Ultralytics

🌟 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. 💊💻✨

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📊 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. ⚙️

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🎯 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
* Use `Any` type-hints for `args` and `kwargs` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18372
* Update FAQ examples in callbacks.md by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18377
* Add MobileSAM auto annotation feature 🚀 by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18374
* Fix Docs calls to `model.benchmark()` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18391
* PyCharm Code Inspect fixes by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18392
* Remove docs.yml fetch-depth 0 by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18394
* Clone Docs `gh-pages` branch `--depth 1` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18396
* Update image count information for COCO-Pose by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18395
* PyCharm Code Inspect fixes for Solutions and Examples by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18393
* Simplify links.yml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18399
* Code scan fixes by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18398
* Restrict ONNX ExecutionProviders by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18400
* Update yolo11n-pose to yolo11n in `speed-estimation.md` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18410
* fix bug in the ConfusionMatrix class by yuzhj in https://github.com/ultralytics/ultralytics/pull/18409
* `ultralytics 8.3.55` New Medical-pills dataset by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18389

New Contributors
* yuzhj made their first contribution in https://github.com/ultralytics/ultralytics/pull/18409

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.54...v8.3.55

🌟 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.

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📊 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.

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🎯 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
* Add `dynamic` to approved OpenVINO export args by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18353
* Bump astral-sh/setup-uv from 4 to 5 in /.github/workflows by dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/18358
* Update `YOLOv8` to `YOLO11` in `region-counting.md` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18360
* Min CI Python 3.9 from 3.8 by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18355
* [Example] RTDETR-ONNXRuntime-Python by semihhdemirel in https://github.com/ultralytics/ultralytics/pull/18369
* `ultralytics 8.3.54` New Streamlit inference Solution by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18316


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.53...v8.3.54

🌟 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. 🚀

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📊 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.

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🎯 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
* Fix JetPack6 Dockerfile for NVIDIA Jetson by lakshanthad in https://github.com/ultralytics/ultralytics/pull/18335
* Improve JetPack5 Dockerfile for NVIDIA Jetson by lakshanthad in https://github.com/ultralytics/ultralytics/pull/18334
* Validate arguments passed as dict to `settings.update()` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18337
* `ultralytics 8.3.53` New Export argument validation by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18185


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.52...v8.3.53

📊 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
* Revert `segment2box` and clip segments by Laughing-q in https://github.com/ultralytics/ultralytics/pull/18294
* Update JetPack6 Dockerfile with latest JetPack6.1 by lakshanthad in https://github.com/ultralytics/ultralytics/pull/18295
* Add https://youtu.be/6bZeCs0xwO4 to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18292
* Fix RGB to BGR conversion in TFLite example by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18305
* Align solutions YAML with `default.yaml` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18300
* Fix incorrect `scale` description by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18303
* Update Jetson doc with NVIDIA Jetson Orin Nano Super Developer Kit by lakshanthad in https://github.com/ultralytics/ultralytics/pull/18289
* ROS Guide, updated YOLO version by ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/18325
* `ultralytics 8.3.52` AutoBatch CUDA computation improvements by Laughing-q in https://github.com/ultralytics/ultralytics/pull/18291


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.51...v8.3.52

🌟 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. 🚀

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📊 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. 🎉

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🎯 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
* Update SAHI example from `YOLOv8` to `YOLO11` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18276
* Add `imx500` and `MNN` in `tutorial.ipynb` export table by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18254
* Add hyperparameter search space to Docs by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18259
* Use `shell=True` to run hyperparameter tuning by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18284
* Add security alarm system as ultralytics solution by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18281
* `ultralytics 8.3.51` AutoBach logspace fit and checks by Laughing-q in https://github.com/ultralytics/ultralytics/pull/18283


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.50...v8.3.51

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📊 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.

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🎯 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
* Removed duplicate IMX500 docs reference by ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/18178
* Fix deleted author profile by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18180
* Fix validation callbacks not triggered during OBB model training by dagokl in https://github.com/ultralytics/ultralytics/pull/18175
* Fix untrained warning when training from yaml by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18168
* Fix SAM CUDA hard-code by adamp87 in https://github.com/ultralytics/ultralytics/pull/18153
* Add YOLO11 audio podcast by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18174
* Add https://youtu.be/qE-dfbB5Sis to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18207
* Add https://youtu.be/j0MOGKBqx7E to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18222
* Add type for `train` arguments by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18221
* Fix Docs relative trailing backlash bug by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18244
* Fix `model.save()` for model YAMLs by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18212
* `ultralytics 8.3.50` Enhanced segment resample by Laughing-q in https://github.com/ultralytics/ultralytics/pull/18171

New Contributors
* dagokl made their first contribution in https://github.com/ultralytics/ultralytics/pull/18175

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.49...v8.3.50