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PyPI · #1672 most downloaded on PyPI
Ultralytics YOLO 🚀 for SOTA object detection, instance segmentation, semantic segmentation, depth estimation, classification, pose estimation, oriented object detection, and multi-object tracking.
Last release today
19 Sep 2026
Ships on a steady schedule
a new release about every 8 days
Nearly every release is documented
notes for 60 of the last 60 stable releases
11 versions withdrawn
withdrawn after publishing
4 years old
831 releases · first in 2022
Stability-focused patch: pins TensorFlow for reliable exports, streamlines Docker images (including a new export-ready image), improves YOLOE class ha
Stability-focused patch: pins TensorFlow for reliable exports, streamlines Docker images (including a new export-ready image), improves YOLOE class handling, and tightens CI to reduce flakiness. 🚀
Tip: If you export to TensorFlow, ensure your environment respects tensorflow<=2.19.0 and numpy<2.0.0 to match this release. 📤✨
set_classes and support text prompt CLI usage by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21667click version lock as 8.2.2 was yanked by @onuralpszr in https://github.com/ultralytics/ultralytics/pull/21671yolo checks spacing by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/21676ultralytics 8.3.179 Pin tensorflow<=2.19.0 for exports by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/21680Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.178...v8.3.179
One column per quarter.
Lighter, faster Docker images with a clear split between a minimal Python runtime and a full export toolkit, plus improved build workflows, headless d
Lighter, faster Docker images with a clear split between a minimal Python runtime and a full export toolkit, plus improved build workflows, headless deps, and small API enhancements for smoother installs and preprocessing. 🚀🐳
Enjoy faster, cleaner Docker workflows and more configurable pipelines—whether you’re prototyping with YOLO or exporting to edge backends. 🚀✨
opencv-python-headless in Dockerfiles by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/21647padding_value and interpolation in LetterBox for better compatibility by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21652ultralytics 8.3.178 new lighter Dockerfile-python images by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/21661Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.177...v8.3.178
Stronger supply chain transparency and compliance: SBOMs help with audits, vulnerability tracking, and enterprise procurement. 🔍✅
Automated SPDX SBOMs are now generated and attached to every release, improving security and compliance; plus enhanced TorchScript export flexibility with dynamic shapes and expanded MNN export testing. 🔐📦🧪
sahi-tiled-inference guide by @CoderUni in https://github.com/ultralytics/ultralytics/pull/21616dynamic=True for torchscript exports by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/21573- from parking-management.md FAQs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21640ultralytics 8.3.177 Add automated SPDX SBOM generation to releases by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/21643Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.176...v8.3.177
No expected breaking changes for most users. 🎉
Ultralytics 8.3.176 improves experiment tracking with Comet ML, streamlines YOLOE exports, and refactors tracking internals for cleaner, more flexible pipelines—plus a new CoreML video to speed up Apple deployments. 🚀📊
No expected breaking changes for most users. 🎉
Results object to filter candidates for tracker by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21536ultralytics 8.3.176 Comet integration fix for YOLO Detect and OBB models by @yaricom in https://github.com/ultralytics/ultralytics/pull/21613Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.175...v8.3.176
This release enhances model safety, improves documentation and logging, expands export testing, and updates example notebooks for a smoother and more
This release enhances model safety, improves documentation and logging, expands export testing, and updates example notebooks for a smoother and more reliable user experience. 🚦📚📝
This update is recommended for all users who want safer workflows, better documentation, and a more seamless experience with Ultralytics models and tools. 🚀
classes and total counts in solutions Logging by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21483--slow CI by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/21572queue-management notebook in docs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21489batch_size for TensorRT inference by @syedhamzamohiuddin in https://github.com/ultralytics/ultralytics/pull/21592ultralytics 8.3.175 YOLOE is_fused() check prior to setting classes by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/21605Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.174...v8.3.175
This release focuses on streamlining development workflows, improving reliability, and enhancing documentation across the Ultralytics ecosystem. 🚀🛠️
This release focuses on streamlining development workflows, improving reliability, and enhancing documentation across the Ultralytics ecosystem. 🚀🛠️
uv tool for much faster Python package installation ⚡Overall, this update makes the Ultralytics development process faster, more transparent, and more robust—benefiting both the community and end users. 🎉
ultralytics 8.3.174 Streamlined Conda package CI by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/21577Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.173...v8.3.174
This release improves how training metrics are logged to ClearML, making results easier to read and analyze. It also updates documentation tools for a
This release improves how training metrics are logged to ClearML, making results easier to read and analyze. It also updates documentation tools for a smoother developer experience. 🚀📚
mkdocs-ultralytics-plugin to version 0.1.26 for better documentation features.click library to version 8.2.1 to resolve a MkDocs build issue.mkdocs-ultralytics-plugin>=0.1.26 by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/21565click==8.2.1 to fix broken MkDocs build by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/21569ultralytics 8.3.173 Fix ClearML logging for metrics and val categories by @darouwan in https://github.com/ultralytics/ultralytics/pull/21549Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.172...v8.3.173
This release streamlines the file download process and updates documentation, making Ultralytics tools lighter and more user-friendly. 🚀
This release streamlines the file download process and updates documentation, making Ultralytics tools lighter and more user-friendly. 🚀
ultralytics 8.3.172 unify downloads progress TQDM by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/21563Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.171...v8.3.172
This release brings enhanced Docker workflow security with NVIDIA NGC authentication, a comprehensive new guide for deploying YOLO11 on Google Cloud V
This release brings enhanced Docker workflow security with NVIDIA NGC authentication, a comprehensive new guide for deploying YOLO11 on Google Cloud Vertex AI, improved object tracking consistency, and several documentation and usability updates. 🚀🔒
pydantic dependency for IMX model export to prevent compatibility issues, and clarified export file naming in the documentation. 📦📝Overall, this update strengthens security, usability, and documentation, while making advanced deployment and tracking features more accessible to the community. 🌍✨
metrics.py by @nikunjlad in https://github.com/ultralytics/ultralytics/pull/21526pydantic<=2.11.7 by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21537ultralytics 8.3.171 add NVIDIA NGC auth to Docker Action by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/21556Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.170...v8.3.171
This release streamlines batch processing and inference for Ultralytics models—especially with OpenVINO—while boosting reliability, transparency, and
This release streamlines batch processing and inference for Ultralytics models—especially with OpenVINO—while boosting reliability, transparency, and user experience across the platform. 🚀
Overall, this update makes Ultralytics models easier to use, more robust, and better documented—helping both new and experienced users get the most out of their AI projects! 🌟
NoneType error while reading image by @nikunjlad in https://github.com/ultralytics/ultralytics/pull/21467batch>1 by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21508ultralytics 8.3.170 Eliminate batch argument for AutoBackend class and fix OpenVINO inference by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21452Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.169...v8.3.170
This release introduces powerful new visualizations for model evaluation, making it easier to understand and debug your results across Detect, Segment
This release introduces powerful new visualizations for model evaluation, making it easier to understand and debug your results across Detect, Segment, Pose, and Oriented Bounding Box (OBB) tasks. 🖼️✨
visualize Option: Added a visualize=True argument for validation commands, enabling these detailed visual breakdowns.Overall, this update brings valuable tools for both beginners and experts to better understand, visualize, and improve their computer vision projects with Ultralytics. 🚀
predictor args while getting model names by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21443max_det for inference by @atalaydenknalbant in https://github.com/ultralytics/ultralytics/pull/21457optimization_for_gpu_delegate flag for TF exports by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/21482ultralytics 8.3.169 Add GT, TP, FP, FN visualization for Detect, Segment, Pose and OBB by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/18868Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.168...v8.3.169
Ultralytics 8.3.168 delivers a major upgrade to how YOLO models export predictions, introduces a unified annotation method, and enhances both document
Ultralytics 8.3.168 delivers a major upgrade to how YOLO models export predictions, introduces a unified annotation method, and enhances both documentation and user-facing tools for a smoother, more consistent experience across the platform. 🚀✨
circle_label and text_label with a single, flexible adaptive_label method for drawing labels (circle or rectangle) on images, simplifying both code and usage.adaptive_label method makes it easier for users and developers to annotate images, with less code and more flexibility.This release is all about making Ultralytics tools more powerful, intuitive, and accessible for everyone—from researchers and developers to everyday users. 💡🖼️🛠️
Ultralytics YOLO11 docs citation section by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21407circle_label and text_label into adaptive_label by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21377text_model module import for Solutions by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21436image inference support to Streamlit application by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21413ultralytics 8.3.168 Optimize unnecessary native-space calculation by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21379Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.167...v8.3.168
This release enhances Sony IMX device support, improves pose estimation export, and makes visual prompting workflows smoother—while also refining docu
This release enhances Sony IMX device support, improves pose estimation export, and makes visual prompting workflows smoother—while also refining documentation and confusion matrix plotting. 🚀🤖
mct-quantizers>=1.6.0 as a required dependency for IMX model export to ensure compatibility.In summary:
This update makes Ultralytics models more versatile on Sony IMX devices, simplifies visual prompting, and improves the overall user experience for both developers and non-experts. 🎉
self.names overwriting in ConfusionMatrix by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21356visual_prompts when running prediction with refer_image by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/21368IMX export and inference for Pose Estimation by @ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/20196ultralytics 8.3.167 Fix Sony IMX export mct-quantizers>=1.6.0 dependency by @ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/21404Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.166...v8.3.167
This release focuses on improving dataset handling and clarity, with a major update to the VisDrone dataset structure and conversion process, along wi
This release focuses on improving dataset handling and clarity, with a major update to the VisDrone dataset structure and conversion process, along with several refinements to dataset configurations and evaluation logic. 🗂️✨
images/train, images/val, and images/test.Overall, this update streamlines dataset usage, improves evaluation reliability, and enhances the user experience for both development and documentation. 🚀
tiger-pose.yaml download size by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21355ultralytics 8.3.166 Standardize VisDrone autodownload structure by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/21367Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.165...v8.3.166
This release standardizes the folder structure for datasets in Ultralytics, making dataset paths more consistent and user-friendly across multiple YAM
This release standardizes the folder structure for datasets in Ultralytics, making dataset paths more consistent and user-friendly across multiple YAML configuration files. 📁✨
images/train, images/val, and images/test.Overall, this update improves the reliability and usability of dataset management, helping users work more efficiently with Ultralytics models and tools.
ultralytics 8.3.165 Update datasets YAML's by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21353Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.164...v8.3.165
This release delivers a critical fix to YOLO detection validation metrics, improves dataset flexibility, enhances export reliability, and polishes doc
This release delivers a critical fix to YOLO detection validation metrics, improves dataset flexibility, enhances export reliability, and polishes documentation and developer experience. 🚀
max_samples parameter for controlling the number of text samples in GroundingDataset and improved logic for negative text selection.valid/ as a fallback validation folder, improving compatibility with Roboflow and similar dataset exports.numpy.ndarray to np.ndarray across code and documentation for clarity.pillow_heif to pi-heif for HEIC image decoding, simplifying license compliance.sweep_annotator method.This update is recommended for all users, especially those validating detection models, working with custom datasets, or exporting models for deployment. 🚀🛠️
max_samples value between GroundingDataset and YOLOMultiModalDataset by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/21286valid/ as fallback validation folder for classification datasets by @JamesBond6873 in https://github.com/ultralytics/ultralytics/pull/21321classify_augmentations by @Toprak2 in https://github.com/ultralytics/ultralytics/pull/21322CSS from similarity-search.html by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21335sweep_annotator example by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21331pillow_heif to pi_heif by @picsalex in https://github.com/ultralytics/ultralytics/pull/21339ultralytics 8.3.164 Fix swapped mAP50 and mAP50-95 in COCOEval stats by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/21350Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.163...v8.3.164
This release brings smarter label validation, improved region counting, easier video frame management, and enhanced export and CI workflows for a smoo
This release brings smarter label validation, improved region counting, easier video frame management, and enhanced export and CI workflows for a smoother and more reliable Ultralytics experience. 🚀✨
For All Users:
For Developers & Advanced Users:
Overall, this update makes Ultralytics tools more robust, user-friendly, and ready for diverse real-world applications! 🌍💡
region initialization every frame ~2x faster by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21288save_txt and save_frames by @jugal-sheth in https://github.com/ultralytics/ultralytics/pull/21276onnxslim>=0.1.59 to TOML export dependencies by @inisis in https://github.com/ultralytics/ultralytics/pull/21302onnxslim from pyproject.toml due to Jetson6 docker tests failing by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/21304ultralytics 8.3.163 Add 1% tolerance for labels normalization check by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21310Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.162...v8.3.163
This release (v8.3.162) brings improved reliability and consistency to model loading, enhanced hardware compatibility, and several quality-of-life upd
This release (v8.3.162) brings improved reliability and consistency to model loading, enhanced hardware compatibility, and several quality-of-life updates for both developers and users. 🚀🛠️
torch.load are replaced with Ultralytics' torch_load utility, ensuring consistent and robust model file handling throughout the codebase.ai-edge-litert package is now pinned to versions >=1.2.0,<1.4.0 to ensure stable TensorFlow SavedModel exports.torch_load, users and developers benefit from fewer bugs and more predictable behavior when working with PyTorch models.Overall, v8.3.162 delivers a more robust, user-friendly, and developer-friendly experience across the Ultralytics ecosystem! 🎉
is_lvis check for open-vocabulary models evaluation by @ImJaewooChoi in https://github.com/ultralytics/ultralytics/pull/21245Pose and Segment tasks by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21259DATASET_DIR relative path compatibility for grounding datasets by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21256CopyPaste augmentation by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/21262ai-edge-litert>=1.2.0,<1.4.0 by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21265ultralytics 8.3.162 Replace torch.load calls with patched torch_load method that defaults to weights_only=False by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/21260Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.161...v8.3.162
Deprecation Warning for Examples: Added a clear notice that community-contributed examples will be retired in Ultralytics v8.4.0, encouraging users to…
This release focuses on making dataset paths simpler and more consistent across Ultralytics, improving documentation, and enhancing compatibility for various platforms and users. 🗂️✨
coco) instead of legacy relative paths (like ../datasets/coco).datasets_dir locations.This update is all about making Ultralytics easier to use, more robust, and ready for a wider range of users and devices! 🚀
final_mixed_train_no_coco_segm in GroundingDataset by @ImJaewooChoi in https://github.com/ultralytics/ultralytics/pull/21215ultralytics/examples by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/21208visdrone2yolo function by @banu4prasad in https://github.com/ultralytics/ultralytics/pull/21226paddlepaddle==3.0.0 for ARM64 by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21238summary method in val.md by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21236BaseSolution inheritance in VisualAISearch by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21240ultralytics 8.3.161 Eliminate ../datasets/ in data yaml files for better datasets_dir compatibility by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21091Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.160...v8.3.161
This release brings improved keypoint handling, smarter data augmentation, and enhanced usability for training and exporting models, making YOLO workf
This release brings improved keypoint handling, smarter data augmentation, and enhanced usability for training and exporting models, making YOLO workflows more robust and user-friendly. 🚀
Overall, this update delivers a more stable, accurate, and user-friendly experience for anyone training, validating, or deploying YOLO models. 🎉
0.5 threshold by @WillieMaddox in https://github.com/ultralytics/ultralytics/pull/21165CustomizedValidator by @picsalex in https://github.com/ultralytics/ultralytics/pull/21196metrics summary method to val logs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21203nms=True by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/21190generate_text_embeddings for DDP training by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21210SolutionResults attributes in index.md and classwise_counts typo fix by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21216lxml dependency by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21221ultralytics 8.3.160 Clip keypoints for better visualization control by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21220Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.159...v8.3.160
This release refactors and unifies the COCO evaluation process across detection, segmentation, and pose models, streamlining the codebase and improvin
This release refactors and unifies the COCO evaluation process across detection, segmentation, and pose models, streamlining the codebase and improving maintainability. It also brings enhancements to object counting, similarity search, NVIDIA Jetson benchmarking, and dependency management. 🛠️✨
coco_evaluate method now shared by detection, segmentation, and pose validators, reducing code duplication and ensuring consistent metric reporting.save_dir) is now included in detection model validation metrics, making experiment tracking and result management easier.save_dir in metrics helps users organize and reproduce results more efficiently.Overall, this release focuses on making the Ultralytics ecosystem more robust, user-friendly, and future-proof for both developers and end users. 🚀
show_in=False and show_out=False by @fn-hide in https://github.com/ultralytics/ultralytics/pull/21047save_dir to Metrics for better access by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21136TextModel class for similarity search by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21114ultralytics 8.3.159 pin model-compression-toolkit>=2.3.0,<2.4.1 by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21161ultralytics 8.3.159 Refactor and Clean up COCO evaluation by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21172Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.158...v8.3.159
This release streamlines image classification workflows and enhances the model selection experience in the Streamlit inference tool, making both more
This release streamlines image classification workflows and enhances the model selection experience in the Streamlit inference tool, making both more reliable and user-friendly. 🖼️🚀
Overall, this update delivers a smoother, more accurate, and user-friendly experience for both developers and end users working with Ultralytics models and tools.
Streamlit solution by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21058ultralytics 8.3.158 Eliminate classification legacy transforms by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21089Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.157...v8.3.158
Future-Proofing: Moves away from deprecated tools (pycocotools) to a modern, actively maintained alternative, ensuring long-term stability.
This release introduces much faster and more modern COCO/LVIS evaluation by integrating the faster-coco-eval library, along with several improvements for segmentation, pose, dataset handling, documentation, and dependency management. 🚀
pycocotools with the high-speed faster-coco-eval library for COCO and LVIS dataset evaluation, resulting in up to 4.5x faster validation. ⚡pycocotools) to a modern, actively maintained alternative, ensuring long-term stability.Overall, this update makes Ultralytics models faster, more reliable, and easier to use—whether you're training, validating, or just getting started! 🚀✨
flask>=3.0.1 for similarity search solution by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21112verify_labels for GroundingDataset by @mohiuddin-khan-shiam in https://github.com/ultralytics/ultralytics/pull/21095macOS runners and pin OpenVINO>=2025.2.0 for macos-15 by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20471ultralytics 8.3.157 Add faster-coco-eval package for COCO/LVIS evaluation by @MiXaiLL76 in https://github.com/ultralytics/ultralytics/pull/17020Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.156...v8.3.157
This release enhances model export reliability—especially for TensorRT INT8 quantization—improves data handling for training and calibration, and brin
This release enhances model export reliability—especially for TensorRT INT8 quantization—improves data handling for training and calibration, and brings documentation and example updates for a smoother user experience. 🚀
dynamic=True during INT8 export, making the process more flexible.drop_last option, letting users drop incomplete batches during data loading for both training and export workflows.requirements.txt for easier setup.This update is recommended for all users who export models, work with INT8 quantization, or want improved examples and documentation. 🚀🛠️
plot_images for classify training by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21072ultralytics 8.3.156 Eliminate dynamic=True enforcement for TensorRT INT8 export by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20989Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.155...v8.3.156
This release (v8.3.155) delivers a key bug fix for YOLO-World models, improves developer experience with enhanced type annotations, and updates docume
This release (v8.3.155) delivers a key bug fix for YOLO-World models, improves developer experience with enhanced type annotations, and updates documentation and learning resources. 🚀📚
set_classes() method after running predictions, ensuring smoother workflow when updating classes dynamically.batch parameter supports both integers and floats, helping users leverage advanced batch size features.Overall, this update enhances reliability, developer experience, and learning resources for the Ultralytics community! 🌟
Solutions by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21057ultralytics 8.3.155 Fix YOLO-World set_classes() error after prediction by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/21051Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.154...v8.3.155
This release refactors and unifies the validation and metrics system across all YOLO tasks, making the codebase more modular, maintainable, and consis
This release refactors and unifies the validation and metrics system across all YOLO tasks, making the codebase more modular, maintainable, and consistent. It also brings UI improvements, performance boosts, and enhanced user feedback across the Ultralytics ecosystem. 🛠️✨
Major Validator & Metrics Refactor
Semantic Image Search UI Upgrade
Performance & Stability Improvements
.cpu() calls and adding caching for faster angle calculations.User Experience Enhancements
Bug Fixes
For Developers:
For End Users:
For the Community:
In summary:
This update makes Ultralytics' YOLO models and tools more powerful, user-friendly, and future-proof—whether you're building on the platform or using it for your projects! 🚀
similarity-search by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21032.cpu() call and add @lru_cache by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21022settings by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21031imgsz for dynamic models by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/21019model.fuse() to maintain NVIDIA JetPack 5 and below compatibility by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/21028ultralytics 8.3.154 Refactor Validator and Metrics classes by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/21009Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.153...v8.3.154
This release introduces comprehensive OpenVINO benchmark results for YOLO11 models on the latest Intel® Core™ Ultra™ 7 265K hardware, alongside improv
This release introduces comprehensive OpenVINO benchmark results for YOLO11 models on the latest Intel® Core™ Ultra™ 7 265K hardware, alongside improved documentation, more accurate benchmarking data for Raspberry Pi and Rockchip devices, enhanced tracking examples, and a key fix for per-class metric reporting. 🚀
This update is especially valuable for developers and researchers deploying YOLO11 models on modern Intel chips or edge devices, and for anyone seeking reliable, easy-to-understand benchmarking and tracking workflows.
trackers README.md by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20986rockchip-rknn.md by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/21006metrics.summary by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20995ultralytics 8.3.153 OpenVINO COCO128 benchmarks by @ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/20877Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.152...v8.3.153
Enhanced segmentation mask accuracy, improved memory management, faster confusion matrix processing, and better documentation for Ultralytics YOLO11.
Enhanced segmentation mask accuracy, improved memory management, faster confusion matrix processing, and better documentation for Ultralytics YOLO11. 🚀🖼️
Overall, this release delivers a smoother, more reliable, and more accessible experience for all Ultralytics YOLO11 users! 🎉
normalize modes for confusion matrix (≤30 classes) by @mihlefeld in https://github.com/ultralytics/ultralytics/pull/20955ultralytics 8.3.152 Optimize padding for Segment mask processing by @horsto in https://github.com/ultralytics/ultralytics/pull/20957Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.151...v8.3.152
This release brings cleaner, more customizable metric reporting across all tasks, improved tracking reliability, and efficiency boosts for video proce
This release brings cleaner, more customizable metric reporting across all tasks, improved tracking reliability, and efficiency boosts for video processing. It also enhances documentation and testing for a smoother user experience. 🚀✨
decimals and normalize options to all metric summary() methods, letting users control the precision and format of evaluation results.Overall, this update delivers a more polished, efficient, and user-friendly experience for anyone working with Ultralytics models and solutions!
solutions extras in uv export install by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20956TrackZone by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20928ultralytics 8.3.151 Add decimals argument to Metrics by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20952Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.150...v8.3.151
This release streamlines model validation, improves memory efficiency for oriented bounding box (OBB) tasks, enhances documentation clarity, and fixes
This release streamlines model validation, improves memory efficiency for oriented bounding box (OBB) tasks, enhances documentation clarity, and fixes usability issues in the parking management solution. 🚀
pytorch-cpu) for Conda-based continuous integration tests, ensuring compatibility on machines without GPUs.Overall, this update delivers a smoother development and user experience, with optimizations that benefit both everyday users and contributors.
parking annotator by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20926pytorch-cpu for Conda CI tests by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20931ultralytics 8.3.150 Set default conf=0.01 for OBB validation by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20923Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.149...v8.3.150
This release enhances model export compatibility, improves usability for video and visualization workflows, and expands documentation and reporting fe
This release enhances model export compatibility, improves usability for video and visualization workflows, and expands documentation and reporting features across Ultralytics tools. 🚀
plot() method, ensuring all options are up-to-date and easy to find.✨ This update is all about making Ultralytics tools more robust, user-friendly, and ready for deployment across a wider range of platforms and workflows!
normalize and decimals support for confusion matrix export by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20912visualization-args.md by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20910yolo11n model support for IMX format in export table by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20884waitKey() and destroyAllWindows() to Predictor by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20913ultralytics 8.3.149 Fix group-convolutions export bug for Edge TPU by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20919Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.148...v8.3.149
This release updates key dependencies and improves error handling, making model exports more reliable and troubleshooting easier for users. 🚀🔧
This release updates key dependencies and improves error handling, making model exports more reliable and troubleshooting easier for users. 🚀🔧
onnxslim dependency to version 0.1.56 for ONNX and TensorFlow SavedModel exports, ensuring compatibility with the latest features and bug fixes.numpy._core error while loading cache by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20904ultralytics 8.3.148 update onnxslim>=0.1.56 dependency by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20905Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.147...v8.3.148
This release brings powerful new ways to export confusion matrix results, improved YOLOv7 ONNX/TensorRT inference support, enhanced OpenVINO documenta
This release brings powerful new ways to export confusion matrix results, improved YOLOv7 ONNX/TensorRT inference support, enhanced OpenVINO documentation for YOLO11, and several usability and documentation updates. 📊🚀
Confusion Matrix Export Enhancements
YOLOv7 ONNX & TensorRT Inference Support
OpenVINO Documentation Update for YOLO11
Prediction Arguments & OBB Documentation
rect argument, explaining its effect on image padding and inference speed.BatchNorm Initialization Fix
Training Parameter Docstring Update
batch instead of batch_size.Easier, Flexible Analysis
Broader Model Compatibility
Up-to-date Hardware Guidance
Improved Usability
More Reliable Training
In summary:
This update makes validation results more accessible, expands model and hardware support, and improves the overall user experience for both developers and non-experts. 🎉
rect to prediction arguments by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20857yaml by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/18149batch parameter by @erfan-zekri in https://github.com/ultralytics/ultralytics/pull/20888ultralytics 8.3.147 Confusion Matrix export to CSV, XML, HTML, JSON, and SQL formats by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20834Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.146...v8.3.147
Added a deprecation notice for Neural Magic integrations.
Ultralytics 8.3.146 introduces full support for grayscale object detection workflows, highlighted by the new COCO8-Grayscale dataset, a dedicated grayscale YOLO11n model, and comprehensive grayscale testing and documentation. 🖤📦
yolo11n-grayscale.pt model is now available for download and use.In summary:
This release makes Ultralytics a more versatile platform for both color and grayscale object detection, while also delivering a range of usability, performance, and documentation improvements. 🚀
{} instead of set([]) by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20803r_s variable for intersection check by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20829save_crop from validation arguments by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20821NoneType result when using ReID with CLI by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20814uv always installing to --system environment by @Burhan-Q in https://github.com/ultralytics/ultralytics/pull/20837ultralytics 8.3.146 New COCO8-Grayscale dataset by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20827Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.145...v8.3.146
This release brings major improvements to model benchmarking flexibility, chart interactivity in documentation, and tracking code clarity—making Ultra
This release brings major improvements to model benchmarking flexibility, chart interactivity in documentation, and tracking code clarity—making Ultralytics tools easier and more powerful for everyone! 🚀📊
benchmark method now accepts data, format, and verbose directly, and supports all export-specific arguments for more customizable benchmarking.is_track property for easier and more consistent tracking checks across code, examples, and docs.verbose argument in benchmark documentation.is_track property standardizes tracking checks, reducing bugs and improving code clarity.Enjoy the new features and improvements! If you have feedback or questions, the Ultralytics community is here to help. 💡
chart-widget.js by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20794is_track attribute by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20792verbose argument description in benchmark doc by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/20791ultralytics 8.3.145 Support all export arguments for benchmark by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20257Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.144...v8.3.145
This release focuses on improved code clarity and reliability, introducing enhanced docstrings and type hints across the Ultralytics codebase, smarter
This release focuses on improved code clarity and reliability, introducing enhanced docstrings and type hints across the Ultralytics codebase, smarter GPU selection, and documentation updates for better user experience. 📝🚀
select_idle_gpu function now considers both free memory and GPU utilization, allowing for more efficient and conflict-free device selection. Users can customize thresholds via environment variables. (PR #20780)✨ This update makes Ultralytics more robust, user-friendly, and ready for both production and research use!
iou in validation docs by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20776ultralytics 8.3.144 Declarative docstrings and type hints by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20777Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.143...v8.3.144
This release introduces detailed performance profiling to Ultralytics Solutions, giving users clear insights into how fast tracking and processing ste
This release introduces detailed performance profiling to Ultralytics Solutions, giving users clear insights into how fast tracking and processing steps run, along with improved logging and more robust object tracking. 🚀⏱️
This update is especially valuable for anyone looking to monitor, debug, or optimize their use of Ultralytics Solutions.
is_obb usage from store_tracking_history by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20760ultralytics 8.3.143 Add Profiling for Solutions by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20730Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.142...v8.3.143
No Breaking Changes: These updates are behind-the-scenes improvements—there’s no change to how you use Ultralytics models or features day-to-day.
This release streamlines how bounding boxes are drawn and improves overall usability, robustness, and documentation clarity across the Ultralytics ecosystem. 🖼️✨
box_label function no longer requires a rotated parameter; it now automatically detects and handles both standard and oriented bounding boxes.🚀 This release is all about making Ultralytics tools smoother, smarter, and more user-friendly for everyone!
Minimum requirements in docs by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20743track_data and its id by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20741frozenset from ValueError logs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20746setMouseCallback where imshow not supported by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20753ultralytics 8.3.142 Simplify Annotator.box_label by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20754Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.141...v8.3.142
This update introduces automatic detection and seamless handling of RTDETR models, along with several improvements for GPU selection, code clarity, da
This update introduces automatic detection and seamless handling of RTDETR models, along with several improvements for GPU selection, code clarity, dataset accessibility, and overall robustness. 🚀
YOLO class now automatically recognizes and initializes RTDETR models from checkpoints, making it effortless to use RTDETR alongside other Ultralytics models.YOLO class, avoiding unnecessary duplication and saving memory.✨ This release makes Ultralytics models—especially RTDETR—even easier to use, more reliable, and more accessible for everyone!
TaskAlignedAssigner module by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20725Autodevice memory check by using percentage form by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20714loss.detach() temp return variable by @genji970 in https://github.com/ultralytics/ultralytics/pull/20721HomeObjects-3K notebook in docs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20732CenterCrop in Classify prediction by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20715ultralytics 8.3.141 Automatically detect RTDETR models by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20578Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.140...v8.3.141
This release brings improved reliability for package management, enhanced export and benchmarking features, clearer documentation, and new video tutor
This release brings improved reliability for package management, enhanced export and benchmarking features, clearer documentation, and new video tutorials to help users get the most out of Ultralytics YOLO models. 🚀
to_df and to_sql methods, allowing users to control normalization, decimal precision, and dynamic SQL table creation for easier data analysis and reporting.This update is all about making Ultralytics tools more reliable, flexible, and user-friendly—whether you're a developer, researcher, or just getting started with computer vision! 🚀✨
to_df method in DataExportMixin by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20706IS_PYTHON_3_11 constant by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20713to_sql method for validation metrics by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20702ultralytics 8.3.140 Fix uv checks with uv -V by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20710Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.139...v8.3.140
This release introduces a powerful new way to export YOLO validation metrics and prediction results in multiple formats, making it much easier to anal
This release introduces a powerful new way to export YOLO validation metrics and prediction results in multiple formats, making it much easier to analyze, share, and integrate your results. 📊✨
DataExportMixin class that lets you export metrics and results as DataFrame, CSV, XML, HTML, JSON, or directly to an SQLite database.Overall, this update is a big step forward for usability, efficiency, and integration in the Ultralytics ecosystem! 🚀
uv in check_requirements by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/18116ultralytics 8.3.139 New DataExportMixin class for Metrics and Results exports by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20546Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.138...v8.3.139
YOLO v8.3.138 introduces support for non-square image sizes in classification models, along with performance improvements, more robust file handling,
YOLO v8.3.138 introduces support for non-square image sizes in classification models, along with performance improvements, more robust file handling, and expanded testing for Ultralytics Solutions. 🚀🖼️
imgsz) image sizes, allowing users to specify rectangular images for inference and export.This release makes YOLO classification models more adaptable and the overall Ultralytics ecosystem more robust and user-friendly! 💡🛠️
85% and overall repo up by +0.8% by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20654maxsplit arg by @Burhan-Q in https://github.com/ultralytics/ultralytics/pull/20514ultralytics 8.3.138 Support non-square imgsz for Classify models by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/16630Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.137...v8.3.138
This release boosts training speed and efficiency for YOLOWorld and YOLOE models by optimizing how text features are cached and handled, alongside imp
This release boosts training speed and efficiency for YOLOWorld and YOLOE models by optimizing how text features are cached and handled, alongside improvements to ONNX export reliability and embedding computations. 🚀📚
build_text_model utility for modular and compatible text model handling.onnxslim dependency to version 0.1.53 for better ONNX and TensorFlow export support.Overall, this update makes training and deploying Ultralytics models more efficient, robust, and user-friendly for both developers and end users. 🚀✨
onnxslim>=0.1.53 and simplify when dynamic=True by @inisis in https://github.com/ultralytics/ultralytics/pull/20569ultralytics 8.3.137 YOLO-World text features cache optimization by @h13-0 in https://github.com/ultralytics/ultralytics/pull/20480Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.136...v8.3.137
This release makes Ultralytics lighter and easier to use by removing the seaborn dependency, improving plotting functions, and enhancing performance a
This release makes Ultralytics lighter and easier to use by removing the seaborn dependency, improving plotting functions, and enhancing performance and documentation. 🚀🧹
Overall, this update streamlines the Ultralytics experience, making it faster, easier, and more robust for everyone! 🎉
checks results to speed up multiple times calls by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20652opencv for solutions in __init__.py by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20665ultralytics 8.3.136 Eliminate seaborn depencency by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20509Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.135...v8.3.136
This release improves model export reliability, speeds up font handling, enhances pose documentation, and streamlines video processing for a smoother
This release improves model export reliability, speeds up font handling, enhances pose documentation, and streamlines video processing for a smoother user experience. 🚀
yolo predict.Overall, this update delivers a faster, more stable, and more user-friendly experience for Ultralytics users! 🌟
check_font result by @jonashaag in https://github.com/ultralytics/ultralytics/pull/20644cv2.waitKey in solutions for Docker tests by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20643pose.md by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20659refer_image by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20661ultralytics 8.3.135 TF.js fix with onnx>=1.12.0,<1.18.0 pin by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20640Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.134...v8.3.135
This release brings important improvements to YOLO tracking reliability, grayscale image support, dataset path handling, and model export stability. I
This release brings important improvements to YOLO tracking reliability, grayscale image support, dataset path handling, and model export stability. It also enhances code quality, test coverage, and user experience across Ultralytics tools. 🚀
Overall, this update makes Ultralytics tools more robust, user-friendly, and compatible with a wider range of workflows and environments. 🚀🖤
onnx>=1.12.0,<1.18.0 in pyproject.toml by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20608LOGGER.info in to_sql method and security-alarm solution by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20619defaultdict for classwise object counting by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20632to_sql and to_html in pytests test_results method by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20635defaultdict in AIGym by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20633grayscale models tracking on videos by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20634copy_paste for grayscale images by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20636ultralytics 8.3.134 Fix ReID duplicated hooks when persist is False by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20618Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.133...v8.3.134
This release improves error handling for dataset loading, enhances NVIDIA Jetson support, and brings several usability and reliability updates to Ultr
This release improves error handling for dataset loading, enhances NVIDIA Jetson support, and brings several usability and reliability updates to Ultralytics models. 🚀🛠️
Overall, this update makes Ultralytics models more user-friendly, robust, and ready for a wider range of deployment scenarios. 🚀✨
fitness from nan values by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20599boats.jpg for obb predict with Python code example by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20602coco8.yaml instead of dota8.yaml by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20592Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.132...v8.3.133
This release brings smarter model weight loading for multi-channel training, introduces the HomeObjects-3K indoor dataset, and enhances object countin
This release brings smarter model weight loading for multi-channel training, introduces the HomeObjects-3K indoor dataset, and enhances object counting, segmentation, and documentation for a more robust and user-friendly Ultralytics experience. 🚀🏠
Smarter Model Weight Loading:
New HomeObjects-3K Dataset:
Object Counting for Rotated Boxes (OBB):
Improved Segmentation Mask Handling:
Unified Dataset Handling & Validation:
Branding & Documentation Updates:
Streamlined CI & Maintenance:
Greater Flexibility:
Expanded Dataset Choices:
Improved Tracking & Counting:
Better User Experience:
Consistency & Professionalism:
✨ This update is packed with improvements that make Ultralytics YOLO models more adaptable, user-friendly, and ready for advanced computer vision tasks—whether you're working in research, industry, or just getting started!
YOLOv8 references to Ultralytics YOLO in docstrings by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20563masks from segmentation model by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20584OBB task by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20585kpt_shape validation in Pose Trainer with clear error handling by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20547ultralytics 8.3.132 Always transfer Conv layer pretrained weights by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20567Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.131...v8.3.132
This release brings enhanced support for grayscale images, improved handling of large and transparent images, a new YOLO11 C++ Triton Inference exampl
This release brings enhanced support for grayscale images, improved handling of large and transparent images, a new YOLO11 C++ Triton Inference example, and important licensing and workflow updates. 🖤🖼️🚀
Grayscale Image Support:
channels parameter to data loaders, enabling seamless inference and visualization with grayscale (single-channel) images.Large & Transparent Image Handling:
YOLO11 Triton C++ Example:
Licensing & Workflow Improvements:
Broader Dataset Compatibility:
Robust Image Processing:
Production-Ready Deployment:
Open-Source Clarity:
This update makes Ultralytics tools more flexible, robust, and ready for advanced real-world use cases! 🚀
>178.9M pixels by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20575ultralytics-actions>=0.0.73 by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20581ultralytics 8.3.131 Optimize grayscale model inference pipeline by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20565Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.130...v8.3.131
This release boosts model initialization speed by optimizing how model layers are fused, adds clearer training metric access for users, and strengthen
This release boosts model initialization speed by optimizing how model layers are fused, adds clearer training metric access for users, and strengthens ONNX export testing and workflow security. 🚀
model.fuse() process by performing layer fusion on the CPU before moving data to the GPU, making model startup more efficient.on_model_save callback to access and print key training metrics after each checkpoint.Overall, this update makes Ultralytics models faster to use, easier to monitor during training, and more robust for deployment and development.
on_model_save callback with Python example to Callbacks docs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20531permissions by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20556ultralytics 8.1.130 Faster model.fuse() operations by @dianyo in https://github.com/ultralytics/ultralytics/pull/20466Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.129...v8.3.130
This release brings smarter data augmentation, improved export and inference reliability, and clearer documentation—making model training and deployme
This release brings smarter data augmentation, improved export and inference reliability, and clearer documentation—making model training and deployment with Ultralytics even smoother and more user-friendly! 🚀🖼️
Overall, this update streamlines the workflow for training, exporting, and benchmarking models with Ultralytics, making it more efficient and user-friendly for everyone! 🌟
max_shape up to 1280 by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20530dla from metadata by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20529check_yaml to accept Path and str by @kaanrkaraman in https://github.com/ultralytics/ultralytics/pull/20483ultralytics 8.3.129 full dataset buffer with cache="ram" by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20474Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.128...v8.3.129
This release (v8.3.128) brings major improvements to object tracking reliability, multi-GPU training, and documentation accessibility, while also enha
This release (v8.3.128) brings major improvements to object tracking reliability, multi-GPU training, and documentation accessibility, while also enhancing compatibility and user experience across various platforms and features. 🚀🛠️🌍
Object Tracking & ReID Enhancements
Multi-GPU & Device Handling
Platform Compatibility
VisualAISearch & CLIP Integration
Documentation & Internationalization
Overall, this update strengthens Ultralytics' commitment to reliability, accessibility, and ease of use for both developers and end users. 🌟
paddlepaddle export on NVIDIA Jetson by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/20490templates folder in the pyproject.toml package-data by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20484numpy for TensorRT inference on JetPack 5 by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/20485VisualAISearch documentation by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20507CLIP module for similarity_search by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20513ultralytics 8.3.128 Fix ReID feature shape check and add tests by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20499Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.127...v8.3.128
Ultralytics 8.3.127 introduces a powerful new semantic image search solution, allowing users to find images using natural language queries through an
Ultralytics 8.3.127 introduces a powerful new semantic image search solution, allowing users to find images using natural language queries through an easy-to-use web app powered by AI. 🖼️🔍✨
VisualAISearch and SearchApp classes enable searching images by describing them in plain language.This release makes advanced AI-powered image search accessible to everyone, opening up new ways to interact with and explore your visual data! 🚀
device argument from tracker args by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20476ultralytics 8.3.127 New Visual Similarity Search Solution by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20397Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.126...v8.3.127
This release introduces automatic selection of the most idle GPUs for training, making it easier and smarter to utilize hardware in multi-GPU environm
This release introduces automatic selection of the most idle GPUs for training, making it easier and smarter to utilize hardware in multi-GPU environments. It also brings improvements to documentation, logging, and code compatibility. 🚀🖥️
device=-1 (or device=[-1, -1]) to automatically use the least busy GPU(s) for training.GPUInfo utility monitors GPU usage, memory, temperature, and power to pick the best GPUs.autodevice utility.device=-1 and let Ultralytics handle it.Overall, this update makes training with Ultralytics models more efficient, user-friendly, and robust—whether you're a beginner or an expert! 💡✨
collections.Iterable TQDM Warning by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20475ultralytics 8.3.126 CUDA idle device auto-assignment by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20451Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.125...v8.3.126
This release introduces a new, faster YAML utility class for more efficient and reliable configuration handling, along with documentation improvements
This release introduces a new, faster YAML utility class for more efficient and reliable configuration handling, along with documentation improvements and performance optimizations across the Ultralytics codebase. 🚀🗂️
YAML class for loading, saving, and printing YAML files, replacing all previous YAML handling methods for better speed and consistency.show_conf, show_labels) and provide improved usage examples.workspace parameter in the default config is now left blank by default, clarifying its optional nature for TensorRT exports.Overall, this update makes working with Ultralytics models and solutions faster, more robust, and more user-friendly for everyone! ✨📈
matplotlib imports by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20462show_conf and show_labels in Solutions docs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20467None value for workspace config by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20464ultralytics 8.3.125 Fast YAML class with lazy init and C-based ops by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20470Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.124...v8.3.125
This release introduces a unified, Python-based configuration system for Ultralytics Vision AI solutions, streamlining setup, customization, and code
This release introduces a unified, Python-based configuration system for Ultralytics Vision AI solutions, streamlining setup, customization, and code maintenance. 🛠️✨
SolutionConfig Python dataclass to manage settings, replacing the previous scattered YAML and default dictionaries.solutions.yaml and related legacy config code have been deleted for a cleaner codebase.requests, psutil, and thop are now loaded only when needed, reducing initial load time and improving performance.This update is a big step forward in making Ultralytics Vision AI solutions more user-friendly, efficient, and maintainable. 🚀
640×360 image resolution for CI compatibility by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20452ultralytics 8.3.124 Create @dataclass SolutionConfig and remove solutions.yaml by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20455Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.123...v8.3.124
This release adds INT8 quantization support for Rockchip RKNN model exports, improves result reporting, enhances TensorFlow export compatibility, and
This release adds INT8 quantization support for Rockchip RKNN model exports, improves result reporting, enhances TensorFlow export compatibility, and makes several codebase refinements for clarity and robustness. 🚀
int8 option for RKNN exports, and exported model filenames clearly indicate INT8 or FP16 format.profile → profile_ops, profile() method → run()).verbose() method for results now provides clearer, more consistent output for both detection and classification tasks.Overall, this update brings valuable new features and refinements for both developers and end users, especially those working with Rockchip devices or deploying models in diverse environments. 🎉
IndexError with empty predictions when using ReID by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20449ultralytics 8.3.123 Rockchip RKNN export INT8 quantization support by @oDestroyeRo in https://github.com/ultralytics/ultralytics/pull/20450Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.122...v8.3.123
This release enhances CoreML export for YOLO classification models, introduces more accurate and user-friendly speed estimation, improves tracking and
This release enhances CoreML export for YOLO classification models, introduces more accurate and user-friendly speed estimation, improves tracking and ReID documentation, and adds flexible installation options. 🚀🛠️
meter_per_pixel for easier and more accurate results. New parameters (meter_per_pixel, max_speed, max_hist, fps) provide greater flexibility. 🚗💨streamlit and shapely, allowing compatibility with the latest releases. 🔄This update is recommended for all users, especially those working with CoreML, speed estimation, advanced tracking, or custom installations! 🚀
track history and region-free calculation by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20344streamlit>=1.29.0 and shapely>=2.0.0 version ceilings by @dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/20045ultralytics 8.3.122 Native Xcode preview of YOLO CoreML Classification models by @rromanchuk in https://github.com/ultralytics/ultralytics/pull/20437Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.121...v8.3.122
This release introduces enhanced support for handling class imbalance with Focal Loss, improves data augmentation reliability, updates documentation,
This release introduces enhanced support for handling class imbalance with Focal Loss, improves data augmentation reliability, updates documentation, and modernizes core dependencies for a smoother user experience. ⚖️🚀
curl is installed before using it for downloads, reducing potential errors.Overall, this update brings greater flexibility, reliability, and clarity to both new and advanced Ultralytics users. 🌟
curl is installed before use by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20426MINMAX_CALIBRATION algo for TensorRT int8 calibration by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20345ultralytics 8.3.121 Add FocalLoss multi-class support by @pow3rpi in https://github.com/ultralytics/ultralytics/pull/20388Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.120...v8.3.121
This release brings smarter and more flexible data augmentation with a major CutMix upgrade, improved YOLOE training workflows, and enhanced support f
This release brings smarter and more flexible data augmentation with a major CutMix upgrade, improved YOLOE training workflows, and enhanced support for all YOLO models in tracking and documentation. 🖼️🛠️
num_areas parameter to CutMix, allowing multiple candidate regions for mixing images..ts (TorchScript) files are now ignored in version control to prevent accidental commits.num_areas option benefits users seeking to fine-tune training behavior.✨ This update is especially valuable for anyone training detection or segmentation models with Ultralytics, offering both immediate accuracy improvements and a smoother development experience!
get_indexes functions in augment.py by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20389ultralytics 8.3.120 CutMix augmentation fix via IoU overlaps by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20393Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.119...v8.3.120
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