How to Train YOLOv9 on a Custom Dataset

YOLOv9, a new computer vision model architecture introduced by Chien-Yao Wang, I-Hau Yeh, and Hong-Yuan Mark Liao, offers improved object detection performance compared to its predecessors like YOLOv8, YOLOv7, and YOLOv5, particularly when benchmarked against the MS COCO dataset. This model supports object detection tasks exclusively, with no support for segmentation or classification yet. The guide outlines the process of installing and running YOLOv9, including downloading the project repository, training on a custom dataset, and deploying the model using Roboflow's Inference server. It also details the steps for setting up the environment in Google Colab, acquiring model weights, and running inference with both the v9-C and v9-E models. Additionally, the guide provides instructions for training a YOLOv9 model on a custom dataset, such as a football players detection dataset, and highlights the importance of using the right data format and tools like Roboflow for data management and model deployment.