How to Train a YOLOv12 Object Detection Model on a Custom Dataset

YOLOv12, introduced by researchers Yunjie Tian, Qixiang Ye, and David Doermann, is a cutting-edge computer vision model that offers improved latency and mean average precision (mAP) for object detection tasks, as demonstrated on the Microsoft COCO dataset. This neutral guide walks users through fine-tuning a YOLOv12 model using a custom dataset, highlighting the installation of necessary dependencies and dataset preparation using Roboflow in a Google Colab environment. The process involves training the model for at least 250 epochs using various YOLOv12 weights and demonstrates inference on a sample image, showcasing the model's ability to detect multiple object classes such as containers and their IDs. The full implementation is available in a notebook, offering a comprehensive resource for users interested in exploring or replicating the training process for their specific object detection needs.

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