Train a YOLO26 Instance Segmentation Model with Custom Data

Erik Kokalj's guide, published on January 15, 2026, explains the process of implementing instance segmentation using the YOLO26 architecture, particularly focusing on a custom Car Parts dataset. Instance segmentation is highlighted as crucial for applications needing precise shape measurement, distinguishing it from object detection by identifying the exact pixels of an object. The guide recommends using Google Colab with a GPU for setting up the environment, and tools like ultralytics for model training, roboflow for dataset management, and supervision for visualization. It details the process of fine-tuning the YOLO26 model on a specialized dataset from Roboflow Universe that includes polygon annotations of car parts. The training involves using the yolo26m-seg.pt weights and focuses on evaluating the model's learning capability over 20 epochs. The fine-tuned model's performance is assessed through a confusion matrix, identifying areas of confusion and size sensitivity, with larger objects achieving higher accuracy. The guide concludes with instructions on visualizing segmentation results using the Supervision library, encouraging further exploration and testing of YOLO26 on other datasets.