Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Update dependency ultralytics to v8.3.59 #124

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Update dependency ultralytics to v8.3.59 #124

wants to merge 1 commit into from
+4 −4

Conversation

renovate[bot]
Copy link
Contributor

@renovate renovate bot commented Dec 16, 2024 •
edited
Loading

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
ultralytics (changelog) 8.3.49 -> 8.3.59 age adoption passing confidence

Release Notes

ultralytics/ultralytics (ultralytics)

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

Compare Source

🌟 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

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.58...v8.3.59

v8.3.58: - ultralytics 8.3.58 Use uint8 type for TensorRT Profile (#​18327)

Compare Source

🌟 Summary

The v8.3.58 release introduces an update to the YOLO model benchmarking utility for TensorRT, documentation enhancements, and new features to improve usability and performance for developers and users. 🚀🛠️

📊 Key Changes

  • TensorRT Model Benchmarking Improvement:
    • Updated benchmarking to use uint8 (integer) input data instead of float32 (decimals) for classification tasks, reflecting real-world input formats.
  • Documentation Enhancements:
    • Embedded instructional videos in object counting and model exporting guides for clarity. 🎥
    • Updated integration documentation for YOLO11, replacing mentions of YOLOv8.
  • New Training Argument:
    • Added multi_scale training option in documentation for dynamic image sizes during training. 🌈
  • Docker Optimization:
    • Added a .dockerignore file to exclude unnecessary files, improving build efficiency and security.

🎯 Purpose & Impact

  • Purpose:

    • Optimize benchmarking processes and align input data with typical formats for more accurate performance evaluations during TensorRT model testing.
    • Enhance usability through better instructional resources and accurate documentation for new model versions.
    • Introduce dynamic training options to improve model adaptability for various image sizes.
    • Improve Docker image builds by reducing clutter and improving security. 🔒

  • Impact:

    • TensorRT users will benefit from faster and more realistic benchmarks for classification tasks. 🏎️
    • Documentation updates simplify learning and onboarding for both new and advanced users. 📘
    • Developers now have the option to train models across multiple resolutions, potentially enhancing inference accuracy.
    • Docker environments become leaner and more secure, supporting cleaner deployments. 🐋

This release is an essential step forward for developers seeking both practical performance boosts and improved clarity in documentation! 💡

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.57...v8.3.58

v8.3.57: - ultralytics 8.3.57 Support is_jetson() and is_raspberrypi() in Docker images (#​18449)

Compare Source

🌟 Summary

The v8.3.57 release includes improved hardware and platform detection in Docker containers, new image annotation visualization utilities, stricter argument validation for export functions, and enhanced documentation.

📊 Key Changes

  • 🔧 Hardware Detection Fix for Docker: Extended platform detection capabilities by supporting is_jetson() and is_raspberrypi() inside Docker environments without requiring risky privileged mode.
  • 🖼️ Annotation Visualization Function: Introduced visualize_image_annotations for previewing YOLO bounding boxes and labels over images pre-training.
  • 🚀 Export Enhancements: Improved validation for model export arguments, refined metadata generation, and updated TensorFlow compatibility with onnx2tf.
  • 🗂️ Documentation Tweaks:
    • Embedded video tutorials directly into guides to simplify learning.
    • Enhanced dataset explorer and SKU-110k dataset documentation for better clarity.
    • Adjusted page navigation in solution docs for easier access.

🎯 Purpose & Impact

  • 🔍 Enable Safe GPU Deployments: The new device detection enhances NVIDIA Jetson and Raspberry Pi compatibility when working in Docker, without compromising security.
  • 🎨 Dataset Quality Checks: Users can now visually verify and correct dataset annotations before model training, reducing training inaccuracies caused by bad labels.
  • ⚙️ Smooth Export Workflow: Stricter checks and dependency updates ensure users face fewer issues when exporting models across formats and environments.
  • 🎥 Improved Usability: Embedded tutorials directly in the docs empower both new and experienced users with visual, hands-on learning resources.
  • 🗃️ Streamlined Navigation: Re-organized documentation improves discoverability of solutions and resources.

This release not only focuses on hardware compatibility improvements but also empowers users with tools to refine projects and workflows efficiently while offering an enhanced user experience! 🚀✨

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.56...v8.3.57

v8.3.56: - ultralytics 8.3.56 PaddlePaddle GPU Inference support (#​18468)

Compare Source

🌟 Summary

The v8.3.56 release introduces GPU support for PaddlePaddle inference and export, along with several key bug fixes, usability enhancements, and documentation updates. 🚀

📊 Key Changes

  1. PaddlePaddle GPU Inference:

    • Added GPU support for PaddlePaddle inference by dynamically checking CUDA availability.
    • Improved compatibility in PaddlePaddle dataloader handling.

  2. UTF-8 Bug Fix:

    • Resolved encoding issues in convert_coco when dealing with non-UTF-8 annotation files.

  3. Dataset Annotation Optimizations:

    • Improved performance and speed for large annotations in the GroundingDataset class.

  4. Export Enhancements:

    • OpenVINO INT8 Export Fix: Resolved an OpenVINO export error by resetting clip_model during export.
    • IMX Export Clarification: Limited IMX export support exclusively to YOLOv8n models.
    • ONNX2TF Compatibility: Updated compatibility with onnx2tf library (v1.26.3), fixing memory and bloated file issues.

  5. Documentation Improvements:

    • Deprecated Jupyter documentation in favor of a markdown-based structure (e.g., explorer.md).
    • Simplified NVIDIA Jetson setup steps by adding streamlined installation commands for PyTorch and Torchvision.
    • Added new guides for thread-safe inference and ROS integration for robotics applications.

  6. Minor Model Updates:

    • Clarified YOLOv6 configuration by emphasizing YAML-based model definitions instead of weight files.

🎯 Purpose & Impact

  • Improved Compatibility: Seamless PaddlePaddle operations on GPU ensure better flexibility for diverse hardware setups. 🖥️⚡
  • Faster Annotations: Accelerated handling of large datasets benefits advanced AI workflows. 🕒✨
  • Enhanced Export Reliability: Fixes to export pipelines (OpenVINO, IMX, ONNX2TF) ensure robust and error-free deployment. 📦✅
  • Accessible Learning Resources: Documentation enrichments support a smoother onboarding of new users and integrations in fields like robotics. 📚🤖
  • Streamlined User Experience: Simplified installation and setup processes save time and reduce confusion for developers. 🛠️🎉

This release continues to refine functionality and usability for both developers and users across varied use cases.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.55...v8.3.56

v8.3.55: - ultralytics 8.3.55 New Medical-pills dataset (#​18389)

Compare Source

🌟 Summary

The v8.3.55 release of Ultralytics YOLO introduces a new dataset, Medical Pills Detection Dataset, aimed at advancing AI applications in pharmaceutical automation, alongside several feature enhancements, bug fixes, and documentation improvements. 💊💻✨

📊 Key Changes
  • New Dataset Added: Medical Pills with 92 training and 23 validation images. 🩺
  • Enhanced auto_annotate Documentation: Centralized details of YOLO-SAM integration for creating segmentation datasets. 📖
  • Fixed ConfusionMatrix: Corrected FP calculation logic for unmatched predictions. 🛠️
  • User-Friendly Updates: Improved workflow cloning speeds and UI components for solutions workflows. 🚀
  • Code Quality Upgrades: Type hinting for better flexibility, Python 3.12 support tweaks, and bug fixes. ⚙️
🎯 Purpose & Impact

  • Purpose:

    • Enable automation in pharmaceutical workflows, e.g., pill quality control and sorting.
    • Provide clearer usage examples for dataset annotation via YOLO-SAM tools.
    • Refine existing tools with a developer-friendly codebase.

  • Impact:

    • Improved AI Training: Medical innovators can train models for specific industries using the new dataset.
    • Documentation Clarity: Ease of adoption for advanced features like hybrid YOLO-SAM workflows.
    • Bug Fixes: These ensure more accurate predictions (e.g., ConfusionMatrix FP fix) and reduce user-errors in workflows.
    • Streamlined DevOps: Faster docs deployment and CI pipelines benefit larger teams.

🚀 This release is a forward leap for developers and researchers aiming to innovate in specialized fields like healthcare!

What's Changed
New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.54...v8.3.55

v8.3.54: - ultralytics 8.3.54 New Streamlit inference Solution (#​18316)

Compare Source

🌟 Summary

Ultralytics v8.3.54 delivers a significant overhaul in the Streamlit-based real-time inference solution, making it easier for users to perform live predictions with a better interface. It also introduces enhancements around exporting flexibility for OpenVINO models, updates to documentation for YOLO11 use, and streamlines development and compatibility workflows.

📊 Key Changes
  • 🚀 Revamped Streamlit Inference Tool: Streamlit apps now feature an all-new Inference class.
    • Sidebar for quick video source, model selection, and settings like confidence thresholds.
    • Support for webcam and video uploads for real-time predictions and visualizations.
    • Enhanced interactivity with class selection, live FPS monitoring, and tracking features.
  • 📦 OpenVINO Export Enhancements:
    • Added support for dynamic shapes, expanding deployment flexibility.
    • Unified argument ordering (batch, dynamic, etc.) across multiple export formats.
  • 📖 YOLO11 Documentation Updates: Updated guides to reflect the latest YOLO11 usage in region counting.
  • 🐍 Python Workflow Updates: Minimum Python version for CI workflows updated to 3.9 for compatibility alignment.
  • 🌐 ONNXRuntime Example for RTDETR:
    • Added an example for deploying RTDETR models with ONNXRuntime in Python.
  • ⚙️ Dependency Updates: Updated GitHub Actions setup-uv workflow to v5 to improve caching and build processes.
🎯 Purpose & Impact
  • Better User Experience with Streamlit:
    • Easier navigation and configuration for real-time inference tasks. 🖥️
    • Developers and beginners alike can now perform live inference with minimal setup.
  • Deployment Flexibility: Support for dynamic OpenVINO exports ensures models work smoothly across various scenarios and hardware configurations. 🧩
  • Clearer Documentation: The shift to YOLO11 references builds clarity and trust for users working with region-based object counting. 📘
  • Future-Proofing Development:
    • Updating Python versions ensures long-term ecosystem compatibility. 🔧
  • ONNXRuntime Examples: Simplifies adopting RTDETR models for developers using ONNXRuntime in Python, with clear setup and usage guidance. 🚀
  • Faster CI/CD Pipelines: Updated dependencies in GitHub workflows boost speed and efficiency. ⚡

This release is ideal for users looking for a blend of usability in inference workflows and robustness in model deployment workflows! 🌟

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.53...v8.3.54

v8.3.53: - ultralytics 8.3.53 New Export argument validation (#​18185)

Compare Source

🌟 Summary

The v8.3.53 release introduces enhanced argument validation during model export to improve error handling and reduce user confusion, alongside other updates focusing on Dockerfile improvements for NVIDIA Jetson devices and internal code enhancements. 🚀

📊 Key Changes

Primary Feature: Enhanced Export Argument Validation
  • ✅ Introduced a mechanism to check whether export arguments are valid for specific formats (e.g., ONNX, TensorRT).
  • 🚫 Previously unsupported or incompatible arguments (e.g., int8 without required calibration data) will now raise clear errors.
Other Updates:
  • 🔧 JetPack Dockerfile Enhancements
    • JetPack 5: Updated base image, streamlined dependencies, and improved TensorRT compatibility.
    • JetPack 6: Removed unnecessary ONNX Runtime GPU package references for cleaner setup.
  • 🛠️ Improved settings.update() Validation: Ensures proper handling of input types and keys for user settings.
  • 🧹 Code Cleanup: Improved internal structures such as string representations for configuration objects (JSONDict) and URL handling (clean_url), improving performance and readability.

🎯 Purpose & Impact

  • Export Validation Improvements

    • 🚀 Provides users with immediate feedback on invalid export configurations.
    • 💪 Reduces confusion by preventing potentially misleading silent failures during export.
    • 🛡️ Ensures more reliable model deployment by enforcing compatibility checks early.

  • Jetson Dockerfile Updates

    • 🖥️ Increased compatibility with updated JetPack versions for NVIDIA Jetson devices.
    • 🔨 Streamlined setup for AI model training and deployment with YOLO on Jetsons.

  • User-Friendly Enhancements

    • 💡 Easier troubleshooting with clearer error messages for user settings and export configurations.
    • 📜 Simpler and more maintainable project codebase with reduced clutter in utilities and configuration processing.

This release strongly benefits both developers configuring their models for export and users building YOLO models on NVIDIA platforms, ensuring smoother workflows and better system compatibility. 🚦

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.52...v8.3.53

v8.3.52: - ultralytics 8.3.52 AutoBatch CUDA computation improvements (#​18291)

Compare Source

🌟 Summary

Version 8.3.52 focuses on enhanced CUDA memory management for improved performance, with additional updates to documentation, compatibility for NVIDIA Jetson devices, and refined functionality for YOLO models. 🚀

📊 Key Changes

  • 🚀 New cuda_memory_usage Utility: Introduced a tool for dynamic monitoring and management of CUDA memory during operations.
  • 💡 Improved Model Profiling: Integrated memory tracking into the profiling process to report GPU memory usage alongside performance stats.
  • 🔄 Enhanced Object Segmentation: Modified segment2box for precise bounding box calculations when segments extend beyond the image boundaries.
  • 📦 JetPack 6.1 Dockerfile Update: Added compatibility for NVIDIA Jetson Orin Nano Super Developer Kit with dependency upgrades and performance benchmarks.
  • 📖 Richer Documentation: Added a CIFAR-100 tutorial video, improved clarity on scale parameter for multiscale training, and updated ROS and NVIDIA Jetson guides.
  • 🧹 TFLite Example Cleanup: Removed unnecessary RGB-to-BGR conversions for simpler and clearer example usage.

🎯 Purpose & Impact

  • 🚀 Enhanced Performance: The cuda_memory_usage utility ensures more efficient GPU memory handling, reducing the risk of out-of-memory crashes during complex operations.
  • 📈 Model Optimization: Developers get richer profiling insights, aiding faster debugging and improving training/production readiness.
  • 🖼️ Robust Object Detection: Improved segmentation functionality provides accuracy even with challenging edge cases, making models more reliable.
  • 🤖 Wider Compatibility: Updating to JetPack 6.1 enables users to fully leverage NVIDIA Jetson’s latest hardware advancements (e.g., Orin Nano Super’s 67 TOPS).
  • 📚 Simplified Learning: Documentation improvements, including engaging tutorials and clarified parameters, lower the barrier to entry for both beginners and experts.
  • 🧑‍💻 Beginner-Friendly Examples: Streamlined TFLite examples ensure ease of adoption for new developers.

This release delivers meaningful improvements for developers working across GPU-heavy tasks, embedded systems, and edge AI deployments! 🚀

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.51...v8.3.52

v8.3.51: - ultralytics 8.3.51 AutoBach logspace fit and checks (#​18283)

Compare Source

🌟 Summary

The Ultralytics v8.3.51 release introduces improved robustness for training batch size optimization, documentation enhancements, new features like a security alarm system, and updates to facilitate the transition from YOLOv8 to YOLO11. 🚀

📊 Key Changes

  • Improved Batch Size Calculation:
    • Automated batch size determination now uses logarithmic polynomial fitting for better accuracy. 🧮
    • Stricter checks ensure safe memory usage and prevent crashes due to misconfigurations. ✅
  • Hyperparameter Tuning:
    • Added default hyperparameter search spaces and clear examples in documentation for easier customization. 🛠️
    • Updated training process to improve reliability by using shell=True for subprocess execution. ⚙️
  • YOLO11 Integration:
    • Updated examples, references, and documentation to reflect the transition from YOLOv8 to YOLO11. 📚
    • Enhanced support for SAHI (Slicing Aided Hyper Inference) with YOLO11 models.
  • New Security Alarm System:
    • Added a ready-to-use, customizable security alarm system solution leveraging YOLO11. Includes email alerts when detections exceed thresholds. 🛡️
  • Expanded Export Options:
    • New formats supported, including MNN and Sony IMX500, enhancing deployment flexibility for diverse platforms. 🎉

🎯 Purpose & Impact

  • Optimized Performance:
    • The refined autobatch method improves training stability and GPU utilization across various devices, helping users achieve smoother workflows.
  • Enhanced Usability:
    • New documentation simplifies hyperparameter tuning for beginners and advanced users alike, reducing the learning curve.
    • Updates to SAHI and model examples make it easier to adopt YOLO11.
  • Greater Flexibility:
    • Broader export options and integration tools expand YOLO's adaptability for edge devices like IMX500.
  • Real-World Applications:
    • With the newly added Security Alarm System, users gain a powerful, practical monitoring tool ready for deployment in surveillance scenarios. 🚨

This release elevates Ultralytics by streamlining processes, expanding use cases, and improving reliability for developers and organizations. ⭐

What's Changed

Full Changelog: ultralytics/ultralytics@v8.3.50...v8.3.51

v8.3.50: - ultralytics 8.3.50 Enhanced segment resample (#​18171)

Compare Source

🌟 Summary

Release v8.3.50 introduces improvements to segment resampling logic, enhanced model handling during training and validation, documentation updates, and bug fixes across multiple areas for increased flexibility, accuracy, and usability. 🚀📘

📊 Key Changes

  • Enhanced Segment Handling:
    • Segment resampling now dynamically adjusts the number of points based on the longest segment for better consistency. 🖌️
    • Empty segments during concatenation are gracefully handled to avoid errors.
  • Improved Validation & Model Workflow:
    • Validation callbacks for OBB models now work correctly during training. 🔄
    • Updates to fix validation warnings when using untrained model YAMLs.
  • Model Saving Updates:
    • Improved checkpoint handling when saving models to reduce initialization errors. 💾
  • Documentation Tweaks:
    • Added multimedia content (audio & video) to YOLO11 documentation for a richer learning experience. 🎧🎥
    • Cleaned up outdated entries (like the Sony IMX500) and enhanced clarity with new formatting and annotated argument types.
    • Internal docs configuration now supports cleaner URLs and auto-deployment enhancements. 🌐
  • Bug Fixes:
    • Fixed CUDA-related bugs in the SAM module for more consistent device handling. 🛠️
    • Adjustments to prevent crashes in scenarios with mixed device usage.

🎯 Purpose & Impact

  • ✅ Reliability Boost: The improved resampling logic ensures stable training and avoids breaking workflows when handling variable-length segments.
  • 📈 Performance Optimization: Better checkpoint and validation handling streamlines user workflows and minimizes potential runtime errors.
  • 🌍 Usability Improvements: Updated Docs and multimedia resources make discovering and using features more user-friendly for both beginners and experts.
  • 🚀 Cross-Device Consistency: Fixes in CUDA logic ensure model compatibility on both CPU and GPU systems, enhancing accessibility.
  • 🖹 Clean Documentation: Removing outdated content and refining resources helps users focus on the latest tools and avoid confusion.

This update is pivotal for developers and users working with segmentation models, large datasets, or seeking smoother workflows during benchmarking, training, and inference with YOLO models.

What's Changed

New Contributors

Full Changelog: ultralytics/ultralytics@v8.3.49...v8.3.50

Configuration

📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.

  • If you want to rebase/retry this PR, check this box

This PR was generated by Mend Renovate. View the repository job log.

@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 8e7c625 to 60ee997 Compare December 17, 2024 21:08
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.50 Update dependency ultralytics to v8.3.51 Dec 17, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 60ee997 to 874b64a Compare December 20, 2024 13:05
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.51 Update dependency ultralytics to v8.3.52 Dec 20, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 874b64a to 08674f5 Compare December 22, 2024 03:17
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.52 Update dependency ultralytics to v8.3.53 Dec 22, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 08674f5 to ba0bcb8 Compare December 24, 2024 14:09
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.53 Update dependency ultralytics to v8.3.54 Dec 24, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from ba0bcb8 to 484eb7d Compare December 26, 2024 14:22
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.54 Update dependency ultralytics to v8.3.55 Dec 26, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from 484eb7d to dd37bfc Compare December 31, 2024 15:16
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.55 Update dependency ultralytics to v8.3.56 Dec 31, 2024
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from dd37bfc to aac1e32 Compare January 2, 2025 21:27
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.56 Update dependency ultralytics to v8.3.57 Jan 2, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from aac1e32 to a812e1f Compare January 5, 2025 16:59
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.57 Update dependency ultralytics to v8.3.58 Jan 5, 2025
@renovate renovate bot force-pushed the renovate/ultralytics-8.x-lockfile branch from a812e1f to ae1aa49 Compare January 9, 2025 16:27
@renovate renovate bot changed the title Update dependency ultralytics to v8.3.58 Update dependency ultralytics to v8.3.59 Jan 9, 2025
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Reviewers
No reviews
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

Successfully merging this pull request may close these issues.
0 participants