Vision Models by Ultralytics

The YOLOv8 pose estimation model allows you to detect keypoints in an image.

YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5.

The state-of-the-art YOLOv8 model comes with support for instance segmentation tasks.

YOLOv5 Instance Segmentation is a version of YOLOv5 that can be used for instance segmentation tasks.

YOLOv5 Classification is a version of the YOLOv5 model used in single-label and multi-label image classification.

A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results.

YOLO11 is a computer vision model that you can use for object detection, segmentation, and classification.

You can retrieve bounding boxes whose edges match an angled object by training an oriented bounding boxes object detection model, such as YOLOv8's Oriented Bounding Boxes model.

An image classification model built using YOLOv8.

YOLOv5-OBB is a variant of YOLOv5 that supports oriented bounding boxes. This model is designed to yield predictions that better fit objects that are positioned at an angle.