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Update dependency ultralytics to v8.3.75 #124

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This PR contains the following updates:

Package Change Age Adoption Passing Confidence
ultralytics (changelog) 8.3.49 -> 8.3.75 age adoption passing confidence

Release Notes

ultralytics/ultralytics (ultralytics)

v8.3.75: - ultralytics 8.3.75 Comet update to new comet_ml.start() API (#​19187)

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🌟 Summary

The v8.3.75 release includes robust updates for improved model export compatibility, user experience, and error handling across platforms, alongside enhanced documentation and integration refinements. 🚀

📊 Key Changes

  • Enhanced CometML Integration:

    • Transitioned to the new comet_ml.start() API for smoother experiment handling.
    • Deprecated the COMET_MODE variable, introducing COMET_START_ONLINE for consistency.

  • Export Function Updates:

    • Protobuf Dependency: Added support for protobuf>=5 for TensorFlow and TFLite exports, resolving compatibility issues.
    • Edge TPU and TF.js: Addressed platform-specific limitations for ARM64 and Linux exports to prevent unsupported configuration errors.

  • Documentation Improvements:

    • Updated SAM auto-annotation, YOLOv8, and export format descriptions for clarity.
    • Redesigned inference examples to use accessible publicly hosted image URLs.

  • New CLI Solutions:

    • Introduced practical use cases, including object counting, workout monitoring, queue analysis, and browser-based inference with Streamlit.

  • Benchmarking Added:

    • Include new comparative performance metrics for popular object detection models like Gold-YOLO, YOLO-NAS, RTDETRv3, etc.

  • Windows-Specific Fix:

    • Resolved an async file write bug to improve caching reliability on Windows systems.

  • Improved Timing Precision:

    • Switched to time.perf_counter() for latency measurements, ensuring greater precision during benchmarking.
🎯 Purpose & Impact

  • Improved Experiment Tracking:

    • Seamless CometML integration provides better environment consistency and logging during training processes.

  • Enhanced Export Reliability:

    • Future-proofs TensorFlow and TFLite workflows while providing early error detection for ARM64/Linux users.

  • Streamlined User Experience:

    • Updated documentation and example consistency ensure clarity, especially for beginners, minimizing friction during model setup and usage.

  • Greater Platform Support:

    • Addressed critical Windows and platform-specific export edge cases, enhancing cross-platform usability.

  • Better Model Insights:

    • Added benchmarks empower users to make informed decisions about which object detection models to implement based on accuracy, speed, and computational cost.

This release focuses heavily on improving reliability, usability, and documentation quality while resolving critical bugs and adding more tools for diverse real-world applications.

What's Changed
New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.74...v8.3.75

v8.3.74: - ultralytics 8.3.74 Fix Ray Tune callback error (#​19144)

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🌟 Summary

Ultralytics v8.3.74 introduces updates to improve compatibility with modern tools (like Ray Tune), smooth errors, and enhance deterministic training and export flexibility. 🛠✨ Simplified workflows for developers with better usability.

📊 Key Changes
  • 🔧 Fixed Ray Tune Callback Issues: Replaced deprecated ray.tune.is_session_enabled() with ray.train._internal.session.get_session() ensuring compatibility with latest Ray versions.
  • 🛠 Enhanced Deterministic Training Control: Added unset_deterministic() to handle environment changes, and prevent unnecessary CUDA warnings.
  • 🖼 PIL Image Support in plot(): Allowed direct return of PIL images with annotator.im, improving compatibility with PIL workflows.
  • 🚀 Improved Export Flexibility: Adjusted model.export() to take a data parameter while simplifying predict() calls.
  • 🐳 Optimized Docker Workflow: Improved Docker token authentication and switched to docker build for better stability and security.
  • ✅ Streamlined Benchmarking: Cleaned dataset and metric assignments in benchmarking to avoid redundancy and improve reliability.
🎯 Purpose & Impact
  • 🚀 Greater Compatibility: Seamless integration with the latest versions of Ray ensures that errors linked to deprecated methods are resolved.
  • ⚡ Workflow Flexibility: Managing deterministic settings dynamically boosts training adaptability while cleaning up workflow logs.
  • 📸 Visualization Improvements: Returning PIL images directly simplifies further processing in pipelines dependent on image outputs.
  • 🛠️ User-Friendly Model Exports: Configurable export makes model usage and testing more straightforward for developers.
  • 🔒 Stronger Security: Docker workflow improvements enhance authentication security, benefitting advanced build setups.
  • ✅ Clarity in Development: Benchmark logic cleanup minimizes confusion and potential errors, improving developer experience.

This version is packed with incremental improvements, making model training, testing, and deployment smoother and more user-friendly while preparing Ultralytics for the future. 🎉

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.73...v8.3.74

v8.3.73: - ultralytics 8.3.73 GitHub Container Registry Images at ghcr.io (#​19114)

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🌟 Summary

The Ultralytics v8.3.73 release focuses on enhancing containerization workflows, updating library dependencies, improving documentation, and refining the development process. 🚀

📊 Key Changes
  • Containerization Enhancements:
    • Added support for publishing Docker images to both GitHub Container Registry (GHCR) and Docker Hub with rich metadata for better usability. 🐋
    • Removed Ubuntu 24.04 ARM in CI workflows for streamlined testing.
  • Dependency and Platform Updates:
    • Updated NVIDIA Jetson support with PyTorch 2.2.0 and Torchvision 0.17.2 for improved compatibility and performance. 🤖
    • Removed the beautifulsoup4 dependency, cleaning up the development environment. 🧹
  • Code Refactoring:
    • Improved SQL result export by simplifying insertion logic and fixing potential issues with empty results.
    • Enhanced type hinting for better code clarity.
  • Documentation and Tutorial Updates:
    • Added an embedded YouTube tutorial on Package Segmentation in the documentation. 🎥✨
🎯 Purpose & Impact
  • Containerization Accessibility:
    • Publishing images to both Docker Hub and GHCR ensures users have multiple options for pulling images, increasing global availability and reducing friction. 🌍
    • The inclusion of detailed metadata in Docker images improves clarity for end-users.
  • Better Hardware and Development Support:
    • NVIDIA Jetson users benefit from newer library versions for seamless deployment and better model performance.
    • Leaner development environments reduce installation times and maintenance burdens.
  • Improved Learning Resources:
    • The YouTube tutorial enriches the documentation and aids new and existing users in understanding package segmentation workflows visually. 📚👩‍💻
TL;DR: This version updates Docker container workflows, improves NVIDIA Jetson compatibility, cleans up dev dependencies, and enhances user education through new video tutorials. 🚀💡
What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.72...v8.3.73

v8.3.72: - ultralytics 8.3.72 Fix NVIDIA Jetson DLA core support for DLA inference (#​19078)

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🌟 Summary

The v8.3.72 release focuses on enhancing NVIDIA Jetson DLA (Deep Learning Accelerator) core compatibility for inference, improving export documentation, and resolving minor inefficiencies and errors for broader usability and smoother performance. 🚀

📊 Key Changes
  • Enhanced NVIDIA Jetson DLA Support:
    • Introduced explicit control of DLA core selection (dla:0/dla:1) during TensorRT export and inference.
    • Added detailed documentation of NVIDIA Jetson DLA device specifications (core count, frequency, etc.).
    • Fixed metadata handling for DLA-specific inference settings.
  • Export Documentation Overhaul:
    • Added detailed argument tables for all model export formats (e.g., ONNX, TensorRT, CoreML), improving clarity on custom export options such as half-precision (FP16), INT8 quantization, and dynamic input sizes.
  • Optimized seg_bbox Rendering:
    • Refined label handling logic in the plotting utility, reducing unnecessary operations if a label is absent, slightly improving performance.
  • Bug Fixes:
    • Resolved an issue with missing nc attributes during NMS export, improving reliability in multi-GPU or custom training setups.
  • Documentation Updates:
    • Enhanced Crack Segmentation Dataset resources with direct Colab integration, a tutorial notebook, and a demo video for easier onboarding.
🎯 Purpose & Impact
  • Improved Compatibility: The NVIDIA Jetson DLA improvements ensure that edge devices benefit from seamless inference setups, enabling accelerated performance with reduced bottlenecks. Ideal for IoT and edge AI devices. 🖥️✨
  • Simplified Export Process: The new export argument tables demystify complex configurations, empowering users to adapt models for their specific hardware or workflows more easily. 📦🔧
  • Performance Benefits: Minor optimizations ensure faster runtime efficiency, especially for visualization and plotting tasks where unnecessary computations are avoided. ⚡
  • Enhanced Reliability: Fixes like handling missing nc attributes and metadata improve model robustness, particularly in advanced user scenarios (e.g., multi-GPU setups, custom models). ✅
  • Streamlined Learning Experience: The improved Crack Segmentation training resources lower the barrier to entry for researchers in infrastructure and transportation safety fields. 🛠️🚗

This release represents a strong push for enhanced edge device support, better export usability, and overall reliability improvements while empowering both beginners and advanced users. 🎉

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.71...v8.3.72

v8.3.71: - ultralytics 8.3.71 require explicit torch.nn usage (#​19067)

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🌟 Summary

The v8.3.71 update enhances code clarity and resolves dependency issues by replacing ambiguous nn references with explicit torch.nn usage. It also improves documentation and user experience with various fixes and additions.

📊 Key Changes

  • Explicit Naming: Codebase updated to use torch.nn instead of nn, ensuring clarity between PyTorch and Ultralytics modules.
  • Dependency Fix: Capped beautifulsoup4 to version 4.12.3 to avoid documentation build errors.
  • Progress Bar Optimization: Added mininterval=1.0 for smoother and consistent updating of tqdm progress bars.
  • Documentation Improvements:
    • Added tutorial video to TrackZone integration docs, enhancing learning and usability.
    • Updated handling of relative dataset paths for better clarity.
    • Added troubleshooting tips for RKNN issues with Rockchip integration.
    • Simplified cloning instructions for the picamera2 repository in Sony IMX500 setups.
    • Excluded auxiliary pages like /compare from the documentation navigation.
  • Minor Fixes: Adjusted documentation examples for better readability and alignment with Python best practices.

🎯 Purpose & Impact

  • Enhanced Readability 🧹:

    • Disambiguating torch.nn vs. ultralytics.nn reduces confusion for developers and improves compliance with coding standards.
    • Cleaner, more informative documentation makes tools easier to use, especially for new users.

  • Improved User Experience 🎥📝:

    • Video tutorials and better dataset guidance streamline workflows and learning.
    • RKNN troubleshooting tips address runtime issues effectively for advanced users.

  • Smoother Development Workflow 🚀:

    • Dependency fixes ensure a more stable experience during documentation builds.
    • The explicit naming structure reflects best practices, making the codebase future-proof and easier to maintain.

This release primarily aids developers with code clarity and users with enhanced documentation. Whether you're debugging workflows, learning tools, or contributing to the codebase, these updates simplify the process and save time. 🌟

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.70...v8.3.71

v8.3.70: - ultralytics 8.3.70 add data argument to Sony IMX500 export (#​18852)

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🌟 Summary

The v8.3.70 release brings feature enhancements with improved export functionalities, updated compatibility for PyTorch, and usability enhancements in benchmarking and documentation. 🚀

📊 Key Changes
  • Sony IMX500 Export Update: Added support for the data argument, enabling dataset configuration during export for better control over quantization in formats like OpenVINO, TensorRT, and TF Lite. 📁
  • Torch 2.6 Compatibility: Updated Torch-Torchvision mappings to ensure seamless functionality with the latest PyTorch update. 🔧
  • Format-Specific Benchmarking: Introduced benchmarking support for individual formats (e.g., ONNX) to allow targeted performance evaluations. 📊
  • NVIDIA DLA Support: Implemented support for running models on specific NVIDIA DLA cores—a key feature for specialized hardware optimization. 🖥️
  • Improved numpy Stability: Pinned numpy version to prevent compatibility issues with OpenVINO and TFLite during CI tests. ✅
  • Documentation Enhancements: Added tutorial videos and refined sections for clarity, aiding users and contributors. 📚
🎯 Purpose & Impact

  • Improved Export Workflows:

    • Purpose: The data argument helps users customize exports with specific dataset configurations, simplifying quantization and compatibility for edge and on-premise deployment.
    • Impact: Makes exports more robust and adaptable to diverse workflows, ensuring higher-quality models with optimized performance.

  • Torch Compatibility:

    • Purpose: Keep the framework current with the latest PyTorch improvements.
    • Impact: Allows users to leverage PyTorch 2.6's advancements without compatibility hiccups, maintaining a seamless experience.

  • More Granular Benchmarking:

    • Purpose: Enable granular analysis of models' efficiency in specific formats like ONNX or TensorFlow Lite.
    • Impact: Helps developers fine-tune models for scenarios where particular formats are essential for deployment.

  • DLA Optimization:

    • Purpose: Ensure efficient inference on NVIDIA's specialized hardware.
    • Impact: Reduces computational overhead and maximizes performance for users running models on NVIDIA DLA platforms.

  • CI Stability with numpy:

    • Purpose: Prevent runtime or testing errors due to incompatible numpy versions.
    • Impact: Ensures reliable and predictable performance for developers and CI pipelines.

  • Accessible Documentation:

    • Purpose: Make it easier for new contributors and users to onboard through visual and detailed guides.
    • Impact: Encourages community growth and simplifies the learning curve for both model and framework regulars.

🎉 This release is packed with features to empower smoother workflows, improve hardware compatibility, and promote user-friendly innovation! 🌟

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.69...v8.3.70

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

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🌟 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

Full Changelog: ultralytics/ultralytics@v8.3.68...v8.3.69

v8.3.68: - ultralytics 8.3.68 Benchmarking model path fix (#​18894)

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🌟 Summary

This release (v8.3.68) delivers meticulous updates enhancing benchmarking workflows, export processes, documentation clarity, and model comparison tools for improved usability and precision. 🚀✨

📊 Key Changes

  • Benchmarking Model Path Fix: Corrected model path handling in benchmarking to prioritize pt_path, fallback to ckpt_path, and then model_name for file identification. Improved log clarity.
  • EfficientDet Integration: Added EfficientDet (d0-d3) benchmarking stats for performance evaluation with other models.
  • Enhanced Visualization: Streamlined chart rendering for benchmarks, including refined dataset logic and active model configurations via page settings.
  • Export Adjustments: Fixed issues with ONNX dynamic export, OpenVINO int8, and TFLite at edge cases (imgsz=32). Improved handling of classification models and adjusted NMS logic.
  • Documentation Updates: Improved AzureML Python version recommendations and introduced a fallback mechanism for file minification during documentation builds.

🎯 Purpose & Impact

  • 📋 Clarity & Consistency: Benchmarking logs now show clearer and more intuitive references to simplify debugging and analysis.
  • 🚀 Improved Model Evaluation: Adding EfficientDet and chart enhancements helps users make better decisions when comparing models.
  • ⚙️ Robust Edge Case Handling: Fixes to TFLite, ONNX, and OpenVINO exports safeguard against errors, particularly with smaller image sizes or specific benchmarks.
  • 🧪 Improved Testing & Usability: Adjustments in export configuration reduce runtime errors during testing.
  • 📝 Developer-Friendly Documentation: Clarified setup instructions in AzureML and optimized minification improve user experience, especially for new developers.

This release focuses on greater flexibility, reliability, and usability for users managing benchmarking, exporting, and evaluating models! 🌟

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.67...v8.3.68

v8.3.67: - ultralytics 8.3.67 NMS Export for Detect, Segment, Pose and OBB YOLO models (#​18484)

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🌟 Summary

v8.3.67 introduces Non-Maximum Suppression (NMS) export capability for all YOLO models, including detection, segmentation, pose estimation, and oriented bounding box (OBB) tasks. 🎉

📊 Key Changes
  • Added NMS support during export for multiple formats: ONNX, TensorRT, TFLite, TFJS, SavedModel, OpenVINO, and TorchScript 🧩.
  • Enabled export-specific NMS for detect, segment, pose, and obb tasks with enhanced options like nms=True.
  • Expanded NMS-related functionality in models and exporters, including support for more complex configurations like agnostic or rotated NMS.
  • Streamlined model APIs to support embedded NMS using an updated NMSModel wrapper.
🎯 Purpose & Impact
  • Purpose:
    • Simplifies deployment pipelines by embedding NMS directly into exported models, removing the need for custom post-processing 🔗.
    • Enhances usability across deployment platforms (e.g., TensorFlow, ONNX, OpenVINO) by integrating NMS into the export pipeline.
  • Impact:
    • Significantly improves portability and ease of deployment for real-time applications 🎯.
    • Makes YOLO models more accessible for hardware-accelerated environments like TensorRT and Edge TPU 🚀.
    • Reduces errors and complexity in downstream pipelines by unifying pre/post-processing across tasks.

Overall, this update empowers developers to deploy YOLO models with integrated NMS across a wide variety of frameworks and platforms, making the process faster, more robust, and less error-prone. 🌟

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.66...v8.3.67

v8.3.66: - ultralytics 8.3.66 add Rockchip RKNN export in tutorial.ipynb (#​18848)

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🌟 Summary

The v8.3.66 release introduces support for Rockchip RKNN export, enhances hardware compatibility, refines documentation, and fixes several bugs, marking a significant step for developers working on edge AI and cross-platform deployments.

📊 Key Changes

  • ✨ Rockchip RKNN Support: Added the ability to export YOLO models to the RKNN format for deployment on Rockchip devices. Includes support for key parameters such as imgsz, batch, and name.
  • 📄 Integration Documentation:
    • Rockchip RKNN: Expanded instructions, performance benchmarks, and FAQs for smoother deployment.
    • Seeed Studio reCamera: Introduced documentation for using YOLO models with the reCamera for edge AI, including ONNX and cvimodel exports.
  • 🚀 Optimizations and Fixes:
    • Streamlined RKNN export code for better clarity and reliability.
    • Fixed ONNX model path issue to resolve export naming conflicts.
    • Enhanced debugging during ONNXRuntime CUDA initialization.
    • Improved label class validation logic to prevent dataset misconfigurations.
    • Updated Albumentations' ImageCompression augmentation range for higher realism.
  • 📦 Testing Enhancements:
    • Added CI support for Ubuntu ARM64 builds, enhancing platform compatibility for ARM-based environments.
  • 🔧 Code Improvements:
    • Introduced a custom TQDM class for consistent progress bar functionality.
    • Refactored unused arguments in modules like TorchVision and Index.
    • Adjusted optimizer logic during training for better performance in DDP setups.

🎯 Purpose & Impact

  • 🚀 Expanded Hardware Reach: Rockchip RKNN and Seeed Studio reCamera integration allow effortless deployment on specialized hardware, facilitating edge AI applications like real-time object detection and energy-efficient designs.
  • 🔗 Enhanced Usability: Rich documentation, benchmarks, and FAQs guide developers through complex setups, broadening accessibility for newcomers.
  • ✅ More Robust Exports: RKNN and ONNX updates improve compatibility and prevent export errors, reducing troubleshooting time for developers.
  • ⚡ Performance Gains: Augmentation and label validation improve model robustness and reduce errors during training and deployment across datasets and hardware.
  • 🛠 Streamlined Development: Refactors simplify code maintenance while maintaining compatibility, fostering a cleaner codebase.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.65...v8.3.66

v8.3.65: - ultralytics 8.3.65 Rockchip RKNN Integration for Ultralytics YOLO models (#​16308)

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🌟 Summary

Ultralytics v8.3.65 introduces support for exporting YOLO models to Rockchip's RKNN format, enabling seamless AI deployment on Rockchip NPUs. This release also includes numerous enhancements, stability improvements, and compatibility updates across modules. 🛠️💡

📊 Key Changes

  • Rockchip RKNN Integration:

    • Added RKNN export for YOLO models optimized for Rockchip hardware (e.g., RK3588, RK3566).
    • Simplified deployment with enhanced documentation and tools for RKNN models.
    • Supported hardware inference via rknn-toolkit2 with assisted device compatibility checks.

  • Stability and Performance Improvements:

    • Enhanced data loader robustness by handling worker termination safely under edge cases. ✅
    • Updated CI workflows to support macOS 15, ensuring compatibility with the latest macOS environments.

  • Compatibility Fixes:

    • Dynamic handling of numpy dependencies for NVIDIA Jetson devices to improve TensorRT functionality, reducing rigid constraints for all other users. 🌍

  • Refactoring:

    • Replaced mutable Python set with immutable frozenset across codebase to improve performance, ensure thread safety, and prevent unintended data modifications. 🚀

  • Documentation Cleanup and Maintenance:

    • Updated regex for consistent link conversion in documentation (plaintext to HTML), simplifying maintenance and improving reliability. ✍️

🎯 Purpose & Impact

  • Purpose:

    • Simplify AI deployment for edge devices, particularly Rockchip-based hardware, using RKNN format.
    • Improve the user experience by addressing edge-case errors in data loaders and ensuring compatibility with macOS and NVIDIA-specific scenarios.
    • Modernize internal code structure for faster performance and better reliability.

  • Impact:

    • 🧠 RKNN Support: Developers now have a streamlined process to export and deploy YOLO models on Rockchip's NPU-enabled devices, unlocking high-performance AI functionality for embedded systems.
    • ✅ Enhanced Stability: Reduced chances of crashes by safely handling resource cleanup issues (e.g., in data loaders).
    • 📈 Optimized Performance: Better immutability within system configurations creates a stable baseline for developers working in multi-threaded environments.
    • 📚 Improved Documentation: Cleaner formatting and precise integrations make it easier for users to implement new features and understand their capabilities.

This release empowers developers with new deployment options while improving the robustness and maintainability of the toolset. 🚀

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.64...v8.3.65

v8.3.64: - ultralytics 8.3.64 new torchvision.ops access in model YAMLs (#​18680)

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🌟 Summary

Ultralytics v8.3.64 introduces enhanced model flexibility with torchvision.ops compatibility in YAML-defined architectures, alongside significant usability improvements for handling tuning directories and cloud environments. Minor bug fixes, documentation, and educational updates further refine the overall user experience. 🚀

📊 Key Changes

  • Integration of torchvision.ops Layers in Model YAMLs 🛠️

    • Users can now leverage PyTorch's torchvision.ops utility classes directly in YAML model definitions, enhancing architecture customization (e.g., ops.Permute for tensor reshaping).
    • Made the truncate option configurable in YAML-defined models.

  • Improved Hyperparameter Tuning Usability 🎛️

    • Added the ability to set the tuning directory using the name parameter, making it easier to resume tuning runs.
    • Introduced better configuration handling during model tuning processes.

  • Enhanced Cloud Environment Detection 🌐

    • Added a new is_runpod() function to detect if code is running in a RunPod environment, optimizing cloud-based workflows.
    • Documentation updated to reflect these improvements for cloud users.

  • YOLOv3 Documentation Streamlined 📘

    • Consolidated YOLOv3 variants (YOLOv3u, YOLOv3-Tinyu, YOLOv3u-SPPu) and updated examples to use unified naming conventions.
    • Clarified the anchor-free head design inherited from YOLOv8, making guidance more intuitive for users.

  • Minor Fixes and Updates ✅

    • Addressed Docker-related issues, including clearer comments about GPU usage.
    • Fixed documentation link redirects for consistent user navigation.
    • Updated the "Model Monitoring" guide with an embedded instructional video on data drift detection.

🎯 Purpose & Impact

  • Flexibility in Model Design 🎨
    The new torchvision.ops integration allows for greater customization in defining models, simplifying workflows such as tensor manipulation for frameworks like Swin Transformer.

  • Streamlined Tuning Experience 🔄
    Improved directory handling ensures cleaner setups and makes resuming training or tuning easier, saving developers time and effort.

  • Enhanced Cloud and Deployment Support ☁️
    With better RunPod integration, users benefit from environment-specific optimizations, ensuring smoother and more efficient cloud-based operations.

  • Improved YOLOv3 Accessibility 🧑‍🏫
    Updated documentation and examples help reduce confusion around YOLOv3 variants, ensuring users can quickly understand and use the updated models effectively.

  • Refined User Experience 💡
    Documentation fixes, embedded video guides, and Docker comment updates ensure users have accurate and beginner-friendly information at their fingertips.

This release focuses on usability, extensibility, and clarity, making it easier for both new and advanced users to work with Ultralytics tools! 🚀✨

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
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from dd62ddb to 63a0454 Compare January 21, 2025 02:11
@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 force-pushed the renovate/ultralytics-8.x-lockfile branch from 63a0454 to afa6fef Compare January 23, 2025 14:40
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.65 Update dependency ultralytics to v8.3.66 Jan 23, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from afa6fef to 80ba431 Compare January 24, 2025 10:43
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.66 Update dependency ultralytics to v8.3.67 Jan 24, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 80ba431 to 5f4d7d3 Compare January 26, 2025 14:15
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.67 Update dependency ultralytics to v8.3.68 Jan 26, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 5f4d7d3 to 54809b7 Compare January 29, 2025 02:40
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.68 Update dependency ultralytics to v8.3.69 Jan 29, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 54809b7 to 59d4486 Compare January 30, 2025 16:03
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.69 Update dependency ultralytics to v8.3.70 Jan 30, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 59d4486 to 89fa058 Compare February 4, 2025 19:03
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.70 Update dependency ultralytics to v8.3.71 Feb 4, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 89fa058 to e16d4d8 Compare February 6, 2025 06:41
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.71 Update dependency ultralytics to v8.3.72 Feb 6, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from e16d4d8 to 0106c3e Compare February 7, 2025 11:24
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.72 Update dependency ultralytics to v8.3.73 Feb 7, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 0106c3e to 7b4a58f Compare February 10, 2025 02:36
@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
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