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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
01 Oct 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11 versions withdrawn
withdrawn after publishing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
846 releases · first in 2022
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New Models: Introduced support for YOLOv8-World, YOLOv8-World-v2 (by @Laughing-q in PR #9268 ), YOLOv9-C, YOLOv9-E (by @Laughing-q in PR #8571 ), and
New Models: Introduced support for YOLOv8-World, YOLOv8-World-v2 (by @Laughing-q in PR #9268 ), YOLOv9-C, YOLOv9-E (by @Laughing-q in PR #8571 ), and YOLOv9 Segment models (by @Burhan-Q in PR #9296 ), expanding the versatility of the Ultralytics platform.
New Features: Added distance calculation in vision-eye, per-class object counting (by @RizwanMunawar in PR #9443 ), and queue management utilities (by @RizwanMunawar in PR #9494 ), enhancing the functionality and applicability of YOLOv8.
Performance Optimizations: Achieved 40% faster ultralytics imports (by @glenn-jocher in PR #9547 ), faster batch same_shapes, and immediate checkpoint serialization (by @glenn-jocher in PR #9437 ), further optimizing the efficiency of the framework.
Enhanced Export Capabilities: Improved export support, including OpenVINO 2023.3 updates (by @adrianboguszewski in PR #8417 ), TensorRT 10 support (by @Burhan-Q in PR #9516 ), and fixes for TFLite, ONNX, and OpenVINO exports.
Documentation Expansion: Significantly expanded the documentation with new guides, integration pages for TorchScript, TFLite, NCNN, PaddlePaddle, TF GraphDef, TF SavedModel, TF.js (by @abirami-vina in multiple PRs), and updates to existing pages, providing comprehensive resources for users.
Training Enhancements: Introduced YOLO-World training support (by @Laughing-q in PR #9268 ), fixed learning rate issues (by @Laughing-q in PR #9468 ), and improved robustness for stopping and resuming training (by @glenn-jocher in PR #9384 ).
Platform Support: Added support for NVIDIA Jetson (by @lakshanthad in PR #9484 ), Raspberry Pi (by @lakshanthad in PR #8828 ), and Apple M1 runners for tests and benchmarks (by @glenn-jocher in PR #8162 ), expanding the usability of YOLOv8 across various platforms.
CI/CD Improvements: Enhanced Ultralytics Actions using OpenAI GPT-4 for PR summaries (by @pderrenger in PR #7867 ) and introduced self-hosted Raspberry Pi 5 CI (by @lakshanthad in PR #8828 ), streamlining the development and testing processes.
Bug Fixes: Resolved various issues related to model loading, inference, plotting, and exports, ensuring a smoother user experience.
Community Contributions: Welcomed contributions from 31 new contributors, reflecting the growing engagement and collaborative spirit within the Ultralytics community.
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