Overview

On February 21st, 2024, Chien-Yao Wang, I-Hau Yeh, and Hong-Yuan Mark Liao released the “YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information'' paper, which introduces a new computer vision model architecture: YOLOv9. Later, the source code was made available, allowing anyone to train their own YOLOv9 models.

According to the YOLOv9 research team, the model architecture achieves a higher mAP than existing popular YOLO models such as YOLOv8, YOLOv7, and YOLOv5, when benchmarked against the MS COCO dataset.

YOLOv9 supports the following task types:

Object detection
Image segmentation

Performance

Use This Model

Label Data Automatically with YOLOv9

You can automatically label a dataset using YOLOv9 with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use YOLOv9 to train a computer vision model.

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