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Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are fast, accurate, and easy to use. They excel at object detection, instance segmentation, semantic segmentation, depth estimation, image classification, pose estimation, and oriented object detection tasks, and can track detected objects across video frames.

Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums!

Request an Enterprise License for commercial use at Ultralytics Licensing.

Ultralytics supports a wide range of YOLO models, from early versions like YOLOv3 to the latest YOLO26. YOLO26 models are pretrained on COCO for Detection, Segmentation, and Pose Estimation. Semantic Segmentation models are pretrained on Cityscapes, Depth Estimation models are pretrained on a broad multi-dataset mix and evaluated on NYU Depth V2, Classification models are pretrained on ImageNet, and OBB models are pretrained on DOTAv1. Tracking mode is compatible with Detection, Segmentation, Pose, and OBB models. All Models download automatically from the latest Ultralytics release on first use.