Ultralytics

🌟 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. 🚀

---

📊 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.

---

🎯 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. 🚀

---

📊 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. 🎉

---

🎯 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

---

📊 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.

---

🎯 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

---

📊 Key Changes
- **Docker Enhancements**:
- Replaced standard `pip install` with `uv pip install` for better Python package management. 🐍🛠️
- System-level package installations across all Dockerfiles for increased reliability.
- Included flags like `--index-strategy` to handle edge cases more robustly.

- **YOLO Dataset Compatibility**:
- Standardized `category_id` indexing in COCO and LVIS datasets, starting indices from 1 for consistency. 📊

- **PyTorch Version Support**:
- Added compatibility for PyTorch `2.5` and Torchvision `0.20` versions. 🔄

- **Documentation Improvements**:
- Updated NVIDIA Jetson guide to explain Deep Learning Accelerator (DLA) functionality and limitations more clearly. 📝
- Refined export format table for YOLOv5 to include improved links to relevant integration guides. 🌐

- **Testing Optimization**:
- Removed slow and outdated Google Drive-dependent tests to streamline testing workflows. 🧪

- **GitHub Workflow Update**:
- Added a `git pull` step to ensure the latest documentation changes are fetched before updates. ⚙️

---

🎯 Purpose & Impact
- **Enhanced Package Management**:
Consolidating Python package installations with `uv pip` ensures safer and more predictable setups, reducing dependency issues. 🛡️

- **Better Dataset Compatibility**:
Improved indexing logic aligns with common standards, reducing confusion during COCO/LVIS dataset evaluations. 📋

- **Future-Ready PyTorch Support**:
Developers leveraging the latest PyTorch and Torchvision versions can seamlessly integrate without compatibility issues, unlocking new features and performance improvements. 🚀

- **Improved Documentation Usability**:
Clearer and more accessible docs guide users in leveraging advanced features, such as model exporting and NVIDIA DLA usage, empowering informed decision-making. 📖✨

- **More Efficient Testing**:
By removing redundant tests, testing processes are faster and less prone to failure caused by external factors like rate limits. ⏩

- **Robust Documentation Workflow**:
Ensures smooth updates and reduces the likelihood of conflicts or overwriting recent changes in collaborative environments. ✅

---

This update reflects Ultralytics' commitment to improving usability, stability, and developer experience across the board! 🌟

What's Changed
* Bump astral-sh/setup-uv from 3 to 4 in /.github/workflows by dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/18123
* Update Jetson Doc with DLA info by lakshanthad in https://github.com/ultralytics/ultralytics/pull/18128
* Update YOLOv5 export table links by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18130
* Update `torchvision` compatibility table by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18117
* Change index to start from 1 by default in `predictions.json` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18140
* Remove Google Drive test by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18162
* Git pull docs before updating by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18163
* `ultralytics 8.3.49` Docker images `uv pip install` by pderrenger in https://github.com/ultralytics/ultralytics/pull/18115


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.48...v8.3.49

🌟 Summary
The `v8.3.48` release focuses on **enhanced security, efficiency, and user convenience** for the Ultralytics CI/CD pipelines and broader ecosystem. Key highlights include improved workflows for publishing, caching, documentation updates, and dependency handling. 🚀✨

---

📊 Key Changes
- **Workflow Security Enhancements**:
- Split PyPI publishing into **stages** (`check`, `build`, `publish`, and `notify`) for better control and automation. 🛠️
- Enabled version handling to ensure only necessary updates are pushed to PyPI. 🔄
- Improved notification systems for success or failure reporting. 📣
- **Dependency Improvements**:
- Added a `--no-cache` flag to ensure cleaner and more reliable Python installations during publishing workflows. 🧹
- **Better Cache Management**:
- Introduced automated CI cache pruning across workflows, reclaiming gigabytes of disk space in tests and GPU CI jobs. 🧑‍💻💾
- **Documentation Fixes**:
- Updated OpenVINO links to guide users toward the most recent version, ensuring accurate and up-to-date information. 🔗📄

---

🎯 Purpose & Impact
- **Stronger Security** 🔒:
- The new CI/CD structure minimizes risks by organizing tasks into stricter, separate stages and limiting unnecessary permission exposure during workflows.
- **Improved CI/CD Efficiency** ✅:
- Automatic cache pruning and artifact handling enable faster builds/tests while reducing redundant storage usage.
- The `--no-cache` flag ensures the freshest dependencies, reducing debugging time caused by outdated installations.
- **Enhanced User Experience** 🌟:
- Developers benefit from cleaner workflows in the open-source Ultralytics ecosystem, ensuring smoother package publishing and version management.
- Updated OpenVINO references ensure users can fully leverage recent AI acceleration tools for optimized model performance.

What's Changed
* Update publish.yml with `--no-cache` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18095
* Add CI cache pruning by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/17664
* OpenVINO broken link fix by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18107
* `ultralytics 8.3.48` split PyPI publish jobs for security by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18111


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.47...v8.3.48