Ultralytics

🌟 Summary
The Ultralytics 8.3.89 release focuses on improving dependency management, enhancing compatibility with NVIDIA Jetson devices, and refining documentation for better usability. 🚀

📊 Key Changes
- **Dependency Management**: Updated `--index-strategy` to `unsafe-best-match` for safer and more reliable package installations.
- **Jetson Compatibility**: Adjusted TensorFlow.js versions for Jetson JetPack 4/5 to ensure seamless compatibility.
- **Documentation Updates**: Standardized code examples across the repository to Python's interactive shell style (`>>>`) for improved clarity and consistency.
- **Stale Workflow Improvements**: Enhanced GitHub workflows to better manage stale pull requests and issues.
- **Version Update**: Incremented the Ultralytics version to 8.3.89.

🎯 Purpose & Impact
- **Enhanced Reliability**: The updated dependency strategy minimizes potential conflicts during package installations, ensuring smoother setups. 🛠️
- **Broader Hardware Support**: Improved TensorFlow.js compatibility ensures better performance on NVIDIA Jetson devices, benefiting edge AI applications. 🤖
- **Developer Experience**: Standardized documentation makes it easier for developers to understand and implement examples, boosting productivity. 📚
- **Efficient Project Management**: Improved stale workflows help maintain a cleaner and more organized repository. 🚀
- **Version Alignment**: The version bump reflects these enhancements, signaling progress and stability.

What's Changed
* Updated stale actions rules by ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/16563
* Align code examples with Google-style by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19496
* `ultralytics 8.3.89` TensorFlow 2.19.0 compatibility updates by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19668


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.88...v8.3.89

🌟 Summary
The `v8.3.88` release of Ultralytics introduces significant enhancements and new features across its solutions, focusing on object detection, segmentation, privacy tools, and advanced analytics. These updates aim to provide more robust, versatile, and user-friendly tools for computer vision tasks.

📊 Key Changes
- **New Solutions Added**:
- **ObjectBlurrer**: Blurs detected objects for privacy protection.
- **ObjectCropper**: Crops and saves detected objects for further analysis.
- **InstanceSegmentation**: Enables segmentation with masks and annotations.
- **VisionEye**: Simulates human observation by mapping objects to a vision anchor point.
- **Enhanced Analytics**:
- Improved support for various chart types (line, pie, bar, area) with better visualization and customization.
- Unified structure for analytics results.
- **Refinements in Object Tracking**:
- Improved handling of bounding boxes and tracking across frames.
- Enhanced region-based counting and queue management.
- **Bug Fixes**:
- Addressed bounding box offset issues in YOLOv8 C++ inference, ensuring accurate object detection and scaling.

🎯 Purpose & Impact
- 🛡️ **Privacy Protection**: The ObjectBlurrer ensures sensitive information in images or videos is obscured, making it ideal for security and compliance use cases.
- ✂️ **Data Preparation**: ObjectCropper simplifies dataset creation by extracting and saving detected objects.
- 🎨 **Advanced Visualization**: InstanceSegmentation and VisionEye provide detailed insights into object relationships and spatial mapping, enhancing analytical capabilities.
- 📈 **Improved Analytics**: The updated analytics tools allow users to derive actionable insights from data with visually appealing and informative charts.
- 🚀 **Enhanced Accuracy**: Fixes in bounding box handling improve detection precision, benefiting applications like autonomous systems and surveillance.

These updates make Ultralytics solutions more powerful and adaptable, catering to a wide range of industries and use cases.

What's Changed
* Fix detection box offset bug in YOLOv8 example model inference results by matriox1003 in https://github.com/ultralytics/ultralytics/pull/19639
* Docs: Update Banner by sergiuwaxmann in https://github.com/ultralytics/ultralytics/pull/19643
* Improved Examples documentation by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19645
* `ultralytics 8.3.88` Solutions refactor and improvements by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18491

New Contributors
* matriox1003 made their first contribution in https://github.com/ultralytics/ultralytics/pull/19639

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.87...v8.3.88

🌟 Summary
The `v8.3.87` release introduces a new `Results.to_html()` method for exporting inference results in HTML format, alongside various usability, compatibility, and documentation improvements.

📊 Key Changes
- **HTML Export for Results**: Added `to_html()` method to convert detection results into a web-friendly HTML format.
- **Documentation Enhancements**: Improved clarity and consistency in method descriptions and added a dedicated page for the COCO128 dataset.
- **Compatibility Updates**:
- Added support for Ubuntu ARM CI runners and removed QEMU emulator for ARM64 Docker builds.
- Restricted OpenVINO dependency to avoid version conflicts.
- **Memory Management**: Optimized GPU memory clearing to trigger only when usage exceeds 90%.
- **Bug Fixes**:
- Resolved bounding box out-of-bounds issues in MNN examples.
- Fixed multi-stream video result saving to avoid overwriting files.
- **Classification Model Updates**: Adjusted FLOPs calculations for classification models to align with the default image size of 224 pixels.
- **Comet Integration**: Added support for logging segmentation annotations in Comet.

🎯 Purpose & Impact
- **Improved Usability**:
- The `to_html()` method simplifies sharing and visualizing inference results in a browser-friendly format.
- Enhanced documentation, including COCO128 details, aids developers in testing and debugging.
- **Broader Compatibility**:
- ARM support ensures better performance and inclusivity for ARM-based systems.
- OpenVINO version constraints prevent compatibility issues.
- **Optimized Performance**:
- Smarter GPU memory management avoids unnecessary slowdowns during training.
- Faster ARM64 Docker builds reduce build times from 8 minutes to 2 minutes.
- **Enhanced Functionality**:
- Segmentation support in Comet improves tracking and visualization for segmentation tasks.
- Bug fixes ensure more reliable and accurate results across various use cases.

What's Changed
* Enable Ubuntu ARM GitHub CI runners by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19596
* Add COCO128 dataset page to docs by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19594
* Fix BBox Out-of-Bounds Issue in MNN Example by jules-ai in https://github.com/ultralytics/ultralytics/pull/19597
* Add extra step for installing `tkinter` for annotator tool by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19534
* Update classification model FLOPs with `imgsz=224` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19593
* Exclude `ort.pyke.io` from link checking by decahedron1 in https://github.com/ultralytics/ultralytics/pull/19554
* Solutions init conditional import by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19589
* Constrain OpenVINO versions to >=2024.0.0,!=2025.0.0 by aleksandr-mokrov in https://github.com/ultralytics/ultralytics/pull/19481
* Fix result saving issue when multi-stream detection by XevenQC in https://github.com/ultralytics/ultralytics/pull/19297
* Remove QEMU emulator in favor of Github-hosted Arm64 Ubuntu runners by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19604
* Add Segmentation support in Comet logging by yaricom in https://github.com/ultralytics/ultralytics/pull/19618
* Revert `shell=True` for `model.tune()` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19625
* Remove parking slots annotator from tests by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19624
* Update docs.yml Ruff fixes by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19626
* `empty_cache()` only if GPU utilization above 90% by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18949
* `ultralytics 8.3.87` New `Results.to_html` method for inference outputs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19161

New Contributors
* decahedron1 made their first contribution in https://github.com/ultralytics/ultralytics/pull/19554
* aleksandr-mokrov made their first contribution in https://github.com/ultralytics/ultralytics/pull/19481
* XevenQC made their first contribution in https://github.com/ultralytics/ultralytics/pull/19297

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.86...v8.3.87

🌟 Summary
The `v8.3.86` release focuses on improving dataset handling, enhancing code consistency, and fixing minor issues to streamline workflows for users working with various datasets and models.

📊 Key Changes
- **Dataset YAML Refactor**: Improved dataset configuration files for better readability and functionality.
- Enhanced autodownload and conversion scripts for datasets like COCO, VOC, ImageNet, and more.
- Unified string formatting to double quotes (`"`) across YAML files.
- Added detailed comments and docstrings for clarity.
- **UTF-8 Encoding Compliance**: Updated all file operations to explicitly use UTF-8 encoding for better cross-platform compatibility.
- **Keypoint Loss Fix**: Adjusted keypoint loss calculations to ensure only relevant dimensions are divided by stride tensors.
- **Documentation Updates**:
- Fixed example code in SAM 2 model documentation.
- Embedded updated YouTube tutorials for YOLO11 training and batch inference.
- **Code Cleanup**: Removed redundant imports and improved path handling using modern Python practices.

🎯 Purpose & Impact
- **Enhanced Dataset Handling**:
- Simplifies dataset preparation with improved autodownload and conversion scripts. 🛠️
- Reduces errors with better file handling and clear documentation. 📚
- **Cross-Platform Compatibility**:
- Ensures consistent behavior across operating systems by enforcing UTF-8 encoding. 🌍
- **Improved Model Accuracy**:
- Fixes in keypoint loss calculations enhance precision in keypoint-based tasks. 🎯
- **User Experience**:
- Updated tutorials and documentation make it easier for users to train and deploy models effectively. 🎥
- **Code Maintainability**:
- Cleaner and more consistent codebase improves readability and reduces potential bugs. 🧹

This release is a quality-of-life update that enhances both backend functionality and user-facing resources, making it easier for developers and researchers to work with Ultralytics tools. 🚀

What's Changed
* Update sam-2.md by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19573
* Remove superfluous LOGGER import from data/converter by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/19577
* Add https://youtu.be/ZN3nRZT7b24 to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19565
* Exclude keypoint visibility from stride division by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19567
* Remove extra import from `cfg/__init__` by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/19576
* Adds open encoding per PEP-597 by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/19578
* Set `dynamic` from metadata by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19560
* `ultralytics 8.3.86` Refactor dataset YAML autodownload scripts by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19579


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.85...v8.3.86

🌟 Summary
This release, `v8.3.85`, focuses on improving TensorRT export functionality and refining ONNX segmentation examples for better performance and usability. 🚀

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📊 Key Changes
TensorRT Updates (Main Focus of Release):
- **Fixed `max_shape` Calculation Bug**: Resolved inconsistent calculations during TensorRT export with non-zero workspace values.
- **Improved Default Behavior**: Ensured workspace settings default to `0` when not specified.

ONNX Segmentation Example Enhancements:
- **Streamlined Preprocessing/Postprocessing**: Simplified the example to use a more efficient and flexible implementation, including key parameters like `iou`, `imgsz`, and `conf`.
- **Unified Confidence Threshold**: Updated ONNX example to align with YOLO conventions for clarity (`conf`, `iou`).
- **Optimized Mask Handling**: Enhanced segmentation mask functionality for better accuracy and resource efficiency.
- **Adaptive Backend Setup**: Integrated GPU support automatically when available, improving ease of use.

---

🎯 Purpose & Impact
- **For TensorRT Users**:
- **Purpose**: Addressed critical bugs in TensorRT export needed for high-performance inference engines.
- **Impact**: Accurate shape calculation ensures smoother deployments of dynamic models with TensorRT, preventing crashes or incorrect behavior. Users exporting YOLO models into `.engine` format, especially with non-zero workspaces, will experience stable exports.

- **For ONNX Developers**:
- **Purpose**: Simplify, optimize, and enhance usability of segmentation examples using ONNX Runtime.
- **Impact**: Developers using ONNX Runtime benefit from faster setup, more intuitive parameter usage, and reliable mask-based object segmentation results.

- **General Improvements**:
- These updates improve the overall developer experience and model deployment across various inference backends, enabling seamless workflows for cutting-edge scenarios.

---

This update primarily strengthens export and inference capabilities 🎯, while making the process more robust for advanced and typical users alike.

What's Changed
* Cleanup and fix ONNX segment example by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19551
* `ultralytics 8.3.85` TensoRT export `max_shape` fix by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19541


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.84...v8.3.85

🌟 Summary
The `v8.3.84` release brings improvements to YOLO's segmentation handling, documentation clarity, and usability, with a focus on filtering invalid outputs and refining user guidance. 🎉

📊 Key Changes
- 🚀 **Segmentation Optimization:** YOLO now filters out predictions with empty masks, resulting in more accurate and valuable outputs.
- 📚 **Updated Documentation Features:**
- Added a correct YouTube link for **SAHI Tiled Inference** for better instructional resources. 🎥
- Improved code examples for consistent and clear understanding of critical tools like `Colors` class and `merge_equals_args`. ✨
- ⚙️ **Validation Enhancements:** Restricted `save_hybrid` mode to only detection tasks, preventing incorrect usage and evaluation inaccuracies.

🎯 Purpose & Impact
- 🧹 **Cleaner Segmentations:** By removing predictions lacking usable masks, segmentation results are more reliable and efficient. This streamlines workflows and avoids irrelevant data.
- ✅ **Easier Learning & Usage:** Updated documentation improves the user experience through better resource links and clearer code examples, easing the learning curve for developers and users.
- ⚠️ **Prevents Missteps:** Limiting `save_hybrid` ensures omission of scenarios that could lead to misinterpretation of validation outputs, particularly for non-detection models.

This update is all about boosting the quality and usability of YOLO tools, paving the way for more productive and error-free model usage! 🏆

What's Changed
* Add https://youtu.be/ILqMBah5ZvI to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19532
* Fix reference docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19528
* Disable `save_hybrid` for OBB and update docs by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19531
* `ultralytics 8.3.84` Remove predictions with no masks by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19537


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.83...v8.3.84