How to Train YOLOv11 Instance Segmentation on a Custom Dataset

RF-DETR Segmentation, released in October 2025, is a cutting-edge instance segmentation model that is three times faster and more accurate than the largest YOLO11 model when assessed on the Microsoft COCO Segmentation benchmark. The article explores how to train a YOLOv11 model, a versatile tool for tasks such as object detection and instance segmentation, on a custom Pelvis X-ray dataset from Roboflow Universe, showcasing its applications in medical diagnosis, training, injury analysis, forensic anthropology, and AI-driven prosthetics. The process involves retrieving the dataset, training the model using the ultralytics package, and deploying the trained model to Roboflow for inference, enabling its use across various devices through a cloud API. The YOLO11 model, with its improved precision and lower memory and runtime costs, demonstrates significant potential in enhancing the accuracy and efficiency of instance segmentation tasks in diverse fields.

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