Object Detection Models with Python Support

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

YOLOv9 is an object detection model architecture released on February 21st, 2024.

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.

YOLOv7 is a state of the art object detection model.

YOLOv10 is a real-time object detection model introduced in the paper "YOLOv10: Real-Time End-to-End Object Detection".

Moondream 2 is the latest model in the Moondream series of “tiny vision language models”.

MT-YOLOv6 is a YOLO based model released in 2022.

D-FINE is a real-time object detection model introduced in the paper " D-FINE: Redefine Regression Task of DETRs as Fine‑grained Distribution Refinement".

YOLOE is a new object detection and segmentation model developed by the creators of YOLOv10.