Ultralytics

🌟 Summary
The v8.3.75 release includes robust updates for improved model export compatibility, user experience, and error handling across platforms, alongside enhanced documentation and integration refinements. 🚀

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📊 Key Changes
- **Enhanced CometML Integration**:
- Transitioned to the new `comet_ml.start()` API for smoother experiment handling.
- Deprecated the `COMET_MODE` variable, introducing `COMET_START_ONLINE` for consistency.

- **Export Function Updates**:
- **Protobuf Dependency**: Added support for `protobuf>=5` for TensorFlow and TFLite exports, resolving compatibility issues.
- **Edge TPU and TF.js**: Addressed platform-specific limitations for ARM64 and Linux exports to prevent unsupported configuration errors.

- **Documentation Improvements**:
- Updated SAM auto-annotation, YOLOv8, and export format descriptions for clarity.
- Redesigned inference examples to use accessible publicly hosted image URLs.

- **New CLI Solutions**:
- Introduced practical use cases, including object counting, workout monitoring, queue analysis, and browser-based inference with Streamlit.

- **Benchmarking Added**:
- Include new comparative performance metrics for popular object detection models like Gold-YOLO, YOLO-NAS, RTDETRv3, etc.

- **Windows-Specific Fix**:
- Resolved an async file write bug to improve caching reliability on Windows systems.

- **Improved Timing Precision**:
- Switched to `time.perf_counter()` for latency measurements, ensuring greater precision during benchmarking.

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🎯 Purpose & Impact
- **Improved Experiment Tracking**:
- Seamless CometML integration provides better environment consistency and logging during training processes.

- **Enhanced Export Reliability**:
- Future-proofs TensorFlow and TFLite workflows while providing early error detection for ARM64/Linux users.

- **Streamlined User Experience**:
- Updated documentation and example consistency ensure clarity, especially for beginners, minimizing friction during model setup and usage.

- **Greater Platform Support**:
- Addressed critical Windows and platform-specific export edge cases, enhancing cross-platform usability.

- **Better Model Insights**:
- Added benchmarks empower users to make informed decisions about which object detection models to implement based on accuracy, speed, and computational cost.

This release focuses heavily on improving reliability, usability, and documentation quality while resolving critical bugs and adding more tools for diverse real-world applications.

What's Changed
* Auto-annotate and SAM docs update by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19156
* Switch to `perf_counter()` for latency measurement by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19177
* Force protobuf>=5 for SavedModel export by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19206
* Fix Docker QEMU issues while building JetPack 6 Dockerfile by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19216
* Fix `bus.jpg` path in `predict.md` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19203
* Update NMS description on export-args.md by Buligon in https://github.com/ultralytics/ultralytics/pull/19211
* Add NMS related args to export-table.md by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19215
* Add Docs models benchmark by Laughing-q in https://github.com/ultralytics/ultralytics/pull/19176
* Tasks Docs updates by LexBarou in https://github.com/ultralytics/ultralytics/pull/19181
* YOLOv8 Docs updates by LexBarou in https://github.com/ultralytics/ultralytics/pull/19182
* Add Solutions CLI usage in `quickstart.md` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19160
* Fix `edgetpu` and `tfjs` exports for `arm64` Linux by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19154
* Fix windows async np.save bug by eric80739 in https://github.com/ultralytics/ultralytics/pull/19218
* Fix `print()` for ConfusionMatrix for Classify task by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19169
* Fix updating of best epoch during early stopping by vfcosta in https://github.com/ultralytics/ultralytics/pull/19164
* `ultralytics 8.3.75` Comet update to new `comet_ml.start()` API by yaricom in https://github.com/ultralytics/ultralytics/pull/19187

New Contributors
* vfcosta made their first contribution in https://github.com/ultralytics/ultralytics/pull/19164
* eric80739 made their first contribution in https://github.com/ultralytics/ultralytics/pull/19218

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.74...v8.3.75

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📊 Key Changes
- 🔧 **Fixed Ray Tune Callback Issues**: Replaced deprecated `ray.tune.is_session_enabled()` with `ray.train._internal.session.get_session()` ensuring compatibility with latest Ray versions.
- 🛠 **Enhanced Deterministic Training Control**: Added `unset_deterministic()` to handle environment changes, and prevent unnecessary CUDA warnings.
- 🖼 **PIL Image Support in `plot()`**: Allowed direct return of PIL images with `annotator.im`, improving compatibility with PIL workflows.
- 🚀 **Improved Export Flexibility**: Adjusted `model.export()` to take a `data` parameter while simplifying `predict()` calls.
- 🐳 **Optimized Docker Workflow**: Improved Docker token authentication and switched to `docker build` for better stability and security.
- ✅ **Streamlined Benchmarking**: Cleaned dataset and metric assignments in benchmarking to avoid redundancy and improve reliability.

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🎯 Purpose & Impact
- 🚀 **Greater Compatibility**: Seamless integration with the latest versions of Ray ensures that errors linked to deprecated methods are resolved.
- ⚡ **Workflow Flexibility**: Managing deterministic settings dynamically boosts training adaptability while cleaning up workflow logs.
- 📸 **Visualization Improvements**: Returning PIL images directly simplifies further processing in pipelines dependent on image outputs.
- 🛠️ **User-Friendly Model Exports**: Configurable export makes model usage and testing more straightforward for developers.
- 🔒 **Stronger Security**: Docker workflow improvements enhance authentication security, benefitting advanced build setups.
- ✅ **Clarity in Development**: Benchmark logic cleanup minimizes confusion and potential errors, improving developer experience.

This version is packed with incremental improvements, making model training, testing, and deployment smoother and more user-friendly while preparing Ultralytics for the future. 🎉

What's Changed
* Fix docker.yml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19125
* Fix missing data warning and undefined variables by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19124
* Fix missing data.yaml error on int8 export by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19128
* Return PIL image if `pil=True` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19146
* Unset CUBLAS_WORKSPACE_CONFIG for non-deterministic training and inference by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19138
* `ultralytics 8.3.74` Fix Ray Tune callback error by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19144


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.73...v8.3.74

🌟 Summary
The Ultralytics `v8.3.73` release focuses on enhancing containerization workflows, updating library dependencies, improving documentation, and refining the development process. 🚀

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📊 Key Changes
- **Containerization Enhancements**:
- Added support for publishing Docker images to both **GitHub Container Registry (GHCR)** and **Docker Hub** with rich metadata for better usability. 🐋
- Removed Ubuntu 24.04 ARM in CI workflows for streamlined testing.
- **Dependency and Platform Updates**:
- Updated NVIDIA Jetson support with **PyTorch 2.2.0** and **Torchvision 0.17.2** for improved compatibility and performance. 🤖
- Removed the `beautifulsoup4` dependency, cleaning up the development environment. 🧹
- **Code Refactoring**:
- Improved SQL result export by simplifying insertion logic and fixing potential issues with empty results.
- Enhanced type hinting for better code clarity.
- **Documentation and Tutorial Updates**:
- Added an embedded YouTube tutorial on *Package Segmentation* in the documentation. 🎥✨

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🎯 Purpose & Impact
- **Containerization Accessibility**:
- Publishing images to both Docker Hub and GHCR ensures users have multiple options for pulling images, increasing global availability and reducing friction. 🌍
- The inclusion of detailed metadata in Docker images improves clarity for end-users.
- **Better Hardware and Development Support**:
- NVIDIA Jetson users benefit from *newer library versions* for seamless deployment and better model performance.
- Leaner development environments reduce installation times and maintenance burdens.
- **Improved Learning Resources**:
- The YouTube tutorial enriches the documentation and aids new and existing users in understanding package segmentation workflows visually. 📚👩‍💻

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TL;DR: This version updates Docker container workflows, improves NVIDIA Jetson compatibility, cleans up dev dependencies, and enhances user education through new video tutorials. 🚀💡

What's Changed
* Remove `beautifulsoup4<=4.12.3` pin by Laughing-q in https://github.com/ultralytics/ultralytics/pull/19103
* Update JetPack 5 `torch` and `torchvision` packages by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19098
* Minor `Results.to_sql` cleanup by Laughing-q in https://github.com/ultralytics/ultralytics/pull/19081
* Add https://youtu.be/im7xBCnPURg to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19115
* `ultralytics 8.3.73` GitHub Container Registry Images at `ghcr.io` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19114


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.72...v8.3.73

🌟 Summary
The `v8.3.72` release focuses on enhancing NVIDIA Jetson DLA (Deep Learning Accelerator) core compatibility for inference, improving export documentation, and resolving minor inefficiencies and errors for broader usability and smoother performance. 🚀

📊 Key Changes
- **Enhanced NVIDIA Jetson DLA Support**:
- Introduced explicit control of DLA core selection (`dla:0`/`dla:1`) during TensorRT export and inference.
- Added detailed documentation of NVIDIA Jetson DLA device specifications (core count, frequency, etc.).
- Fixed metadata handling for DLA-specific inference settings.
- **Export Documentation Overhaul**:
- Added detailed argument tables for all model export formats (e.g., ONNX, TensorRT, CoreML), improving clarity on custom export options such as half-precision (FP16), INT8 quantization, and dynamic input sizes.
- **Optimized `seg_bbox` Rendering**:
- Refined label handling logic in the plotting utility, reducing unnecessary operations if a label is absent, slightly improving performance.
- **Bug Fixes**:
- Resolved an issue with missing `nc` attributes during NMS export, improving reliability in multi-GPU or custom training setups.
- **Documentation Updates**:
- Enhanced Crack Segmentation Dataset resources with direct Colab integration, a tutorial notebook, and a demo video for easier onboarding.

🎯 Purpose & Impact
- **Improved Compatibility**: The NVIDIA Jetson DLA improvements ensure that edge devices benefit from seamless inference setups, enabling accelerated performance with reduced bottlenecks. Ideal for IoT and edge AI devices. 🖥️✨
- **Simplified Export Process**: The new export argument tables demystify complex configurations, empowering users to adapt models for their specific hardware or workflows more easily. 📦🔧
- **Performance Benefits**: Minor optimizations ensure faster runtime efficiency, especially for visualization and plotting tasks where unnecessary computations are avoided. ⚡
- **Enhanced Reliability**: Fixes like handling missing `nc` attributes and metadata improve model robustness, particularly in advanced user scenarios (e.g., multi-GPU setups, custom models). ✅
- **Streamlined Learning Experience**: The improved Crack Segmentation training resources lower the barrier to entry for researchers in infrastructure and transportation safety fields. 🛠️🚗

This release represents a strong push for enhanced edge device support, better export usability, and overall reliability improvements while empowering both beginners and advanced users. 🎉

What's Changed
* Optimize `seg_bbox` calculations by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19056
* Resolve warnings by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19073
* Add https://youtu.be/C4mc40YKm-g and notebook badge in docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19086
* Add Export Arguments tables to all Export docs by lakshanthad in https://github.com/ultralytics/ultralytics/pull/18952
* Fix missing nc attribute error on NMS export by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19083
* Replace `beautifulsoup4` pin with `mkdocs-ultralytics-plugin>=0.1.17` by Laughing-q in https://github.com/ultralytics/ultralytics/pull/19085
* `ultralytics 8.3.72` Fix NVIDIA Jetson DLA core support for DLA inference by Laughing-q in https://github.com/ultralytics/ultralytics/pull/19078


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.71...v8.3.72

🌟 Summary
The **v8.3.71** update enhances code clarity and resolves dependency issues by replacing ambiguous `nn` references with explicit `torch.nn` usage. It also improves documentation and user experience with various fixes and additions.

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📊 Key Changes
- **Explicit Naming**: Codebase updated to use `torch.nn` instead of `nn`, ensuring clarity between PyTorch and Ultralytics modules.
- **Dependency Fix**: Capped `beautifulsoup4` to version `4.12.3` to avoid documentation build errors.
- **Progress Bar Optimization**: Added `mininterval=1.0` for smoother and consistent updating of `tqdm` progress bars.
- **Documentation Improvements**:
- Added tutorial video to TrackZone integration docs, enhancing learning and usability.
- Updated handling of relative dataset paths for better clarity.
- Added troubleshooting tips for RKNN issues with Rockchip integration.
- Simplified cloning instructions for the `picamera2` repository in Sony IMX500 setups.
- Excluded auxiliary pages like `/compare` from the documentation navigation.
- **Minor Fixes**: Adjusted documentation examples for better readability and alignment with Python best practices.

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🎯 Purpose & Impact
- **Enhanced Readability** 🧹:
- Disambiguating `torch.nn` vs. `ultralytics.nn` reduces confusion for developers and improves compliance with coding standards.
- Cleaner, more informative documentation makes tools easier to use, especially for new users.

- **Improved User Experience** 🎥📝:
- Video tutorials and better dataset guidance streamline workflows and learning.
- RKNN troubleshooting tips address runtime issues effectively for advanced users.

- **Smoother Development Workflow** 🚀:
- Dependency fixes ensure a more stable experience during documentation builds.
- The explicit naming structure reflects best practices, making the codebase future-proof and easier to maintain.

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This release primarily aids developers with code clarity and users with enhanced documentation. Whether you're debugging workflows, learning tools, or contributing to the codebase, these updates simplify the process and save time. 🌟

What's Changed
* Add Lychee to CI Summary by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18948
* Update branch of `picamera2` in Sony IMX500 Doc by lakshanthad in https://github.com/ultralytics/ultralytics/pull/18954
* Add https://youtu.be/SMSJvjUG1ko to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18959
* Enhance clarity in `results.to_` function examples. by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18957
* Clarify relative path usage for `path` in dataset `yaml` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/18953
* Add note on failing RKNN inference on select Rockchip hardware by lakshanthad in https://github.com/ultralytics/ultralytics/pull/18964
* Add `not_in_nav` section to mkdocs.yml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19068
* `ultralytics 8.3.71` require explicit `torch.nn` usage by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19067


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.70...v8.3.71

🌟 Summary
The **v8.3.70** release brings feature enhancements with improved export functionalities, updated compatibility for PyTorch, and usability enhancements in benchmarking and documentation. 🚀

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📊 Key Changes
- **Sony IMX500 Export Update**: Added support for the `data` argument, enabling dataset configuration during export for better control over quantization in formats like OpenVINO, TensorRT, and TF Lite. 📁
- **Torch 2.6 Compatibility**: Updated Torch-Torchvision mappings to ensure seamless functionality with the latest PyTorch update. 🔧
- **Format-Specific Benchmarking**: Introduced benchmarking support for individual formats (e.g., ONNX) to allow targeted performance evaluations. 📊
- **NVIDIA DLA Support**: Implemented support for running models on specific NVIDIA DLA cores—a key feature for specialized hardware optimization. 🖥️
- **Improved `numpy` Stability**: Pinned `numpy` version to prevent compatibility issues with OpenVINO and TFLite during CI tests. ✅
- **Documentation Enhancements**: Added tutorial videos and refined sections for clarity, aiding users and contributors. 📚

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🎯 Purpose & Impact

- **Improved Export Workflows**:
- **Purpose**: The `data` argument helps users customize exports with specific dataset configurations, simplifying quantization and compatibility for edge and on-premise deployment.
- **Impact**: Makes exports more robust and adaptable to diverse workflows, ensuring higher-quality models with optimized performance.

- **Torch Compatibility**:
- **Purpose**: Keep the framework current with the latest PyTorch improvements.
- **Impact**: Allows users to leverage PyTorch 2.6's advancements without compatibility hiccups, maintaining a seamless experience.

- **More Granular Benchmarking**:
- **Purpose**: Enable granular analysis of models' efficiency in specific formats like ONNX or TensorFlow Lite.
- **Impact**: Helps developers fine-tune models for scenarios where particular formats are essential for deployment.

- **DLA Optimization**:
- **Purpose**: Ensure efficient inference on NVIDIA's specialized hardware.
- **Impact**: Reduces computational overhead and maximizes performance for users running models on NVIDIA DLA platforms.

- **CI Stability with `numpy`**:
- **Purpose**: Prevent runtime or testing errors due to incompatible `numpy` versions.
- **Impact**: Ensures reliable and predictable performance for developers and CI pipelines.

- **Accessible Documentation**:
- **Purpose**: Make it easier for new contributors and users to onboard through visual and detailed guides.
- **Impact**: Encourages community growth and simplifies the learning curve for both model and framework regulars.

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🎉 This release is packed with features to empower smoother workflows, improve hardware compatibility, and promote user-friendly innovation! 🌟

What's Changed
* Update torchvision compatibility table for `torch 2.6` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18935
* Add https://youtu.be/yMR7BgwHQ3g to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18936
* Add support for single `export` format `benchmark` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/18740
* Remove YOLOv10 benchmarks from CI by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18937
* mkdocs-ultralytics-plugin>=0.1.16 by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18942
* Pin `numpy<=2.1.1` to resolve failing --slow CI by lakshanthad in https://github.com/ultralytics/ultralytics/pull/18943
* Add DLA specific core usage by AbelHaro in https://github.com/ultralytics/ultralytics/pull/18930
* Minor Docs edits by LexBarou in https://github.com/ultralytics/ultralytics/pull/18940
* Eliminate `numpy<2.0.0` pin for OpenVINO on macOS by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18945
* `ultralytics 8.3.70` add `data` argument to Sony IMX500 export by lakshanthad in https://github.com/ultralytics/ultralytics/pull/18852

New Contributors
* LexBarou made their first contribution in https://github.com/ultralytics/ultralytics/pull/18940
* AbelHaro made their first contribution in https://github.com/ultralytics/ultralytics/pull/18930

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.69...v8.3.70