Ultralytics

🌟 Summary
Ultralytics release `v8.3.83` focuses on refining image augmentation to make color transformations more natural while clarifying validation parameter requirements in the documentation. 🎉

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📊 Key Changes
- **Image Augmentation Adjustments**:
- Reverted hue, saturation, and value (HSV) augmentation logic back to a **relative shift approach** for more natural-looking image transformations.
- Fixed an issue with hue adjustments to align with the original implementation for improved consistency.
- Ensured constraints, like maintaining pure white to avoid unnatural color changes, remain enforced. 🎨

- **Documentation Improvement**:
- Clarified the `batch` parameter for validation, specifying that it must be a **positive integer** to avoid confusion about unsupported functionality like `AutoBatch` in validation. 📚

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🎯 Purpose & Impact
- **For Developers and Users**:
- 🎨 **Enhanced Visual Realism**: The relative shift method restores the natural look of augmented images, which is important for data augmentation and model training. This could improve model performance on tasks requiring realistic visual data.
- 🛠 **Accurate Transformations**: Bug fixes ensure reliable and consistent adjustments during augmentation, preventing potential training issues.
- 📚 **Eased Configuration**: Improved documentation reduces ambiguity in setting validation parameters, simplifying usage for both new and experienced users.

This release is a step forward in delivering both technical precision and user efficiency! 🚀

What's Changed
* Fix `batch` description for validation by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19504
* `ultralytics 8.3.83` Revert saturation and value augmentation to relative shift by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19515


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.82...v8.3.83

📊 Key Changes
- **ONNX FP16 Export Fix**: Introduced a workaround (`arange_patch`) for exporting ONNX models with both `dynamic` and `half` options, avoiding incompatibilities in the PyTorch `torch.arange` function.
- **Preprocessing Accuracy Boost**: Improved image handling (aspect ratio, resizing, and padding) for ONNXRuntime examples to ensure more precise object detection.
- **Broader MNN Test Coverage**: Enabled MNN export testing on Raspberry Pi to ensure compatibility.
- **Dataset Configuration Streamlining**: Refined `open-images-v7.yaml` to centralize dataset directory management for clarity and maintainability.

🎯 Purpose & Impact
- **ONNX Export Enhancements**:
- **Purpose**: Solve PyTorch incompatibility issues during ONNX model export with FP16 precision in dynamic mode.
- **Impact**: Developers can now seamlessly export high-performance ONNX models while avoiding device-specific restrictions, improving workflows on GPUs.

- **Accurate Preprocessing**:
- **Purpose**: Fix preprocessing bugs in ONNXRuntime inference to ensure image resizing, aspect ratio, and padding align with expected outputs.
- **Impact**: Results in more consistent and reliable object detection performance between PyTorch and ONNX models.

- **Compatibility Extension**:
- **Purpose**: Enable MNN export tests on Raspberry Pi to ensure cross-platform functionality.
- **Impact**: Broader hardware compatibility for MNN format users, improving reliability on low-cost systems like Raspberry Pi.

- **Dataset Management Improvements**:
- **Purpose**: Streamline configuration for the Open Images V7 dataset.
- **Impact**: Easier and more consistent dataset setup, particularly useful for developers managing large datasets.

This update improves model export workflows, ensures consistent inference results across platforms, and expands compatibility for developers using diverse environments. 🌐

What's Changed
* Enable `mnn` in Raspberry Pi Tests by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19483
* Fix ONNX Example letterboxing by quangdungluong in https://github.com/ultralytics/ultralytics/pull/19486
* Fix dataset_dir error with `open-images-v7.yaml` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19475
* `ultralytics 8.3.82` ONNX `dynamic` and `half` export by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19464

New Contributors
* quangdungluong made their first contribution in https://github.com/ultralytics/ultralytics/pull/19486

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.81...v8.3.82

🌟 Summary
The **Ultralytics v8.3.81** release resolves a critical memory management issue in model validation while introducing key updates to documentation, testing workflows, and system reporting for enhanced usability and efficiency. 🚀✨

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📊 Key Changes
- 🧹 **Memory Leak Fix in Validation Modules**:
Resolved circular references in metrics (`on_plot`) across validators (`DetectionValidator`, `PoseValidator`, etc.) to avoid CPU memory leaks during repeated evaluations.
- 📚 **Documentation Enhancements**:
Added examples for annotators in the library, and fixed/corrected metadata instructions for Triton guides and links in SAM 2 documentation.
- 🐧 **Raspberry Pi CI Improvements**:
Re-enabled Raspberry Pi CI workflow with improved benchmarks and added cleanup for self-hosted CI runners.
- 🔍 **Installation Diagnostics Update**:
Added project root installation path in the system environment summary for easier debugging.
- 📊 **Better Table Sorting in Docs**:
Improved table sorting functionality for file sizes, numbers, and dot-separated values.

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🎯 Purpose & Impact
- **Memory Management Improvement**:
Eliminates residual memory accumulation when evaluating models repeatedly in scripts, preventing CPU overloading and Out-Of-Memory (OOM) errors. This fix improves the efficiency and reliability of long-running evaluation pipelines. 🧠🔥
- **User-Friendly Documentation**:
New usage examples and enhanced instructions make it easier for developers to understand and implement key features. Correcting SAM 2 links ensures access to the right research resources. 📚💡
- **Expanded Testing Coverage**:
Re-enabling Raspberry Pi CI and adding benchmarks enhances testing on diverse hardware, ensuring broader reliability across platforms. Improved CI cleanup reduces risks of resource conflicts. 🌐🔧
- **Debugging Simplification**:
Including installation paths in diagnostics facilitates resolving Python environment-related issues for developers. 🔍🛠️
- **Enhanced Sorting for Visual Data**:
Improved table sorting in docs boosts usability, especially when navigating large datasets or results with complex numbering or file size values. 📈✨

Overall, this update strengthens performance, stability, and developer experience, while prioritizing clarity and usability for the broader community. 🎉

What's Changed
* Update Raspberry Pi 5 CIs with 16GB model by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19306
* New: add `SAM` and `SAM-2` notebook in docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19461
* Add installation path to `yolo checks` output by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19463
* Clarify metadata instructions in Triton docs by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19457
* Fix Docs table number sorting by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19455
* Add examples for `plotting` reference by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19439
* Update SAM 2 research paper links by joshua-dean in https://github.com/ultralytics/ultralytics/pull/19465
* Clean up Raspberry Pi CI runners by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19478
* `ultralytics 8.3.81` Fix Metrics `on_plot` circular references by RemiPT in https://github.com/ultralytics/ultralytics/pull/19318

New Contributors
* joshua-dean made their first contribution in https://github.com/ultralytics/ultralytics/pull/19465
* RemiPT made their first contribution in https://github.com/ultralytics/ultralytics/pull/19318

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.80...v8.3.81

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📊 Key Changes (bullet points highlighting any major changes)
- 🔄 **Model Export Enhancements**: Fixed YOLO-NAS export by integrating default configs (`DEFAULT_CFG_DICT`) into model attributes for higher flexibility.
- 🧠 **RBOX Regularization**: Refined bounding box angle calculations for consistency in the OpenCV definition.
- 📋 **Documentation Upgrades**: Introduced sortable tables for performance data to improve clarity and usability across pages.
- 🔧 **Framework Compatibility**: Constrained OpenVINO dependency to version `>=2024.0.0,<2025.0.0` and updated outdated function calls for compatibility.
- 🐳 **Docker Workflow Fix**: Removed deprecated `numpy` dependency to resolve CI errors and streamlined build conditions.

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🎯 Purpose & Impact (bullet points explaining any benefits and potential impact to users)
- 🌟 **Improved Export Reliability**: The configuration merging for YOLO-NAS ensures a more robust process, minimizing errors during model deployment and export.
- 🧮 **Better Prediction Accuracy**: Regularizing rboxes improves detections by addressing angle inconsistencies in bounding boxes.
- 🖱️ **Enhanced User Experience**: Sortable tables in documentation allow users to interactively organize performance metrics, making comparisons easier.
- ✅ **Smoother OpenVINO Exports**: Version constraints and function updates enhance stability and prepare the platform for future releases without breaking changes.
- 🔄 **Cleaner CI Pipelines**: Removing deprecated dependencies ensures more reliable builds, benefitting developers contributing to the codebase.

This update underscores Ultralytics' focus on improving usability, compatibility, and export-related functionalities for smoother workflows and precise model performance. 🚀

What's Changed
* Optimize Sony IMX500 doc by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19421
* Constrain OpenVINO versions to `>=2024.0.0,<2025.0.0` by ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/19122
* New Docs sortable tables by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19376
* Fixing tflite export CI error by ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/19422
* Fix rbox regularization by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19429
* `ultralytics 8.3.80` Fix YOLO-NAS export by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19426


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.79...v8.3.80

🌟 Summary
The **v8.3.79** release of the Ultralytics YOLO framework introduces crucial bug fixes, enhancements to performance, and documentation updates. The primary focus is on correcting HSV augmentation mechanics and refining various code components for better reliability and usability. 🛠️✨

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📊 Key Changes
- **HSV Augmentation Fix**: Resolved incorrect hue, saturation, and value shifts during data augmentation, ensuring consistent color transformations. 🎨
- **YOLO12 Benchmark Refinement**: Fixed performance metrics for YOLO12 models in documentation, updating speed and mAP comparisons ensuring accurate representations. 📈
- **Docker Streamlining**: Removed redundant `tensorrt-cu12` dependency and added environment checks for better CI validation and Docker compatibility. 🐳
- **Comet Integration Fix**: Corrected class mapping index misalignment in Comet logging to avoid errors and misinterpretations in visualized data. 📋
- **Single-Class Model Consistency**: Enforced single-class model output alignment by adjusting `nc` and `names` parameters during training. 🚀
- **Documentation Updates**: Improved accuracy in multiple areas, embedding a YouTube interactive guide for YOLO12, and updating outdated citation links for research credibility. 📚
- **General Code Cleanup**: Enhanced readability by adhering to PEP8 conventions (`snake_case`), simplifying code for maintainability. 🖋️

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🎯 Purpose & Impact
- **Improved Data Augmentation**: Provides users with accurate HSV augmentations, leading to better model robustness and performance during training.
- **Benchmark Accuracy**: Ensures users receive correct performance metrics allowing better model selection and easier comparison across versions.
- **Enhanced Docker Usability**: Smoother image builds and reduced dependency issues result in a simplified development experience. Perfect for CI pipelines!
- **Better Logging**: Developers benefit from accurate class mappings in Comet analytics, avoiding confusion when examining predictions.
- **Single-Class Models Alignment**: Offers consistency in model behavior, improving usability for tasks like binary classification.
- **Improved Documentation**: Users gain easier access to tutorials, accurate technical references, and a better learning experience overall.
- **Cleaner Codebase**: Enhances long-term maintainability and aligns code with modern Python standards, beneficial for both contributors and end-users.

This release makes strides in improving accuracy, user experience, and usability for developers and researchers alike. 🚀🌟

What's Changed
* Fix YOLO12 cfg links by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19332
* Fix `mkdocs_github_authors.yaml` author LexBarou by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19334
* Fix keypoints data display in `pose.md` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19337
* Fix YOLO12 performance table by Laughing-q in https://github.com/ultralytics/ultralytics/pull/19333
* Fix benchmark.js by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19351
* Fix TensorRT export issue by removing redundant package by lakshanthad in https://github.com/ultralytics/ultralytics/pull/19343
* Add https://youtu.be/SMSJvjUG1ko to `yolo12.md` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19358
* Update model_data.py authors by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19365
* Assign variable `dt` before reference in exception block by sjhpark in https://github.com/ultralytics/ultralytics/pull/19349
* Use `chart.jslatest` by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19372
* YOLOv3 paper URL update by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19378
* Add `imx` export tip by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19404
* Det models with `nc=1` when `single_cls=True` by kevinconka in https://github.com/ultralytics/ultralytics/pull/19381
* Fixed Comet integration to use class map aligned index when trying to get class name by yaricom in https://github.com/ultralytics/ultralytics/pull/19408
* Simplify `auto_annotate` function by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/19400
* Updates `imgToAnns` variable to `snake_case` by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/19402
* Fix `Grad strides do not match bucket view strides` warning for YOLO12 DDP training by Laughing-q in https://github.com/ultralytics/ultralytics/pull/19395
* Updates `scaleFill` argument to `snake_case` by Burhan-Q in https://github.com/ultralytics/ultralytics/pull/19401
* Check environment in docker.yml by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19418
* Disable Dockerfile tests temporarily by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19420
* Fix incorrect metrics when nothing is detected by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19362
* `ultralytics 8.3.79` Fix shift in HSV augmentation by picsalex in https://github.com/ultralytics/ultralytics/pull/19311

New Contributors
* picsalex made their first contribution in https://github.com/ultralytics/ultralytics/pull/19311
* kevinconka made their first contribution in https://github.com/ultralytics/ultralytics/pull/19381
* sjhpark made their first contribution in https://github.com/ultralytics/ultralytics/pull/19349

**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.78...v8.3.79

🌟 Summary
This release, `v8.3.78`, brings an exciting new model to the family: **YOLO12** 🚀, featuring an attention-centric design for superior accuracy and efficiency across a variety of computer vision tasks.

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📊 Key Changes
- **Introduction of YOLO12 Models**:
- **New Architecture**: Incorporates attention mechanisms like **Area Attention**, **R-ELAN**, and **FlashAttention** for optimized performance.
- **Comprehensive Task Support**:
- Object detection, segmentation, pose estimation, classification, and oriented bounding box (OBB) detection.
- **Benchmark Improvements**:
- Demonstrates higher mAP (mean Average Precision) and efficiency compared to YOLO10/YOLO11 and competitors like RT-DETR.

- **Model-Specific Enhancements**:
- Introduced multiple YOLO12 variants (`n`, `s`, `m`, `l`, `x`) catering to different computing environments such as cloud systems and edge devices.
- Added new task-focused configurations for image classification, pose estimation, and segmentation.

- **Documentation Updates**:
- YOLO12 now included in detailed model documentation with performance metrics and usage examples.
- Extensive references, including benchmarks for comparison with leading global detection models.

- **Code Simplifications and Bug Fixes**:
- **ONNX Run-Time Fixes**: Improved device handling and tensor reshaping for ONNX users.
- **TFLite Export Cleanup**: Removed redundant parameters for simpler TensorFlow Lite export logic.
- **Code Refinement**: Enhanced readability and maintainability across inference and export pipelines.

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🎯 Purpose & Impact
- **Purpose**:
- YOLO12 brings a **paradigm shift** in accuracy and efficiency by adopting attention mechanisms tailored for real-time object detection.
- Streamlines codebase for easier maintenance and integration in diverse projects.

- **Impact**:
- Developers gain access to cutting-edge **state-of-the-art models** excelling in versatility, speed, and precision.
- Tasks like multi-object detection, segmentation, and pose estimation become more accessible for smaller devices (e.g., edge devices).
- Improved user experience with **easier model selection**, robust export support, and refined prediction outputs.

🔮 This update is not only a leap forward in technological advancement but also a commitment to making **intelligent vision accessible to all**.

What's Changed
* Remove unused parameters from `export_tflite` by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19319
* Fix ONNX RuntimeError with dynamic WorldModel by Y-T-G in https://github.com/ultralytics/ultralytics/pull/19322
* Add YOLO12 model info by Laughing-q in https://github.com/ultralytics/ultralytics/pull/19328
* Add https://youtu.be/BPYkGt3odNk to docs by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19331
* Refactor with simplifications by glenn-jocher in https://github.com/ultralytics/ultralytics/pull/19329
* update `model_data.py` by RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/19330
* `ultralytics 8.3.78` new YOLO12 models by Laughing-q in https://github.com/ultralytics/ultralytics/pull/19325


**Full Changelog**: https://github.com/ultralytics/ultralytics/compare/v8.3.77...v8.3.78