YOLO11 and SAM2 for custom Instance Segmentation

In this tutorial, you will learn how to perform custom instance segmentation by combining YOLO11 with Segment Anything Model 2 (SAM2). This method is extremely powerful for medical image segmentation, especially when you need accurate masks for tumors, lesions, abnormalities, or other medical structures. We will first train an Object Detection Model (YOLOv5 / YOLOv8 / YOLOv9 / YOLOv10 / YOLO11 — any model works). The detection output will then be passed as prompts to SAM2, which generates high-quality segmentation masks for each detected object. This step-by-step guide is perfect for beginners, researchers, and developers working on medical imaging or advanced computer vision tasks. 🔥 What You Will Learn ✔ How YOLO and SAM2 work together ✔ Training custom object detection model ✔ Passing detection outputs to SAM2 ✔ Instance segmentation on medical images ✔ Improving segmentation quality ✔ Complete Python implementation 🔗 Downloads & Resources 📘 GitHub Repository (Code): https://github.com/codewithaarohi/YOLO11-and-SAM2 📥 Dataset (Roboflow): https://universe.roboflow.com/brain-tumor-detection-wsera/tumor-detection-ko5jp/dataset/8 📧 Contact For help or collaboration: Email: aarohisingla1987@gmail.com #yolo11 #sam2 #segmentanything #instancesegmentation #medicalimagesegmentation #computervision #ai #deeplearning #machinelearning #yolo #medicalai #datascience