Ultralytics

📊 Key Changes
- **PyPI Security Fix**: Ensures secure publication workflow for the Ultralytics package. 🔒
- **Batch Processing**: Added a `batch` parameter to `predict()` for faster processing of large datasets or videos. 🏎️
- **Segmentation Enhancement**: Adjusted output handling for segmentation tasks to address class name issues and improve robustness. 🛠️
- **Improved Examples**: Simplified heatmap and queue management examples for clearer usage. 📖
- **Dynamic Benchmarking**: Made version control for Raspberry Pi benchmarks easier in docs. 🔄
- **YouTube Tutorial Link**: Added a direct video link in `tutorial.ipynb` for enhanced learning. 🎥
- **Documentation Updates**: Fixed and updated links, added a `homepage` footer link, and improved accuracy in various docs. 🌐
- **Bug Fix**: Resolved an integer conversion issue in `xyxy2xywhn` for accurate results. 🐛

🎯 Purpose & Impact
- **Secure Publishing**: Protects the integrity of updates published on PyPI, safeguarding against potential threats. ✔️
- **Faster Inference**: The new `batch` parameter enables higher throughput, particularly valuable for power users with demanding workloads. 🚀
- **Better Segmentation**: Improved handling of segmentation workflows reduces errors, enhancing reliability for users working on complex datasets. ✅
- **User-Friendly**: Clearer examples and accurate docs reduce entry barriers and enhance the learning experience for beginners. 📘
- **Streamlined Workflow**: Innovations in dependency and workflow management make development and contribution processes smoother for maintainers. 🤝
- **Learning Resources**: A new tutorial video provides a practical guide to using Ultralytics YOLO models with Colab, bridging knowledge gaps for users. 🎬

This release balances technical fixes and documentation improvements, making it more robust, user-friendly, and secure! 🎉

What's Changed
* Update mkdocs.yml homepage by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17924
* Add `batch` to list of `predict()` arguments by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17979
* Add https://youtu.be/ZN3nRZT7b24 to tutorial.ipynb by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17875
* Improve docs for Ultralytics version in benchmarks by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17925
* `ultralytics 8.3.42` update AutoBackend `names` placeholder by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17970
* Update CLI message by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18041
* Update functions description by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18050
* Fix `np.empty_like` function input type by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18049
* `ultralytics 8.3.43` PyPI publishing security fix by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18052


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.40...v8.3.43

📊 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
* GPU CI Fix wrong Ultralytics Installation by Skillnoob in https://github.com/ultralytics/ultralytics/pull/17883
* Fix typo in Sony IMX500 doc by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17871
* wrong expression fix Update README.md by ArtificialZeng in https://github.com/ultralytics/ultralytics/pull/17868
* Add more tracking args for solutions by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17878
* `uv pip install` for Benchmarks by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17749
* Add `MNN` benchmarks to Raspberry Pi doc by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17910
* Add arguments for solutions in `usage/cfg` docs page by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17920
* `ultralytics 8.3.40` new `TrackZone` Solution by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17918

New Contributors
* ArtificialZeng made their first contribution in https://github.com/ultralytics/ultralytics/pull/17868

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.39...v8.3.40

🌟 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
* Add YOLO11 docs page redirect in `README.md` and `README.zh-CN.md` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17806
* Fix missing labels when all segment points are out of bounds by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17810
* New Solutions sweep counting annotator by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17742
* Improved Docs minify by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17816
* Revert Docs minify attempt by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17831
* Update format.yml Discord and Kaggle links by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17814
* Update contributing.md with open-sourcing guide by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17832
* Fix YOLO11 usage by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17834
* Update Tasks Banner in README.md by pderrenger in https://github.com/ultralytics/ultralytics/pull/17833
* Fix region-counting indents by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17835
* Update Tasks banner spacing by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17843
* Add functions descriptions in `plotting.py` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17813
* Remove Docs Tasks banner linebreak by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17844
* Add https://youtu.be/-aYO-6VaDrw and https://youtu.be/M7xWw4Iodhg to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17863
* Add `classes` to train arguments by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17856
* Add BGR to RGB conversion in LibTorch example by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17864
* `__getattr__` support to access YOLO attributes via Model class by WYYAHYT in https://github.com/ultralytics/ultralytics/pull/17805
* `ultralytics 8.3.39` fix classification validation loss scaling by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17851

New Contributors
* WYYAHYT made their first contribution in https://github.com/ultralytics/ultralytics/pull/17805

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.38...v8.3.39

🌟 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
* Delete .github/workflows/codeql.yaml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17772
* Fix `RepC3` module for RT-DETR models by Andrewymd in https://github.com/ultralytics/ultralytics/pull/17086
* Removes unused argument `label_smoothing` by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/16014
* Concat all segments by default for multi-part masks by Y-T-G in https://github.com/ultralytics/ultralytics/pull/16826
* Improve `is_jetson` to support more Jetson devices by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17770
* Fix DLA export by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17765
* Fix CI.md CodeQL badges by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17779
* Fix Prettier docs issues by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17798
* Convert frames to RGB before SAHI inference by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17795
* Fix parking points annotator for macOS by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17797
* Fix `forward_fuse` comment by arun477 in https://github.com/ultralytics/ultralytics/pull/17714
* `ultralytics 8.3.38` SAM 2 video inference by Laughing-q in https://github.com/ultralytics/ultralytics/pull/14851

New Contributors
* Andrewymd made their first contribution in https://github.com/ultralytics/ultralytics/pull/17086
* arun477 made their first contribution in https://github.com/ultralytics/ultralytics/pull/17714

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.37...v8.3.38

🌟 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
* Fix labels padding for Letterbox with `center=False` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17728
* Add https://youtu.be/c-S5M36XWmg & https://youtu.be/fd6u1TW_AGY to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17722
* Update `coco-seg.yaml` to `coco.yaml` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/17739
* Bump astral-sh/setup-uv from 3 to 4 in /.github/workflows by dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/17753
* Standardize default region points in docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17721
* Enable model.eval() usage for `YOLO` class by Laughing-q in https://github.com/ultralytics/ultralytics/pull/17754
* `ultralytics 8.3.37` TensorRT auto-workspace size by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/17748


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.36...v8.3.37

🌟 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
* Fix `imx500` YOLO support in export table by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17702
* Ultralytics Refactor https://ultralytics.com/actions by pderrenger in https://github.com/ultralytics/ultralytics/pull/17701
* Update extra.js by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17705
* Minify-html fix by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17706
* `extra.js` dark mode fix by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/17707
* Benchmarks graph Javascript fix by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/17700
* Standardize default region points by Jerry-Kon in https://github.com/ultralytics/ultralytics/pull/17715
* `ultralytics 8.3.36` unpin OpenVINO ARM install version by adrianboguszewski in https://github.com/ultralytics/ultralytics/pull/16600

New Contributors
* Jerry-Kon made their first contribution in https://github.com/ultralytics/ultralytics/pull/17715

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.35...v8.3.36