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chore(deps): update dependency ultralytics to v8.3.40 #7

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merged 1 commit into from
Dec 2, 2024
Merged

chore(deps): update dependency ultralytics to v8.3.40 #7

merged 1 commit into from
Dec 2, 2024

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@renovate renovate bot commented Nov 27, 2024 •
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This PR contains the following updates:

Package Change Age Adoption Passing Confidence
ultralytics (changelog) ==8.3.27 -> ==8.3.40 age adoption passing confidence

Release Notes

ultralytics/ultralytics (ultralytics)

v8.3.40: - ultralytics 8.3.40 new TrackZone Solution (#​17918)

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🌟 Summary

Ultralytics v8.3.40 introduces an exciting new feature: TrackZone, which enables targeted object tracking within specific areas of a video frame, rather than the entire frame. 🎯

📊 Key Changes
  • TrackZone Added: A new solution for zone-based tracking, allowing users to monitor objects in custom-defined regions.
  • Enhanced Documentation: Detailed guidance on TrackZone usage, its arguments, and real-world applications added. 📝
  • Framework Updates: Improvements to tracking arguments, CI dependency handling, and updated Raspberry Pi benchmarks.
🎯 Purpose & Impact
  • More Precise Analytics: By confining tracking to user-defined zones, the solution optimizes resource usage and allows fine-grained insights for scenarios like surveillance, crowd management, and industrial monitoring. 🚨
  • Simplifies Complex Applications: Users can now easily define and analyze specific areas of interest without needing to process unnecessary parts of a video feed, reducing computational overhead. 🚀
  • Improved Documentation and Benchmarks: Helps users navigate with ease while accessing expanded Raspberry Pi benchmarks for better framework comparison. 💡
Example Use Case

For instance, in a security application, you can define a "restricted area" within a camera feed and monitor only that zone for intrusions, improving both performance and practicality. 🛡️

What's Changed
New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.39...v8.3.40

v8.3.39: - ultralytics 8.3.39 fix classification validation loss scaling (#​17851)

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🌟 Summary

The Ultralytics v8.3.39 release focuses on improving model behavior, functionality, and user experience across multiple aspects, including classification validation, documentation enhancements, and tool usability. It introduces critical fixes and new features to improve the overall quality of the platform. 🚀

📊 Key Changes

  • 🧠 Fixed Classification Validation Loss:

    • Adjusted classification model's loss scaling during validation to improve output consistency and accuracy.
    • Introduced a refined approach to apply softmax only in necessary scenarios for clarity and precision.

  • 🎯 "Classes" Filter in Training:

    • Added a new classes argument to the training configuration, enabling model training on specific class IDs selectively.

  • 🎥 Enhanced Video Annotation Tool:

    • Introduced a "Sweep Annotation" utility for dynamic video annotation. Users can now visualize objects based on an interactive sweep line that tracks their positions.

  • 🎨 Improved Color Handling in LibTorch Example:

    • Addressed a key issue by adding a BGR to RGB conversion step in the C++ LibTorch inference example, ensuring color compatibility for accurate YOLO results.

  • 🗂️ Documentation Updates:

    • Significant improvements in README files:
      • Clickable YOLO11 performance plot images now redirect to documentation.
      • Enhanced clarity about model auto-download behavior and training details.
    • Added new high-quality tutorial videos across docs for better onboarding and understanding.
    • Fixed YOLOv11 references to the correct term YOLO11 for consistency.

  • ⚙️ Code Improvements and Maintenance:

    • Simplified segmentation handling with better clipping (clip()) for out-of-bounds coordinates in segmentation tasks.
    • Added an elegant __getattr__ method making model attributes (e.g., stride or task) directly accessible from the Model class.
    • Refined model logging for better debugging and developer experience.
🎯 Purpose & Impact
  • Enhanced Accuracy and Model Behavior: The classification loss scaling fix addresses a crucial inconsistency, delivering more reliable results during validation phases.
  • Increased Flexibility: The "classes" argument empowers users with precise control, making training workflows more tailored and efficient by focusing on specific class IDs. 💡
  • Better Video Annotation: The "Sweep Annotation" tool adds an intuitive way to annotate video data interactively, offering new possibilities for detection and tracking tasks.
  • Improved Inference Quality: The BGR to RGB fix ensures accurate detections for users operating in C++ environments with LibTorch inference.
  • Streamlined User Education: Updated and accessible documentation alongside engaging video tutorials helps onboard new users quickly while enhancing knowledge for experienced developers. 📚
  • Consistency: Terminology such as YOLO11 aligned across documentation ensures clarity and avoids user confusion.

This release keeps refining both functionality and usability, advancing the YOLO ecosystem for a diverse range of practical applications. 🎉

What's Changed
New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.38...v8.3.39

v8.3.38: - ultralytics 8.3.38 SAM 2 video inference (#​14851)

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🌟 Summary

The release of 'v8.3.38' introduces significant enhancements, particularly emphasizing video interaction capabilities through the new SAM2VideoPredictor class for object segmentation and tracking in videos. This update also includes general improvements and optimizations across various modules.

📊 Key Changes
  • SAM2VideoPredictor: A new class aimed at enhancing video segmentation and object tracking, supporting advanced interactions such as prompts for segment modifications.
  • Improved Video Segmentation: Features non-overlapping masks, better memory management, and support for interactive user prompts.
  • Configuration Clean-Up: Removal of obsolete parameters such as label_smoothing.
  • Platform Compatibility: Extended detection for NVIDIA Jetson devices, accommodating more models.
  • Documentation and Code Updates: Adjustments for improved clarity and accuracy in both code and documentation.
🎯 Purpose & Impact
  • 📽️ Enhanced Video Interaction: The SAM2VideoPredictor allows users to fine-tune video processing outputs dynamically, making video segmentation more precise and interactive.
  • 🚀 Efficiency & Resource Management: Optimized memory use during video segmentation leads to faster inferencing and resource savings, beneficial for running on resource-constrained devices.
  • 🛠️ Code Simplification: Removing unnecessary parameters like label_smoothing helps streamline configuration settings, reducing potential user confusion.
  • 📱 Broader Device Support: Updating device compatibility ensures the software is functional across a wider range of hardware, improving the user experience for those utilizing NVIDIA Jetson platforms.
  • 📚 Improved User Documentation: Enhanced documentation aids both beginners and advanced users by making it easier to understand and implement model configurations and changes efficiently.
What's Changed
New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.37...v8.3.38

v8.3.37: - ultralytics 8.3.37 TensorRT auto-workspace size (#​17748)

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🌟 Summary

The release of v8.3.37 introduces significant improvements and fixes across the export functionality and model operation modes, aiming to streamline user experience and enhance performance when using Ultralytics tools.

📊 Key Changes
  • TensorRT Auto-Workspace Size: Implements an auto-managed workspace size for TensorRT exports by default, allowing more flexibility and reducing manual configuration errors.
  • Label Padding Adjustment: Optimized the label augmentation by correctly updating vertical and horizontal padding, enhancing image annotation accuracy.
  • Model Evaluation Mode: Introduced an eval method to easily switch models between training and evaluation modes, ensuring consistent performance during model assessments.
  • Documentation Updates: Added video tutorials for better understanding of hand keypoint estimation and annotation utilities, and standardized dataset configuration references for clarity.
🎯 Purpose & Impact
  • Ease of Use: Setting the TensorRT workspace to None by default takes the burden off users to configure export parameters manually, simplifying the model export process.
  • Improved Accuracy: The fix in label padding ensures accurate annotations, critical for reliable model training and evaluation.
  • Consistent Evaluation: By allowing models to switch to evaluation mode seamlessly, users will experience more reliable model performance metrics which are crucial for assessments.
  • Enhanced Learning Resources: With new video tutorials, users can gain a deeper understanding of utilizing Ultralytics features, potentially increasing the adoption and correct usage of functionalities.
  • Documentation Consistency: Transitioning to a uniform dataset configuration in examples reduces confusion, making it easier for users to follow guides and setups.
What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.36...v8.3.37

v8.3.36: - ultralytics 8.3.36 unpin OpenVINO ARM install version (#​16600)

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🌟 Summary

This release focuses on enhancing compatibility with OpenVINO, refining documentation, optimizing code performance, and improving theming logic in documentation.

📊 Key Changes

  • OpenVINO Compatibility: Updated the Ultralytics package to version 8.3.36; OpenVINO and NNCF dependencies now require newer versions.
  • Documentation Tweaks: Corrected model names and improved documentation consistency in export tables.
  • Code Refactoring: Streamlined and optimized JavaScript and Python code to enhance readability, maintainability, and performance.
  • Theme Management: Refined theme change logic in documentation, improving the user experience when switching between light and dark modes.
  • Region Points Update: Standardized default region points for more accurate object counting tasks.

🎯 Purpose & Impact

  • Enhanced Tool Compatibility: Ensures the software works smoothly with the latest OpenVINO version, especially on macOS, reducing export issues. 🖥️
  • Improved Documentation Accuracy: Accurate model references and improved readability prevent user confusion. 📚
  • Efficiency and Performance: Optimized code results in faster execution which enhances productivity and user experience. 🚀
  • Better User Experience: Improved theme logic offers a smoother transition between modes, enhancing the user interface interaction. 🌗
  • Reliable Object Detection: Revising region points leads to more consistent and reliable object detection and tracking outcomes. 📐🔍

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.35...v8.3.36

v8.3.35: - ultralytics 8.3.35 enable auto letterbox if model is dynamic (#​17687)

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🌟 Summary

This release, version 8.3.35, introduces enhanced support for models with dynamic shapes in image processing, making model handling more adaptable and efficient. 🚀

📊 Key Changes

  • Dynamic Models Support: Improved the pre_transform function to enable automatic letterboxing when working with models that support dynamic input shapes.
  • Updated Docker Configuration: Switched Docker's base image to Python 3.11.10 for better consistency and added PaddlePaddle installation for broader compatibility.
  • Documentation Enhancements: Improved Ray Tune documentation, benchmarking tools, and documentation site usability with a scalable search bar.
  • Cosmetic and Code Maintenance: Various JavaScript updates for cleaner code structure and updated styles for improved user interaction.

🎯 Purpose & Impact

  • Enhanced Model Handling: By supporting dynamic shapes, the update ensures that users working with such models benefit from accurate image preprocessing and potentially improved performance.
  • Consistency and Compatibility: Docker updates aid in consistent environment setup and extend functionality by supporting PaddlePaddle installations.
  • Improved User Experience: Revised documentation and a smoother search experience make it easier for users to find information and ensure a seamless interaction with the site.
  • Developer-Focused Improvements: Code and workflow updates facilitate easier maintenance and readability, enabling developers to work more efficiently.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.34...v8.3.35

v8.3.34: - ultralytics 8.3.34 FastSAM non-detection fix (#​17628)

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🌟 Summary

The update to version 8.3.34 focuses on improving prediction reliability in the FastSAM model and enhances various internal systems to optimize workflows and accuracy. 🚀

📊 Key Changes

  • 🛠️ Enhanced FastSAM model's prompt method to handle cases with empty predictions effectively.
  • 🔧 Updated GitHub Actions to use uv for dependency installation, reducing potential Python packaging issues.
  • 📋 Improved project name handling in training setups to fix issues with special characters, ensuring compatibility with systems like W&B.
  • 🔄 Revised v8_transforms function with better hyperparameter handling using Namespace.
  • 🚀 Enhanced dataset configuration for RT-DETR with new parameters like fraction, single_cls, and classes to better align with YOLO dataset management.
  • 📈 Refined object counting method in heatmaps to use centroids instead of bounding boxes for improved accuracy.

🎯 Purpose & Impact

  • ✅ Reliable Predictions: The FastSAM model update helps avoid errors during inference when some results are empty, making the prediction process more robust.
  • 💡 Streamlined Workflows: Switching to uv in GitHub Actions enhances dependency management and ensures smoother continuous integration.
  • 🗄️ Project Naming Flexibility: By reformatting project names, users will face fewer naming issues, particularly when integrating with various external systems.
  • 📊 Improved Handling of Hyperparameters: Developers benefit from more manageable code and potentially fewer bugs with the new Namespace implementation.
  • 🎯 Enhanced Customization: The dataset improvements allow users more control over the training process, focusing on specific classes and data subsets for faster experiments.
  • 👁️‍🗨️ Better Object Tracking: The refined object counting mechanism boosts the precision of tracking, enhancing analytics accuracy which can significantly improve object detection applications.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.33...v8.3.34

v8.3.33: - ultralytics 8.3.33 Solutions counter direction fix (#​17607)

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🌟 Summary

The latest release, v8.3.33, primarily focuses on refining object counting in the Ultralytics YOLO framework, boosting accuracy for tracking objects across specified regions.

📊 Key Changes

  • Object Counting Enhancement: Overhauled the object counting logic by focusing on centroids for more precise tracking, especially in complex shapes and motions.
  • Updated Documentation: Clarified the retina_masks and device arguments in the documentation for better user comprehension.
  • Expanded Hardware Compatibility: Enabled MNN export on Raspberry Pi and NVIDIA Jetson platforms.
  • CI/CD Improvements: Upgraded GitHub workflow actions for better integration with Codecov and Slack.

🎯 Purpose & Impact

  • Improved Counting Accuracy: By utilizing centroids over bounding boxes, the update ensures more reliable object tracking and counting, crucial for applications needing high precision. 🎯
  • User Clarity: Enhanced documentation provides clearer guidelines, helping both novice and expert users understand configuration impacts better.
  • Broader Device Support: Allowing MNN exports on more devices fosters flexibility and innovation, broadening the community's ability to deploy models on diverse hardware setups.
  • Streamlined Workflows: Upgrades to GitHub actions contribute to more efficient development cycles and error handling, ensuring smoother operations and faster updates.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.32...v8.3.33

v8.3.32: - ultralytics 8.3.32 New Dog-Pose dataset (#​17556)

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🌟 Summary

The release of v8.3.32 introduces a major new dataset called "Dog-pose", designed for pose estimation tasks, along with some important improvements and fixes.

📊 Key Changes

  • Dog-pose Dataset: Added a new dataset consisting of approximately 6,000 images with detailed annotations for 24 keypoints per dog, specifically for pose estimation using YOLO11.
  • Documentation Update: Enhanced guides and introductory materials for the Dog-pose dataset, including usage through Python and CLI examples.
  • Link Fix: Corrected a broken URL in the Jetson device setup documentation.
  • Workflow Update: Extended retry delay for link checks in the GitHub workflow to enhance reliability.
  • Efficiency Fix: Improved conditional logging for WandB reporting by checking the availability of plot data.

🎯 Purpose & Impact

  • 🐕 Enhanced Pose Estimation: The Dog-pose dataset greatly expands capabilities in animal pose estimation, useful in fields like veterinary research and animal behavior analysis.
  • 📘 User-Guidance: Updated documentation makes it easier for users to leverage the new dataset effectively in their projects.
  • 🔧 Improved Accessibility: Fixing documentation links enhances user experience by providing direct access to the correct setup resources.
  • 🕒 Optimized Workflow: Longer delays between retries in automated link checks reduce server loads and improve the reliability of workflows.
  • 🎨 Efficient Resource Use: The logging enhancement prevents the saving of unnecessary plots, optimizing storage and improving artifact management in model training.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.31...v8.3.32

v8.3.31: - ultralytics 8.3.31 add max_num_obj factor for AutoBatch (#​17514)

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🌟 Summary

The v8.3.31 release of Ultralytics introduces enhancements to automatic batch size estimation during model training, which aims to optimize memory usage and manage CUDA memory issues more effectively.

📊 Key Changes

  • Batch Size Optimization: Implemented auto_batch functionality to determine the best batch size by evaluating memory consumption.
  • Improved Profiling: The profiling tools have been updated to include a max_num_obj parameter for better batch size accuracy.
  • Error Management: Introduced logging for CUDA out-of-memory warnings and an automatic switch to CPU computation when necessary.
  • Documentation Updates: Removed the verbose argument from training documentation as it was deemed ineffective.

🎯 Purpose & Impact

  • Efficient Memory Use: Automatically adjusting batch sizes helps prevent overloading GPU memory, resulting in more efficient and stable training sessions. This is particularly beneficial for preventing abrupt interruptions due to memory errors.
  • Greater Reliability: By switching to CPU processing when encountering memory errors, the system maintains training continuity, avoiding crashes and ensuring an uninterrupted user experience.
  • Simplified User Experience: Streamlining training configuration by removing unnecessary options enhances usability, making the setup less complex for users.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.30...v8.3.31

v8.3.30: - ultralytics 8.3.30 run TAL on CPU if torch.OutOfMemoryError (#​17515)

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🌟 Summary

Version 8.3.30 of Ultralytics introduces a resilient fallback for running task alignment processes on CPU in case of GPU memory shortages, enhancing stability and user experience for YOLO applications. 🚀

📊 Key Changes

  • Memory Management: Implemented a CPU fallback mechanism for task alignment calculations if a GPU torch.OutOfMemoryError occurs.
  • Method Refactoring: Introduced a helper method _forward to elegantly manage memory overflow conditions.
  • Docker and Documentation Fixes: Improved Docker image tagging and fixed a broken Jetson device documentation link.
  • Enhanced Features: Simplified documentation examples and introduced a new RegionCounter module for easier region-based object counting.

🎯 Purpose & Impact

  • Stability and Reliability: By ensuring task alignment processes can run on CPU under memory pressure, the update prevents application crashes and allows users with limited GPU resources to continue operations smoothly.
  • User Experience: The changes make YOLO operations more flexible and robust, especially in environments with constrained computational resources, helping users to maintain performance without interruptions.
  • Documentation and Usability: Improved documentation clarity makes it easier for both new and existing users to implement video analytics and other YOLO model features effectively. The RegionCounter addition simplifies integrating real-time object counting in specific video regions, broadening the tool's practical applications. 🔧

These updates and enhancements ensure that users have a smoother and more reliable experience with Ultralytics YOLO, particularly in resource-constrained settings.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.29...v8.3.30

v8.3.29: - ultralytics 8.3.29 Sony IMX500 export (#​14878)

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🌟 Summary

The v8.3.29 release has introduced a new capability in the Ultralytics YOLO framework, enabling the export of YOLOv8 models to the Sony IMX500 format. This advancement supports AI deployment on devices like Raspberry Pi AI Cameras, enhancing their utility for smart applications.

📊 Key Changes

  • Sony IMX500 Export Support: Added the option to export models in the Sony IMX500 format, crucial for devices with constrained resources.
  • New FXModel Class: Implemented for increased compatibility with torch.fx, facilitating advanced model manipulations.
  • Updated .gitignore: Now ignores *_imx_model/ directories, which store exported model artifacts.
  • Documentation and Tests: Comprehensive documentation and tests added to cover the new export functionality, ensuring smooth user experience and reliability.

🎯 Purpose & Impact

  • Enhanced Device Integration: The ability to export to Sony's IMX500 format allows for efficient AI processing on Raspberry Pi AI Cameras, making edge computing more viable and accessible. 🛠️🎥
  • Improved User Guidance: The updated documentation provides clear steps for users to leverage this new feature, enabling developers to quickly integrate it into their projects. 📚
  • Streamlined Development Process: The addition of the FXModel class and the support for the IMX500 format simplifies the model deployment process, further reducing the barriers to implementation on edge devices. 🖥️💡

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.28...v8.3.29

v8.3.28: - ultralytics 8.3.28 new Solutions CLI commands (#​17233)

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🌟 Summary

The release of version 8.3.28 introduces new command-line interface (CLI) commands for "Solutions," allowing users to easily execute various video analytics tasks.

📊 Key Changes

  • New Solutions CLI Commands: Users can now use CLI commands to apply different video analytics solutions without needing to modify arguments manually.
  • Additional CLI Examples: Includes CLI examples for tasks like object counting, heatmaps, queue management, workout monitoring, speed estimation, and more, complete with customizable parameters.
  • Enhanced Auto-Annotation: Improved auto-annotation functionality with new parameters like max_det to limit detections and classes for class-specific filtering.
  • Updated Documentation and Badges: Enhancements in documentation accuracy with updated contributor details and added visibility via new badges in README files.
  • Rust and TFLite Examples: New and improved examples for Rust ONNX runtime and TFLite Python integration for YOLO models.
  • New Docker Support: Added a JupyterLab Docker image for improved interactive development support.

🎯 Purpose & Impact

  • Ease of Use: Simplifies using video analytics solutions directly from the command line, making it more accessible for users to implement complex video tasks with YOLO models.
  • Enhanced Control: Users gain more precise control over dataset annotation outputs, aiding in task-specific preparation.
  • Improved Documentation: Allows for better tracking of project metrics and user interactions with enhanced visibility.
  • Robust Cross-Platform Support: New examples and JupyterLab Docker integration support diverse environments, improving user experience and accessibility.
  • Platform Precision: Export compatibility checks ensure smoother model conversions across different hardware setups.

Overall, this release significantly enhances usability and equips users with flexible tools for effective computer vision tasks.

What's Changed

Configuration

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@renovate renovate bot force-pushed the renovate/ultralytics-8.x branch from 680aa17 to 6a50b4a Compare November 29, 2024 01:54
@renovate renovate bot changed the title chore(deps): update dependency ultralytics to v8.3.38 chore(deps): update dependency ultralytics to v8.3.39 Nov 29, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x branch from 6a50b4a to 66f2060 Compare December 2, 2024 15:32
@renovate renovate bot changed the title chore(deps): update dependency ultralytics to v8.3.39 chore(deps): update dependency ultralytics to v8.3.40 Dec 2, 2024
@aslafy-z aslafy-z merged commit 6c6d42e into main Dec 2, 2024
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@aslafy-z aslafy-z deleted the renovate/ultralytics-8.x branch December 2, 2024 16:01
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