Ultralytics

🌟 Summary
The Ultralytics 8.3.95 release introduces a CUDA-optimized Dockerfile for enhanced GPU support, updates dependencies, and improves documentation and usability across various tools and integrations.

📊 Key Changes
- **New NVIDIA CUDA Dockerfile**: Added `Dockerfile-nvidia-cuda` for optimized YOLO11 training and inference on GPUs.
- **Dependency Updates**: Upgraded PyTorch base image to `2.6.0-cuda12.6-cudnn9-runtime` and CoreML compatibility to version 8.0.
- **Improved Documentation**: Enhanced formatting, added examples, and clarified CLI usage across guides.
- **CLI Syntax Standardization**: Ensured array inputs (e.g., `classes`, `kpts`) are enclosed in quotes for consistency.
- **YOLOE Documentation Update**: Added detailed tips for selecting detection modes.
- **GitHub Banner Update**: Redirects to the latest YOLO11 blog for better user engagement.

🎯 Purpose & Impact
- **Enhanced GPU Performance**: The new CUDA Dockerfile ensures seamless multi-GPU training and inference, benefiting users with high-performance hardware. 🚀
- **Improved Compatibility**: Updates to CoreML and PyTorch ensure support for the latest Python versions and GPU optimizations. 🖥️
- **Better Usability**: Documentation improvements and CLI standardization reduce user errors and enhance the overall experience. 📚
- **Future-Proofing**: Keeps the project aligned with the latest software advancements, ensuring long-term reliability and efficiency. 🌟

This release is a significant step forward for developers and users leveraging YOLO11 for advanced AI tasks.

What's Changed
* Add bash codeblock formatting by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19804
* Add quotes for solutions CLI arguments by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19806
* Fix tip display in `YOLOE.md` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19813
* FROM pytorch/pytorch:2.6.0-cuda12.6-cudnn9-runtime by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19817
* Require `coremltools>=8.0` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19819
* Add https://youtu.be/EXIpyYVEjoI to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19820
* Update GitHub banner redirect by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19828
* `ultralytics 8.3.95` New `dockerfile-nvidia-cuda` FROM nvidia/cuda:12.8.1-cudnn-runtime-ubuntu22.04 by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19833


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.94...v8.3.95

🌟 Summary
The YOLO 8.3.94 release focuses on improving segmentation validation efficiency and enhancing documentation and code clarity across the project. 🚀

📊 Key Changes
- **Segmentation Validation Improvements**:
- Increased the limit of plotted masks during validation to 50 for better visualization.
- Added a warning when more than 50 masks are plotted to maintain speed and user awareness.
- Updated maximum detection limit for plotting predictions to 50 for consistency. ⚡
- **Documentation Enhancements**:
- Updated YOLOE documentation with corrected benchmarks and clarified threading and Ray Tune integration.
- Fixed inaccuracies in the Tiger Pose dataset description.
- **Code Standardization**:
- Replaced uppercase `Dict`, `List`, and `Tuple` with lowercase counterparts (`dict`, `list`, `tuple`) in type hints for modern Python compatibility.
- **JavaScript Optimization**:
- Improved JS minification logic for cleaner and more efficient documentation handling.

🎯 Purpose & Impact
- **Enhanced User Experience**:
- Improved segmentation validation plots provide more comprehensive insights into model performance while maintaining speed.
- Clearer documentation ensures better understanding of YOLOE's capabilities and usage.
- **Consistency and Modernization**:
- Standardized type hints align with Python best practices, improving code readability and maintainability.
- **Efficiency Gains**:
- Optimized JavaScript handling reduces file sizes, enhancing documentation performance.

This update ensures a smoother and more efficient experience for developers and users working with YOLO models. 🌟

What's Changed
* Update YOLOE Docs page by Laughing-q in https://github.com/ultralytics/ultralytics/pull/19776
* Use lowercase `dict/list/tuple` for type hint in docstrings by Laughing-q in https://github.com/ultralytics/ultralytics/pull/19771
* Fix JS Minification by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19786
* Fix `tiger-pose` dataset description in docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19789
* `ultralytics 8.3.94` Limit Segment val plots to 50 items/image for speed by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19792


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.93...v8.3.94

🌟 Summary
Release 8.3.93 introduces a critical fix for TorchScript models with Non-Maximum Suppression (NMS), along with several documentation and usability enhancements.

📊 Key Changes
- **TorchScript Fix**: Resolved a loading error for TorchScript models with NMS by ensuring `torchvision` is imported before model loading.
- **YOLOE Documentation**: Added comprehensive documentation for YOLOE, a new real-time open-vocabulary detection and segmentation model.
- **Documentation Improvements**:
- Updated banner image to AVIF format for faster loading.
- Minified HTML, CSS, and JS files for improved documentation performance.
- Enhanced parameter descriptions for export, prediction, validation, and visualization tasks.
- **New Features**:
- YOLOE supports text, visual, and internal prompts for detecting unseen object classes.
- Added `stream` for memory-efficient video/image processing and `txt_color` for customizable annotation text colors.

🎯 Purpose & Impact
- **TorchScript Fix**: Ensures seamless loading and inference for TorchScript models, improving reliability for users deploying models in production. 🛠️
- **YOLOE Introduction**: Expands YOLO's capabilities to open-world scenarios, enabling detection of arbitrary objects with state-of-the-art performance while maintaining YOLO's speed. 🚀
- **Documentation Enhancements**:
- Faster and more reliable documentation access due to optimized assets. ⚡
- Clearer parameter descriptions improve usability for developers and users. 📚
- **New Features**:
- YOLOE's versatility supports dynamic detection tasks, making it ideal for robotics, AR, and real-time applications. 🤖
- Memory-efficient processing and customizable visuals enhance user experience and flexibility. 🎨

This release focuses on improving model robustness, introducing cutting-edge capabilities, and enhancing user experience through better documentation and new features.

What's Changed
* Update style.css announcement bar AVIF by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19760
* Fix Docs CSS, Javascript and HTML minification by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19763
* Update macros by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19764
* Create YOLOE Docs page by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19766
* `ultralytics 8.3.93` Fix TorchScript Model with NMS loading error by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19747


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.92...v8.3.93

🌟 Summary
The YOLO11 `v8.3.92` release focuses on enhancing single-class training functionality, improving compatibility for TensorFlow exports, and refining documentation. These updates aim to provide a smoother user experience and expanded capabilities for diverse use cases. 🚀

📊 Key Changes
- **Single-Class Training Fix**: Resolved a cache error during single-class training by updating label processing logic.
- **TensorFlow Export Update**: Added `ai-edge-litert>=1.2.0` as a dependency for improved TensorFlow model export compatibility.
- **Python Version Check Fix**: Corrected version comparison logic for Jetson devices to prevent unnecessary dependency downgrades.
- **Documentation Enhancements**: Improved clarity, formatting, and examples across multiple sections, including YOLO11 integration and dataset usage.
- **Customizable Text Colors**: Introduced a `txt_color` parameter for detection result annotations, allowing users to specify RGB text colors.

🎯 Purpose & Impact
- **Improved Training Flexibility**: The single-class training fix ensures smoother workflows for users focusing on specific object categories.
- **Enhanced Export Reliability**: TensorFlow users benefit from more stable and efficient model exports, especially for edge AI applications.
- **Broader Compatibility**: Fixes for Python version checks enhance usability on Jetson devices, avoiding unnecessary compatibility issues.
- **User-Friendly Documentation**: Updated guides and examples make it easier for users to understand and utilize YOLO11 features effectively.
- **Customizable Visual Outputs**: The new `txt_color` parameter allows for more professional and adaptable visualizations, aligning with project-specific requirements. 🌈

These updates collectively enhance the usability, flexibility, and professional output of YOLO11, catering to a wide range of user needs. 🌟

What's Changed
* Add `ai-edge-litert>=1.2.0` to exporter.py by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19727
* Fix `Autobackend` Python version check by Auc7us in https://github.com/ultralytics/ultralytics/pull/19722
* Docs `/usage` updates by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19731
* Fix broken links in docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19734
* Bump astral-sh/setup-uv from 4 to 5 in /.github/workflows by dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/19736
* Documentation fixes and improvements by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19749
* Expose `txt_color` parameter for Results plots by zanaries in https://github.com/ultralytics/ultralytics/pull/19718
* `ultralytics 8.3.92` Fix `single_cls` training cache error by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19739

New Contributors
* zanaries made their first contribution in https://github.com/ultralytics/ultralytics/pull/19718
* Auc7us made their first contribution in https://github.com/ultralytics/ultralytics/pull/19722

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.91...v8.3.92

🌟 Summary
The YOLO `8.3.91` release focuses on simplifying TensorFlow installation, enhancing export compatibility for ARM64/Linux platforms, improving dataset handling, and refining documentation and visualization features. 🚀

---

📊 Key Changes
- **TensorFlow Installation Simplification**: Streamlined TensorFlow setup for smoother exports, with updated dependency requirements.
- **Export Enhancements**:
- Improved support for ARM64 and Linux platforms for TFLite and TensorFlow.js exports.
- Clearer error messages for unsupported configurations.
- **Dataset Handling Improvements**:
- Automatic fallback logic for missing `val` or `test` dataset splits.
- Enhanced logging of image batches during training for better Comet integration.
- **Visualization Updates**:
- Adjusted font sizes in image annotations for better readability.
- **Documentation Updates**:
- Added comparisons between YOLO models (e.g., YOLO11n-seg) and Meta's SAM models, highlighting YOLO's efficiency.
- Included new social media links (e.g., WeChat) for broader accessibility.

---

🎯 Purpose & Impact
- **Improved Compatibility**: Export processes are now more robust on ARM64 and Linux systems, reducing platform-specific issues. 🛠️
- **Enhanced User Experience**:
- Simplified TensorFlow installation and clearer error messages save users time and effort.
- Better dataset handling minimizes manual intervention and errors. 🗂️
- **Visualization & Logging**:
- Improved image annotations and logging streamline debugging and training workflows. 📊
- **Documentation Clarity**:
- Performance comparisons help users select the best model for their needs.
- Expanded social media presence ensures easier access to resources and support. 🌐

This release ensures smoother workflows, better platform support, and improved usability for developers and users alike. 🚀

What's Changed
* Improved visualization of labels in classify task for better Comet integration by yaricom in https://github.com/ultralytics/ultralytics/pull/19700
* Add WeChat social icon to Docs by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19702
* Update SAM vs YOLO benchmarks by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19705
* `ultralytics 8.3.91` Simplify `tensorflow` installation by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19712


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.90...v8.3.91

🌟 Summary
The Ultralytics `v8.3.90` release introduces several key updates, including a fix for memory usage calculations on Apple MPS devices, documentation enhancements, and architectural optimizations across YOLO models. 🚀

📊 Key Changes
- **MPS Memory Fix**: Updated memory usage calculation for Metal Performance Shaders (MPS) devices to improve accuracy using `psutil.virtual_memory().percent`.
- **YOLO Model Optimizations**: Reduced the number of layers across YOLO11, YOLOv8, and YOLOv9 models for improved efficiency without compromising performance.
- **Documentation Improvements**: Enhanced clarity, formatting, and consistency across multiple files, including detailed descriptions for methods and parameters.
- **Logging Enhancements**: Improved logging behavior for better user control and debugging.
- **C++ Example Fix**: Resolved input image dimension handling in YOLOv8 C++ inference code.
- **Default Solution Handling**: Added fallback to "count" solution when no solution name is provided in YOLO commands.

🎯 Purpose & Impact
- **Enhanced User Experience**:
- Apple MPS users benefit from more accurate memory tracking, optimizing resource management during training or inference. 🍎
- Clearer documentation and improved logging make the platform more user-friendly and easier to debug.
- **Performance Boost**:
- YOLO model optimizations lead to faster inference times and reduced computational overhead, enhancing efficiency for various tasks. ⚡
- **Developer Convenience**:
- Fixes in C++ examples and default solution handling streamline workflows for developers.

This release ensures better compatibility, performance, and usability for both developers and end-users. 🌟

What's Changed
* Documentation improvements by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19667
* Fix links.yml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19665
* Fix layer counts in model YAMLs by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19663
* Fix verbose for Solutions by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19651
* Fix `formatToSquare` bug in YOLOv8 C++ example by matriox1003 in https://github.com/ultralytics/ultralytics/pull/19653
* Fix solutions CI by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19675
* Large Python files documentation update by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19695
* Add `uv pip install` for Raspberry Pi CI Benchmarks and Tests by lakshanthad in https://github.com/ultralytics/ultralytics/pull/17912
* `ultralytics 8.3.90` Fix MPS `get_memory()` error by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19686


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.89...v8.3.90