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Update dependency ultralytics to v8.3.38 #114

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This PR contains the following updates:

Package Change Age Adoption Passing Confidence
ultralytics (changelog) 8.3.9 -> 8.3.38 age adoption passing confidence

Release Notes

ultralytics/ultralytics (ultralytics)

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

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.27...v8.3.28

v8.3.27: - ultralytics 8.3.27 HUB timed training fix (#​17298)

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🌟 Summary

The v8.3.27 release for Ultralytics primarily includes improvements in repository management and default training settings, alongside several enhancements to benchmarking and export functionality.

📊 Key Changes

  • Default Training Epochs: Set a fallback of 100 epochs in trainer.py if no specific value is provided.
  • Author Information Update: Refreshed GitHub avatars and usernames for contributors in documentation.
  • Removal of Jupyter Checks: Cleaned up unnecessary checks for Jupyter notebooks in checks.py.
  • Benchmark Visualization: Introduced interactive benchmark graphs in benchmark.md allowing dynamic model comparison through selectable checkboxes.
  • Export Compatibility: Added checks to skip MNN export tests on Raspberry Pi and NVIDIA Jetson due to known unsupported issues.

🎯 Purpose & Impact

  • 🔧 Enhanced Training Robustness: By setting a default epoch, training processes are safeguarded against accidental misconfiguration, ensuring a more reliable setup for users.
  • 🏷️ Better Attribution: Updating author profiles ensures that contributors receive the correct recognition, improving engagement and transparency in the community.
  • 🛠️ Clean Codebase: Removing obsolete Jupyter checks streamlines the code, making the library more efficient and easier to maintain.
  • 📊 User-Friendly Benchmarking: The addition of visual tools for model comparison makes it easier for users to evaluate performance metrics, aiding in informed decision-making.
  • 🛡️ Compatibility Safeguards: Preventing exports to MNN format on unsupported devices avoids potential errors and enhances user experience by providing clear usage boundaries.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.26...v8.3.27

v8.3.26: - ultralytics 8.3.26 EdgeTPU Pose models fix (#​17281)

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🌟 Summary

The release of Ultralytics version 8.3.26 focuses on enhancing performance for pose estimation tasks, improving export functionality for various formats, and incorporating user-centric updates for ease of use.

📊 Key Changes

  • Pose Task Enhancements: Improved scaling for pose coordinates to boost accuracy in pose estimation tasks.
  • Export Improvements: Enhanced export support, including numerical stability for TFLite and EdgeTPU, and formatting fixes for NCNN.
  • Documentation Updates: Revised default models in example files and documentation for clarity and accuracy.
  • Export Order Fix: Adjusted test order for MNN and NCNN formats to avoid CI errors on Windows systems.
  • Case-insensitive Optimizers: Made optimizer selection process case-insensitive.
  • Auto Annotation Customization: Added new parameters (confidence, IoU, image size) for more flexible image auto-annotation.

🎯 Purpose & Impact

  • Enhanced Precision: Improved pose estimation allows more accurate tracking of movements, crucial for applications in sports analysis, healthcare, and more.
  • Expanded Versatility: With better export support, models can be used on more diverse hardware platforms seamlessly.
  • User Experience: Documentation and usability updates help users gain quicker, more straightforward access to advanced features and models.
  • Platform Stability: Ordering tests correctly means smoother application durability, especially for Windows users running CI processes.
  • Simplified Workflow: Case-insensitive optimizer names and auto-annotation parameter flexibility reduce potential friction for users, promoting a more efficient development process.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.25...v8.3.26

v8.3.25: - ultralytics 8.3.25 Alibaba MNN export and predict support (#​16802)

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🌟 Summary

The v8.3.25 release of Ultralytics brings a major update with the introduction of support for the Alibaba MNN (Mobile Neural Network) format, enabling better deployment of YOLO models on mobile and embedded systems.

📊 Key Changes

  • Alibaba MNN Support: Added the ability to export and predict with YOLO models in the MNN format.
  • Integration Updates: Documentation and integration guides have been enhanced to include MNN details.
  • Improved ONNX Runtime: The ONNX Runtime has been optimized for faster inference, reducing overheads and improving performance, allowing for efficient handling of dynamic and static shape inferences.
  • Tracking Improvements: Lowered default confidence thresholds for trackers to align with detection predictions, facilitating better tracking performance.

🎯 Purpose & Impact

  • Enhanced Mobile Deployment: With MNN support, users can deploy models with high efficiency on mobile and ARM devices, allowing for more versatile applications.
  • Performance Optimization: Faster ONNX inference ensures reduced runtime and improved speed, which is particularly beneficial for applications requiring real-time processing.
  • User-Friendly: Updated thresholds for tracking models now provide more intuitive operations, aligning the outputs of tracking systems with user expectations.
  • Streamlined Updates: Improved configuration and integration steps reduce friction during the setup of modern AI workflows, enhancing overall usability and user satisfaction.

What's Changed

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@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from ccc3f77 to bac03f7 Compare October 12, 2024 19:56
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.10 Update dependency ultralytics to v8.3.11 Oct 12, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from bac03f7 to 2573bc2 Compare October 13, 2024 16:00
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.11 Update dependency ultralytics to v8.3.12 Oct 13, 2024
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@renovate renovate bot changed the title Update dependency ultralytics to v8.3.12 Update dependency ultralytics to v8.3.13 Oct 14, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 0bb20af to 7b6844e Compare October 16, 2024 14:08
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.13 Update dependency ultralytics to v8.3.14 Oct 16, 2024
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@renovate renovate bot changed the title Update dependency ultralytics to v8.3.17 Update dependency ultralytics to v8.3.18 Oct 19, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 20b24fa to b5892f3 Compare October 22, 2024 01:51
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.18 Update dependency ultralytics to v8.3.19 Oct 22, 2024
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@renovate renovate bot changed the title Update dependency ultralytics to v8.3.22 Update dependency ultralytics to v8.3.23 Oct 25, 2024
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@renovate renovate bot changed the title Update dependency ultralytics to v8.3.23 Update dependency ultralytics to v8.3.24 Oct 28, 2024
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@renovate renovate bot changed the title Update dependency ultralytics to v8.3.24 Update dependency ultralytics to v8.3.25 Oct 30, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 8484986 to afb3e17 Compare October 31, 2024 13:53
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.25 Update dependency ultralytics to v8.3.26 Oct 31, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from afb3e17 to a3275c5 Compare November 1, 2024 04:32
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.26 Update dependency ultralytics to v8.3.27 Nov 1, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from a3275c5 to 61592be Compare November 7, 2024 01:47
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.27 Update dependency ultralytics to v8.3.28 Nov 7, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 61592be to 0a2f3e7 Compare November 11, 2024 17:50
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.28 Update dependency ultralytics to v8.3.29 Nov 11, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 0a2f3e7 to 9773ddf Compare November 13, 2024 21:02
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.29 Update dependency ultralytics to v8.3.30 Nov 13, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 9773ddf to b433f53 Compare November 14, 2024 00:19
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.30 Update dependency ultralytics to v8.3.31 Nov 14, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from b433f53 to f9097f0 Compare November 16, 2024 21:32
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.31 Update dependency ultralytics to v8.3.32 Nov 16, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from f9097f0 to 6082496 Compare November 18, 2024 19:29
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.32 Update dependency ultralytics to v8.3.33 Nov 18, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 6082496 to 1d484c4 Compare November 19, 2024 14:17
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.33 Update dependency ultralytics to v8.3.34 Nov 19, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 1d484c4 to 2ad5d6e Compare November 21, 2024 14:52
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.34 Update dependency ultralytics to v8.3.35 Nov 21, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 2ad5d6e to 5e4115c Compare November 22, 2024 11:58
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.35 Update dependency ultralytics to v8.3.36 Nov 22, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 5e4115c to 50ccb7f Compare November 25, 2024 11:27
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.36 Update dependency ultralytics to v8.3.37 Nov 25, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 50ccb7f to 0f78849 Compare November 26, 2024 11:50
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.37 Update dependency ultralytics to v8.3.38 Nov 26, 2024
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