances ONNX model export functionality, improves preprocessing for ONNXRuntime examples, and streamlines compatibility across various hardware setups. 🚀

📊 Key Changes

  • ONNX FP16 Export Fix: Introduced a workaround (arange_patch) for exporting ONNX models with both dynamic and half options, avoiding incompatibilities in the PyTorch torch.arange function.
  • Preprocessing Accuracy Boost: Improved image handling (aspect ratio, resizing, and padding) for ONNXRuntime examples to ensure more precise object detection.
  • Broader MNN Test Coverage: Enabled MNN export testing on Raspberry Pi to ensure compatibility.
  • Dataset Configuration Streamlining: Refined open-images-v7.yaml to centralize dataset directory management for clarity and maintainability.

🎯 Purpose & Impact

  • ONNX Export Enhancements:

    • Purpose: Solve PyTorch incompatibility issues during ONNX model export with FP16 precision in dynamic mode.
    • Impact: Developers can now seamlessly export high-performance ONNX models while avoiding device-specific restrictions, improving workflows on GPUs.

  • Accurate Preprocessing:

    • Purpose: Fix preprocessing bugs in ONNXRuntime inference to ensure image resizing, aspect ratio, and padding align with expected outputs.
    • Impact: Results in more consistent and reliable object detection performance between PyTorch and ONNX models.

  • Compatibility Extension:

    • Purpose: Enable MNN export tests on Raspberry Pi to ensure cross-platform functionality.
    • Impact: Broader hardware compatibility for MNN format users, improving reliability on low-cost systems like Raspberry Pi.

  • Dataset Management Improvements:

    • Purpose: Streamline configuration for the Open Images V7 dataset.
    • Impact: Easier and more consistent dataset setup, particularly useful for developers managing large datasets.

This update improves model export workflows, ensures consistent inference results across platforms, and expands compatibility for developers using diverse environments. 🌐

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.81...v8.3.82

v8.3.81: - ultralytics 8.3.81 Fix Metrics on_plot circular references (#​19318)

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🌟 Summary

The Ultralytics v8.3.81 release resolves a critical memory management issue in model validation while introducing key updates to documentation, testing workflows, and system reporting for enhanced usability and efficiency. 🚀✨

📊 Key Changes

  • 🧹 Memory Leak Fix in Validation Modules:
    Resolved circular references in metrics (on_plot) across validators (DetectionValidator, PoseValidator, etc.) to avoid CPU memory leaks during repeated evaluations.
  • 📚 Documentation Enhancements:
    Added examples for annotators in the library, and fixed/corrected metadata instructions for Triton guides and links in SAM 2 documentation.
  • 🐧 Raspberry Pi CI Improvements:
    Re-enabled Raspberry Pi CI workflow with improved benchmarks and added cleanup for self-hosted CI runners.
  • 🔍 Installation Diagnostics Update:
    Added project root installation path in the system environment summary for easier debugging.
  • 📊 Better Table Sorting in Docs:
    Improved table sorting functionality for file sizes, numbers, and dot-separated values.

🎯 Purpose & Impact

  • Memory Management Improvement:
    Eliminates residual memory accumulation when evaluating models repeatedly in scripts, preventing CPU overloading and Out-Of-Memory (OOM) errors. This fix improves the efficiency and reliability of long-running evaluation pipelines. 🧠🔥
  • User-Friendly Documentation:
    New usage examples and enhanced instructions make it easier for developers to understand and implement key features. Correcting SAM 2 links ensures access to the right research resources. 📚💡
  • Expanded Testing Coverage:
    Re-enabling Raspberry Pi CI and adding benchmarks enhances testing on diverse hardware, ensuring broader reliability across platforms. Improved CI cleanup reduces risks of resource conflicts. 🌐🔧
  • Debugging Simplification:
    Including installation paths in diagnostics facilitates resolving Python environment-related issues for developers. 🔍🛠️
  • Enhanced Sorting for Visual Data:
    Improved table sorting in docs boosts usability, especially when navigating large datasets or results with complex numbering or file size values. 📈✨

Overall, this update strengthens performance, stability, and developer experience, while prioritizing clarity and usability for the broader community. 🎉

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.80...v8.3.81

v8.3.80: - ultralytics 8.3.80 Fix YOLO-NAS export (#​19426)

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🌟 Summary (single-line synopsis)

Version 8.3.80 features crucial upgrades to YOLO-NAS handling, improved configuration merging for smoother exports, enhanced documentation interactivity, and compatibility refinements for key frameworks. 🚀

📊 Key Changes (bullet points highlighting any major changes)

  • 🔄 Model Export Enhancements: Fixed YOLO-NAS export by integrating default configs (DEFAULT_CFG_DICT) into model attributes for higher flexibility.
  • 🧠 RBOX Regularization: Refined bounding box angle calculations for consistency in the OpenCV definition.
  • 📋 Documentation Upgrades: Introduced sortable tables for performance data to improve clarity and usability across pages.
  • 🔧 Framework Compatibility: Constrained OpenVINO dependency to version >=2024.0.0,<2025.0.0 and updated outdated function calls for compatibility.
  • 🐳 Docker Workflow Fix: Removed deprecated numpy dependency to resolve CI errors and streamlined build conditions.

🎯 Purpose & Impact (bullet points explaining any benefits and potential impact to users)

  • 🌟 Improved Export Reliability: The configuration merging for YOLO-NAS ensures a more robust process, minimizing errors during model deployment and export.
  • 🧮 Better Prediction Accuracy: Regularizing rboxes improves detections by addressing angle inconsistencies in bounding boxes.
  • 🖱️ Enhanced User Experience: Sortable tables in documentation allow users to interactively organize performance metrics, making comparisons easier.
  • ✅ Smoother OpenVINO Exports: Version constraints and function updates enhance stability and prepare the platform for future releases without breaking changes.
  • 🔄 Cleaner CI Pipelines: Removing deprecated dependencies ensures more reliable builds, benefitting developers contributing to the codebase.

This update underscores Ultralytics' focus on improving usability, compatibility, and export-related functionalities for smoother workflows and precise model performance. 🚀

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.79...v8.3.80

v8.3.79: - ultralytics 8.3.79 Fix shift in HSV augmentation (#​19311)

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🌟 Summary

The v8.3.79 release of the Ultralytics YOLO framework introduces crucial bug fixes, enhancements to performance, and documentation updates. The primary focus is on correcting HSV augmentation mechanics and refining various code components for better reliability and usability. 🛠️✨

📊 Key Changes

  • HSV Augmentation Fix: Resolved incorrect hue, saturation, and value shifts during data augmentation, ensuring consistent color transformations. 🎨
  • YOLO12 Benchmark Refinement: Fixed performance metrics for YOLO12 models in documentation, updating speed and mAP comparisons ensuring accurate representations. 📈
  • Docker Streamlining: Removed redundant tensorrt-cu12 dependency and added environment checks for better CI validation and Docker compatibility. 🐳
  • Comet Integration Fix: Corrected class mapping index misalignment in Comet logging to avoid errors and misinterpretations in visualized data. 📋
  • Single-Class Model Consistency: Enforced single-class model output alignment by adjusting nc and names parameters during training. 🚀
  • Documentation Updates: Improved accuracy in multiple areas, embedding a YouTube interactive guide for YOLO12, and updating outdated citation links for research credibility. 📚
  • General Code Cleanup: Enhanced readability by adhering to PEP8 conventions (snake_case), simplifying code for maintainability. 🖋️

🎯 Purpose & Impact

  • Improved Data Augmentation: Provides users with accurate HSV augmentations, leading to better model robustness and performance during training.
  • Benchmark Accuracy: Ensures users receive correct performance metrics allowing better model selection and easier comparison across versions.
  • Enhanced Docker Usability: Smoother image builds and reduced dependency issues result in a simplified development experience. Perfect for CI pipelines!
  • Better Logging: Developers benefit from accurate class mappings in Comet analytics, avoiding confusion when examining predictions.
  • Single-Class Models Alignment: Offers consistency in model behavior, improving usability for tasks like binary classification.
  • Improved Documentation: Users gain easier access to tutorials, accurate technical references, and a better learning experience overall.
  • Cleaner Codebase: Enhances long-term maintainability and aligns code with modern Python standards, beneficial for both contributors and end-users.

This release makes strides in improving accuracy, user experience, and usability for developers and researchers alike. 🚀🌟

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.78...v8.3.79

v8.3.78: - ultralytics 8.3.78 new YOLO12 models (#​19325)

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🌟 Summary

This release, v8.3.78, brings an exciting new model to the family: YOLO12 🚀, featuring an attention-centric design for superior accuracy and efficiency across a variety of computer vision tasks.

📊 Key Changes

  • Introduction of YOLO12 Models:

    • New Architecture: Incorporates attention mechanisms like Area Attention, R-ELAN, and FlashAttention for optimized performance.
    • Comprehensive Task Support:
      • Object detection, segmentation, pose estimation, classification, and oriented bounding box (OBB) detection.
    • Benchmark Improvements:
      • Demonstrates higher mAP (mean Average Precision) and efficiency compared to YOLO10/YOLO11 and competitors like RT-DETR.

  • Model-Specific Enhancements:

    • Introduced multiple YOLO12 variants (n, s, m, l, x) catering to different computing environments such as cloud systems and edge devices.
    • Added new task-focused configurations for image classification, pose estimation, and segmentation.

  • Documentation Updates:

    • YOLO12 now included in detailed model documentation with performance metrics and usage examples.
    • Extensive references, including benchmarks for comparison with leading global detection models.

  • Code Simplifications and Bug Fixes:

    • ONNX Run-Time Fixes: Improved device handling and tensor reshaping for ONNX users.
    • TFLite Export Cleanup: Removed redundant parameters for simpler TensorFlow Lite export logic.
    • Code Refinement: Enhanced readability and maintainability across inference and export pipelines.
🎯 Purpose & Impact

  • Purpose:

    • YOLO12 brings a paradigm shift in accuracy and efficiency by adopting attention mechanisms tailored for real-time object detection.
    • Streamlines codebase for easier maintenance and integration in diverse projects.

  • Impact:

    • Developers gain access to cutting-edge state-of-the-art models excelling in versatility, speed, and precision.
    • Tasks like multi-object detection, segmentation, and pose estimation become more accessible for smaller devices (e.g., edge devices).
    • Improved user experience with easier model selection, robust export support, and refined prediction outputs.

🔮 This update is not only a leap forward in technological advancement but also a commitment to making intelligent vision accessible to all.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.77...v8.3.78

v8.3.77: - ultralytics 8.3.77 faster YOLOv8-Segment ONNX Runtime example (#​19312)

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🌟 Summary

The YOLOv8 v8.3.77 release introduces a significant performance optimization for ONNX Runtime segmentation models, as well as essential compatibility enhancements and minor fixes. 🚀

📊 Key Changes

  • 🚀 YOLOv8-Segment Optimization (ONNX Runtime)

    • Post-processing was drastically improved for both GPU (91.7% faster) and CPU (58.9% faster), resulting in a total inference speedup of up to 32.4%.

  • 🔧 Optional thop Dependency Support

    • Made the thop library optional by handling its absence gracefully to avoid errors in environments lacking it.

  • 📂 ONNX Export Improvements

    • Streamlined ONNX dynamic model export logic by revising type handling, ensuring better reliability and maintainability.

🎯 Purpose & Impact

  • Faster and More Efficient Inference ⚡

    • The optimization of YOLOv8-Segment ONNX Runtime reduces latency significantly, improving user experiences in deployment scenarios where real-time performance is essential.

  • Increased Compatibility with Minimal Setups 🤝

    • By making thop optional, the release ensures broader support for systems, including lightweight environments like Conda setups, enhancing user flexibility.

  • Improved Developer Experience 🛠️

    • Simplified export logic and code maintainability ensures a more robust development and debugging process for ONNX users.

These updates collectively enhance usability, efficiency, and reliability for YOLOv8 users across diverse applications. 🎉

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.76...v8.3.77

v8.3.76: - ultralytics 8.3.76 fix dynamic batch inference with NMS export (#​19249)

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🌟 Summary

The release of Ultralytics v8.3.76 introduces improved dynamic batch inference for ONNX models with NMS export, a better object tracking experience, and various code and documentation enhancements. 🚀

📊 Key Changes

  • Dynamic Batch Improvements:

    • Solved issues with dynamic=True and nms=True where the batch size was fixed at export.
    • Enabled dynamic input handling by introducing padding for unmatched batch sizes during ONNX exports.

  • Tracking Enhancements:

    • Addressed errors when processing Torch tensors in model.track().
    • Improved integration of original input images with trackers for accuracy.

  • Performance Accuracy:

    • Fixed GPU memory conversion errors for logging VRAM usage to ensure accurate reporting.

  • Documentation Updates:

    • Standardized formatting in documentation for better consistency.
    • Enhanced examples for interpreting prediction results across detection, pose, segmentation, and more.

  • Other Code Refinements:

    • Addressed layer miscount in logging by ensuring layers with no parameters are also displayed.
    • Enhanced GitHub issue templates for clearer differentiation between bug reports and feature requests.
🎯 Purpose & Impact
  • 🛠 Improved Model Deployment: Dynamic padding during export ensures robust handling of varying batch sizes while maintaining compatibility with ONNX workflows.
  • 🎥 Better Tracking: Smoother operation for streamed data and enhanced consistency in object tracking results benefit both developers and end-users.
  • 📋 Accurate Logging: Correct VRAM usage metrics improve debugging and resource optimization.
  • 📚 Developer Friendliness: Updated docs and examples make it easier for users to understand and utilize prediction results effectively.
  • 🚀 Efficiency Boost: Code tweaks and fixes culminate in faster, more accurate model handling without disruptions.

This release addresses several community-reported issues, focusing on operational accuracy and usability across export, tracking, and development workflows! 🙌

What's Changed

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@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
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from ba0bcb8 to 484eb7d Compare December 26, 2024 14:22
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.54 Update dependency ultralytics to v8.3.55 Dec 26, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 484eb7d to dd37bfc Compare December 31, 2024 15:16
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.55 Update dependency ultralytics to v8.3.56 Dec 31, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from dd37bfc to aac1e32 Compare January 2, 2025 21:27
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.56 Update dependency ultralytics to v8.3.57 Jan 2, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from aac1e32 to a812e1f Compare January 5, 2025 16:59
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.57 Update dependency ultralytics to v8.3.58 Jan 5, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from a812e1f to ae1aa49 Compare January 9, 2025 16:27
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.58 Update dependency ultralytics to v8.3.59 Jan 9, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from ae1aa49 to efed5b2 Compare January 13, 2025 14:55
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.59 Update dependency ultralytics to v8.3.60 Jan 13, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from efed5b2 to 7ad1fad Compare January 13, 2025 22:38
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.60 Update dependency ultralytics to v8.3.61 Jan 13, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 7ad1fad to 78bdede Compare January 16, 2025 10:54
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.61 Update dependency ultralytics to v8.3.62 Jan 16, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 78bdede to 74b1aa9 Compare January 17, 2025 14:38
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.62 Update dependency ultralytics to v8.3.63 Jan 17, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 74b1aa9 to dd62ddb Compare January 20, 2025 03:08
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.63 Update dependency ultralytics to v8.3.64 Jan 20, 2025
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@renovate renovate bot changed the title Update dependency ultralytics to v8.3.64 Update dependency ultralytics to v8.3.65 Jan 21, 2025
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.73 Update dependency ultralytics to v8.3.74 Feb 10, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 7b4a58f to e267e37 Compare February 13, 2025 05:16
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.74 Update dependency ultralytics to v8.3.75 Feb 13, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from e267e37 to 837da34 Compare February 18, 2025 17:45
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.75 Update dependency ultralytics to v8.3.76 Feb 18, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 837da34 to 9db0ea0 Compare February 19, 2025 18:34
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.76 Update dependency ultralytics to v8.3.77 Feb 19, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 9db0ea0 to 12d9fc3 Compare February 20, 2025 13:05
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.77 Update dependency ultralytics to v8.3.78 Feb 20, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 12d9fc3 to 6bbb721 Compare February 25, 2025 17:57
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.78 Update dependency ultralytics to v8.3.79 Feb 25, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 6bbb721 to 7528839 Compare February 26, 2025 13:40
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.79 Update dependency ultralytics to v8.3.80 Feb 26, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 7528839 to bc771d1 Compare March 2, 2025 16:49
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.80 Update dependency ultralytics to v8.3.82 Mar 2, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from bc771d1 to a75983a Compare March 4, 2025 21:22
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.82 Update dependency ultralytics to v8.3.83 Mar 4, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from a75983a to b933076 Compare March 5, 2025 21:19
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.83 Update dependency ultralytics to v8.3.84 Mar 5, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from b933076 to 47a0d0b Compare March 6, 2025 17:33
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.84 Update dependency ultralytics to v8.3.85 Mar 6, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 47a0d0b to 2dc1588 Compare March 10, 2025 13:21
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.85 Update dependency ultralytics to v8.3.86 Mar 10, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 2dc1588 to 75b0430 Compare March 11, 2025 00:50
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.86 Update dependency ultralytics to v8.3.87 Mar 11, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 75b0430 to 8ffe3b7 Compare March 11, 2025 18:27
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.87 Update dependency ultralytics to v8.3.88 Mar 11, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 8ffe3b7 to 9134108 Compare March 13, 2025 00:47
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.88 Update dependency ultralytics to v8.3.89 Mar 13, 2025
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