Ultralytics

🌟 Summary
Ultralytics has released version 8.2.43, bringing in improvements and new additions mostly focused on predictive functionality and innovative ways to process and display predictions.

📊 Key Changes
- **Install Requirements Update**: Added `pytest-cov` to the pip install command.
- **Prediction Enhancements**:
- Added augment settings (`save_txt`, `save_crop`, `augment`) in model prediction.
- Improved handling of augmented predictions and warnings for unsupported configurations.
- **Non-Max Suppression**: Introduced `rotated` parameter for Oriented Bounding Boxes (OBB).
- **Mask Processing**: Adjusted threshold for mask processing from `0.5` to `0.0`.
- **Label Positioning**:
- Enhanced text label fitting and positioning to ensure labels don't overlap or extend beyond image boundaries.

🎯 Purpose & Impact
- **More Robust Testing**: Including `pytest-cov` enhances code testing with better coverage reporting, leading to more reliable software.
- **Enhanced Prediction Flexibility**: New augment settings in `model.predict` facilitate more detailed and varied output, aiding in diverse application scenarios.
- ** Warning Visibility**: Clearer warning messages provide better understanding about what functionalities are or aren't supported, reducing user confusion.
- **Optimized Non-Max Suppression**: The `rotated` parameter enables better handling of complex bounding boxes, extending model applicability.
- **Accurate Mask Processing**: Adjusting the mask threshold ensures all relevant masks are considered, improving accuracy in image segmentation tasks.
- **Clearer Visualization**: Improvements to label positioning prevent overlaps and ensure that labels are displayed properly, making visual outputs more readable and professional.

These updates collectively enhance the usability, accuracy, and robustness of the Ultralytics software, benefiting both developers and end-users. 🚀🔍

🌟 Summary
Release `v8.2.42` of Ultralytics brings several critical updates to enhance the performance, security, and flexibility of the YOLO models, including Dockerfile improvements, support for OpenVINO C++ inference, and improved package dependencies.


📊 Key Changes
- **Dockerfile Updates**:
- PyTorch CUDA version updated from 2.2.2 to 2.3.1.
- Package installation improvements, switching `nvidia-tensorrt` to `tensorrt`.
- Added `unzip` package for better compatibility.
- **YOLOv10 Documentation**:
- Enhanced examples with Python and CLI commands for better usability.
- **OpenVINO Integration**:
- Added support for YOLOv8 inference in C++ using OpenVINO and OpenCV APIs, complete with build instructions and usage examples.
- **Dependency Management**:
- Updated dependency checks and installations to ensure compatibility with the latest packages.
- Improved handling of TensorRT and other dependencies in different environments.
- **Bug Fixes & Enhancements**:
- Fixes for the loss function computation to handle bounding boxes correctly.

🎯 Purpose & Impact
- **Improved Performance**:
- Upgrading to PyTorch 2.3.1 with updated packages will leverage the latest performance and security enhancements.
- **Better Compatibility**:
- Adding the `unzip` package and refining package installations improves overall compatibility and setup processes on various systems.
- **Developer Flexibility**:
- The addition of the YOLOv8 OpenVINO C++ examples allows developers to integrate and leverage powerful YOLO models in their C++ projects, offering more flexibility and performance tuning options.
- **Enhanced Usability**:
- Detailed documentation for running YOLOv10 models via Python and CLI facilitates ease of use for both new and experienced users.
- **Security and Stability**:
- Ensuring that all packages are up-to-date reduces the risk of vulnerabilities and promotes more stable deployments.

This release is a significant step towards making the YOLO models more robust, easier to use, and performant in various environments. 🚀

What's Changed
* Add YOLOv8 OpenVINO C++ Inference example by rlggyp in https://github.com/ultralytics/ultralytics/pull/13839
* Ultralytics TensorRT10 update by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/13933
* Dockerfile FROM `pytorch/pytorch:2.3.1-cuda12.1-cudnn8-runtime` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/13937
* Add CLI commands for `predict` and `train` YOLOv10 models. by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/13940
* `ultralytics 8.2.42` NVIDIA TensorRT 10 default by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/13943

New Contributors
* rlggyp made their first contribution in https://github.com/ultralytics/ultralytics/pull/13839

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.2.41...v8.2.42

🌟 Summary
Ultralytics v8.2.41 brings several optimizations and dependency updates, ensuring smoother performance and better compatibility.

📊 Key Changes
- Updated documentation images for better clarity and relevance.
- Adjusted `numpy` dependency to support versions `>=1.23.5, <2.0.0`.
- Removed some optional development dependencies (e.g., `check-manifest`, `pre-commit`).
- Updated `hub-sdk` dependency to `>=0.0.8` for improved Ultralytics HUB model integration.
- Added `torch_distributed_zero_first` decorator in training to enhance distributed training by avoiding duplicate dataset downloads.
- Improved session creation logic in `HUBTrainingSession` for better authentication handling and user guidance.

🎯 Purpose & Impact

- **Enhanced Documentation**: 📚 The updated images in the ROS Quickstart guide make it easier for users to follow along with clear visuals.
- **Expanded Compatibility**: 🔄 By tweaking the `numpy` dependency range, users experience fewer compatibility issues, ensuring the software runs smoothly across more environments.
- **Streamlined Development Environment**: 🛠️ The removal of certain optional development dependencies helps to simplify the development setup, reducing potential setup issues for developers.
- **Improved Model Integration**: 🧩 The updated `hub-sdk` version and authentication improvements ensure that users have a smoother experience when interacting with Ultralytics HUB models, boosting overall productivity.
- **Optimized Distributed Training**: 🚀 The addition of the `torch_distributed_zero_first` decorator helps prevent redundant dataset downloads during distributed training, saving time and resources.
- **Better User Guidance**: 🔍 Enhanced session creation logic provides clearer warnings and instructions, ensuring that users can effectively resolve authentication issues with Ultralytics HUB.

These updates collectively ensure more efficient, stable, and user-friendly performance for both developers and non-expert users working with Ultralytics projects.

What's Changed
* Adjust `numpy<2.0.0` compatibility by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/13906
* Fix HUB session with DDP training by Laughing-q in https://github.com/ultralytics/ultralytics/pull/13103
* Compress ROS Guide Images by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/13914
* `ultralytics 8.2.41` fix HUB unzip subdirectory bug by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/13913


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.2.40...v8.2.41

🌟 Summary
The v8.2.40 release of Ultralytics includes improvements and fixes aimed at enhancing the workflow for model training and documentation processes.

📊 Key Changes
- **GitHub Actions Workflow:** Added `git pull` command to ensure the latest branch state before making changes.
- **Documentation Updates:** Minor spelling corrections and new callback documentation.
- **Model and HUB Session Updates:** Refined session management in `HUBTrainingSession` and imported it directly in `model.py`.

🎯 Purpose & Impact
- **Smoother CI Process:** By pulling the latest branch state before committing, the continuous integration (CI) setup becomes more reliable, minimizing the risk of conflicts.
- **Enhanced Documentation:** Correction of spelling and addition of missing callbacks ensures that the documentation is more accurate and comprehensive for users.
- **Improved HUB Interactions:** The refactoring of `HUBTrainingSession` simplifies code, ensures robust session creation, and addresses possible issues with unauthenticated sessions, which should make the training process on the Ultralytics HUB more seamless and error-free.

These changes collectively aim to enhance the user experience by making the system more reliable and easier to use, especially for model training and collaboration on the Ultralytics HUB. 🚀

What's Changed
* `ultralytics 8.2.40` refactor HUB code into callbacks by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/13896


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.2.39...v8.2.40

🌟 Summary (v8.2.39 Release)
The latest release `v8.2.39` from Ultralytics includes optimizations and updates across various files to enhance the overall functionality and performance.

📊 Key Changes
- **Documentation Updates:**
- Updated links across the documentation to use consistent URLs, replacing shortened links with full URLs for better readability and maintenance.
- Added embedded YouTube video to the SAHI documentation for enhanced user guidance.

- **Code Optimization and Fixes:**
- Adjusted layer configurations in `yolov8-p6.yaml` for more detailed memory and parameter utilization.
- Refined data augmentation and normalization operations for classification tasks in `augment.py`.
- Simplified code structure by replacing multiple `+=` operations with `append()` for clarity and performance.
- Updated `exporter.py` dependencies for ONNX, ensuring compatibility with the latest versions and enhancements in export capabilities.
- Improved segmentation handling and label generation in `converter.py`.
- Enhanced model configuration handling for ROS integration and temporary module management.

🎯 Purpose & Impact
- **Improved Documentation:**
- Ensures that users have direct and clear access to resources and guidance, enhancing overall user experience.
- Video content helps users better understand complex processes visually.

- **Enhanced Model Configuration and Exports:**
- Updated dependencies and configuration adjustments ensure that models utilize resources efficiently, improving performance metrics like GFLOPs and parameter counts.
- Optimized augmentation routines and classification tasks lead to faster and more accurate model training and predictions.

- **Code Maintenance and Clarity:**
- Codebase improvements, such as using `append()` over `+=`, make the code easier to read and maintain.
- Better structure and handling of transformations and temporary modules prevent potential runtime errors and streamline development.

These updates collectively ensure that the Ultralytics framework remains robust, user-friendly, and optimal for various AI and machine learning applications. 🚀

What's Changed
* Code refactor https://ultralytics.com/actions by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/13844
* ROS Quickstart, fixed code formatting by ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/13855
* Replace `+=` with faster list `.append()` by Kayzwer in https://github.com/ultralytics/ultralytics/pull/13849
* Fix ambiguous variable names by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/13864
* Add https://youtu.be/tq3FU_QczxE to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/13867
* Update yolov8-p6.yaml with model parameters and GFLOPs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/13862
* Fix HUB link https://ultralytics.com/hub by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/13884
* `ultralytics 8.2.39` update `onnxslim>=0.1.31` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/13883


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.2.38...v8.2.39

🌟 Summary
Release `v8.2.38` introduces YOLOv10 models to the Ultralytics package, alongside enhancements and bug fixes.

📊 Key Changes
- **Benchmarking YOLOv10 Models:** Added benchmarks for YOLOv10 models.
- **YOLOv10 Documentation:** Detailed addition of YOLOv10 architecture and usage examples.
- **YOLOv10 Support:** Added YOLOv10 configurations (`.yaml` files) for different model sizes including YOLOv10n, YOLOv10s, YOLOv10m, YOLOv10l, and YOLOv10x.
- **New Modules:** Introduced new neural network modules (e.g., `RepVGGDW`, `CIB`, `C2fCIB`, `Attention`, `PSA`, `SCDown`).
- **End-to-End Detect (E2EDetect) Loss:** Added a new loss function for end-to-end detection.
- **Extended Model Exports:** Updated exporter configurations and limitations for new YOLOv10 operations.
- **Bug Fixes & Optimizations:** Addressed various bugs and performance enhancements (e.g., support for different export formats).

🎯 Purpose & Impact
- **Improved Object Detection:** The introduction of YOLOv10 models ensures optimized real-time object detection with high accuracy and low computational cost, beneficial for both current and future applications.
- **Enhanced Flexibility:** The addition of new modules and configurations allows users to tailor their models and training pipelines more precisely according to their needs.
- **Better Performance:** Benchmarking enhancements and end-to-end loss integration ensure more efficient and effective training and inference.
- **Comprehensive Documentation:** Detailed YOLOv10 documentation facilitates easier adoption and understanding for both new and existing users.
- **Expanded Export Options:** While not all formats are currently supported, the expanded export options provide more opportunities to deploy models across different platforms efficiently.

🚀 Next Steps
- Users are encouraged to explore the new YOLOv10 models and configurations for enhanced detection capabilities.
- Refer to the updated documentation for detailed guidance on utilizing the new features and modules effectively.

What's Changed
* `ultralytics 8.2.38` official YOLOv10 support by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/13113


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.2.37...v8.2.38