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Update dependency ultralytics to v8.3.70 #124

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This PR contains the following updates:

Package Change Age Adoption Passing Confidence
ultralytics (changelog) 8.3.49 -> 8.3.70 age adoption passing confidence

Release Notes

ultralytics/ultralytics (ultralytics)

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

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.63...v8.3.64

v8.3.63: - ultralytics 8.3.63 IMX500 sudo install fix (#​18714)

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🌟 Summary

The v8.3.63 release focuses on robustness improvements, developer convenience, and enhanced edge-case handling through better sudo command detection, optimized imports, and model training consistency in distributed environments.

📊 Key Changes

  • Sudo Detection Utility: Added is_sudo_available() function to streamline installation in export processes (e.g., Edge TPU, IMX500).
  • Optimized Imports: Simplified thop and other imports to improve loading efficiency for developers.
  • Distributed Training Fix: Corrected learning rate inconsistencies between single-GPU and DDP training by reapplying model attributes.
  • Documentation Enhancements: Improved link and file consistency (e.g., underscores to hyphens in filenames), and clarified version references for testing.
  • Dataloader Cleanup: Prevented errors during worker shutdown for cases without initialized workers.

🎯 Purpose & Impact

  • Improved Stability:

    • 🛠️ Systems without sudo now handle export dependencies more smoothly, reducing user setup issues.
    • 🚀 DDP training now avoids unintended fallback values, ensuring consistent performance across all setups.

  • Enhanced Developer Experience:

    • ⚡ Faster module loading due to scoped imports and redundant checks removal.
    • 📚 Clearer and more accessible documentation aids both developers and end-users in understanding workflows.

  • Error Prevention:

    • ✅ Edge-case safeguards for dataloaders avoid crashes during cleanup tasks.

This update reflects thoughtful optimizations for stability, usability, and performance while addressing minor bugs and maintaining interoperability. 🚀

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.62...v8.3.63

v8.3.62: - ultralytics 8.3.62 Fix non-deterministic transforms with albumentations>=1.4.21 (#​18701)

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🌟 Summary

Ultralytics 8.3.62 introduces key updates to ensure deterministic data augmentation for deep learning models, alongside other repository maintenance and usability improvements. 🚀

📊 Key Changes
  • Fix for Non-Deterministic Transforms: Resolved randomness issues in data augmentation with albumentations>=1.4.21 by adding support for setting a random seed. 🔧
  • Workflow and Documentation Enhancements:
    • Renamed GitHub workflow files for consistency (.yaml → .yml). 📂
    • Updated project licensing headers for clarity across all source files and configurations. 📝
    • Refreshed metadata to display the current year (2025) in documentation. 📅
  • Bug Fixes: Addressed sporadic dataloader freezes during consecutive training sessions. 🛠️
  • Code Clean-Up: Streamlined hyperparameter mutation logic by reducing unnecessary data access. ✨
🎯 Purpose & Impact
  • Consistent Training Results: Deterministic transformations allow reproducible results in model training, improving debugging and performance evaluation. 📈
  • Improved Usability: Updated workflows and file organization ensure better developer experience and easier maintenance for contributors. 🧑‍💻
  • Enhanced Stability: Fixes to the dataloader and optimization pipeline enhance reliability for users running training sessions repeatedly. 🚦
  • Professional Branding: Revised licensing headers and metadata maintain a polished and up-to-date project representation. 🌐

⚙️ Whether you're a developer improving AI systems or a researcher fine-tuning models, this release ensures smoother, more predictable processes while adhering to modern software conventions. 🎉

What's Changed
New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.61...v8.3.62

v8.3.61: - ultralytics 8.3.61 Restore Python 3.8 compatibility (#​18666)

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🌟 Summary

The v8.3.61 release restores compatibility with Python 3.8, refines several utility components, and makes user-facing improvements for better prediction workflows. It smooths the usability, performance, and flexibility of the Ultralytics ecosystem. 🚀💡

📊 Key Changes

  • Python 3.8 Compatibility Restored: Ensures older Python versions, including 3.8, are now supported by replacing operations incompatible with earlier versions. 🐍✅
    • Replaced | operator for dictionary merging with the Python 3.8-compatible ** method.
  • Prediction API Simplification: The Predictor and SAM2Predictor classes now output results in a consolidated object (result), replacing the previous multi-output format (masks, scores, boxes).
    • Example updates reflect this newer, simpler API. 🧰
  • CI Workflow Improvements: Adjusted triggers and settings in GitHub Actions workflows for smoother testing and CI processes.
  • Minor Bug Fixes: Addressed issues in utility functions, prediction methods, and loss calculations to improve overall reliability and prevent unexpected errors. 🛠️
  • Version Bumped to 8.3.61: Reflects these refinements in the package version.

🎯 Purpose & Impact

  • Broader Python Compatibility: Expands usability for projects running Python 3.8, accommodating users on older infrastructure without losing core functionality. 🌎
  • Simplified Model Predictions: A single-output format for Predictor and SAM2Predictor reduces user friction, improves code readability, and simplifies integration into pipelines. Especially helpful for new users! 🧩🚀
  • Improved Reliability: Minor bug fixes enhance stability, ensuring smoother performance across diverse use cases.
  • CI Stability & Testing Precision: Improvements to workflow configurations ensure better support for continuous integration and testing scenarios.
Note for Users 🤓:

If you’ve been using the Predictor or SAM2Predictor classes, make sure to update your scripts with the new result structure (e.g., use result.masks, result.boxes, etc.) instead of relying on separate outputs. This alignment will make your workflows cleaner and future-proof! 😊

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.60...v8.3.61

v8.3.60: - ultralytics 8.3.60 Fix CoreML Segment inference (#​18649)

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🌟 Summary

This update primarily fixes CoreML segmentation output handling, improves documentation, and enhances the usability of model features for developers and end users. 🔄✨

📊 Key Changes

  • CoreML Segmentation Fix: Improved logic for processing segmentation model outputs in autobackend.py (fixed reverse order issue for specific use cases).
  • Docker Update: Dockerfile upgraded to PyTorch 2.5.1 (with CUDA 12.4 and cuDNN 9), enabling improved compatibility and performance for Docker-based workflows. 🐳⚡
  • Colab Integrations: Added Colab badges to various documentation pages for easy, hands-on experimentation with datasets and tutorials. 📚🔗
  • Enhanced Auto-Annotation Documentation: Updated guides for segmentation auto-annotation, adding clarity around supported models and parameter configurations. 🖼️✅
  • Bug Reporting Improvements: Adjusted GitHub issue templates to request full traceback info for better debugging efficiency. 🛠️🔍
  • Standardized String Formatting: Converted strings to consistently use double-quoted f-strings for better code clarity and maintainability. 🖊️

🎯 Purpose & Impact

  • CoreML Update:

    • 🛠 Purpose: Fix and streamline CoreML model support, specifically for segmentation outputs.
    • 🌟 Impact: Smoother deployment for Apple-device-specific workflows with reduced risk of errors in segmentation processing.

  • Docker Upgrade:

    • 🚀 Purpose: Ensure containerized environments stay up-to-date and performant with compatibility fixes.
    • 🌟 Impact: Faster inference and training workflows with enhanced reliability.

  • Colab Additions:

    • 🛠 Purpose: Enable effortless model experimentation with interactive tools directly from the documentation.
    • 🌟 Impact: Lowers the entry barrier for new users while improving developer productivity.

  • Auto-Annotation Improvements:

    • 🎯 Purpose: Clarify how to use segmentation models like SAM and MobileSAM for large datasets.
    • 🌟 Impact: Saves time in dataset labeling by simplifying setup and enabling quick-start options.

  • Standardized String Formatting:

    • 🖊️ Purpose: Improve code readability and ease of maintenance for developers.
    • 🛡 Impact: Cleaner, more professional code with improved developer experience.

  • Bug Reporting Guidelines:

    • 🚨 Purpose: Collect more detailed user environment data to speed up issue resolution.
    • 🌍 Impact: Quicker turnaround in fixing bugs due to detailed diagnostic info.

No breaking changes in this release, ensuring smooth upgrades across workflows! 🛡💡

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.59...v8.3.60

v8.3.59: - ultralytics 8.3.59 Add ability to load any torchvision model as module (#​18564)

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🌟 Summary

The latest release, v8.3.59, introduces the ability to load any torchvision model as a backbone, along with several quality-of-life updates, including enhanced Docker support, dataset path refinements, and usability improvements in documentation and tools. 🚀

📊 Key Changes

  • 🔥 Custom TorchVision Backbone Support: Allows integration of any torchvision model (e.g., EfficientNet, MobileNet, ResNet) as YOLO backbones! Includes options for pretrained weights and layer customization.
  • 🖼️ Expanded Segmentation Mask Support: Added compatibility for .jpg masks alongside existing .png support.
  • 🐛 Validation Enhancements for INT8 Calibration: New checks ensure calibration datasets meet batch size requirements, providing more robust error handling.
  • 🛠️ Improved Docker Environment: Simplified JupyterLab installations and introduced retry mechanisms for Docker image pushes.
  • 🔧 Updated Dataset Paths: Refined YAML dataset path structures for better organization and reduced misconfigurations.
  • ⚙️ Enhanced Multi-Processing Documentation: Help added for common Windows-related training errors (e.g., RuntimeError) with clear solutions.
  • 📊 New Benchmarks: Extended NVIDIA DeepStream and Coral TPU performance benchmarks for development on Jetson devices and Raspberry Pi (including Pi 5).

🎯 Purpose & Impact

  • Flexibility & Power with TorchVision Backbones:
    • Users can now integrate models like ConvNext and MobileNet directly into YOLO pipelines.
    • Pretrained weights accelerate training for both object detection and classification tasks. 🔄
  • Streamlined Segmentation Workflows:
    • Compatibility with .jpg masks eliminates a frequent need for manual file conversions, saving time. 🕒
  • INT8 Improvements:
    • The validation on calibration size prevents breakdowns in compression workflows, ensuring higher-quality deployment setups.
  • Smoother Docker & DevOps:
    • Better Docker resilience and JupyterLab setup reduce installation friction for developers. 🐳
  • Training Guidance on Windows:
    • Clear troubleshooting advice mitigates pitfalls for users launching scripts in Windows environments for seamless training experiences.
  • Enhanced Benchmark Documentation:
    • Developers can now better choose the hardware and YOLO model precision (e.g., FP32, FP16, or INT8) for NVIDIA Jetson or Edge TPU use cases. 📈

This release offers powerful new capabilities for model customization and smoother workflows, making it a significant upgrade for developers working with YOLO and associated 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
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@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
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@renovate renovate bot changed the title Update dependency ultralytics to v8.3.59 Update dependency ultralytics to v8.3.60 Jan 13, 2025
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@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
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