How to Train an RF-DETR Segmentation Model with a Custom Dataset

RF-DETR Segmentation, released in October 2025, is a cutting-edge instance segmentation model designed to precisely locate objects in images at the pixel level. The accompanying guide details training an RF-DETR Segmentation model using the rfdetr Python package, focusing on detecting cracks in concrete, and describes the process of preparing datasets in the Microsoft COCO JSON Segmentation format, configuring the training environment with necessary dependencies, and employing optimal training strategies like gradient accumulation. The guide further explains deploying the trained model to the cloud using Roboflow Workflows, which allows for the creation of computer vision applications with features such as object tracking and visualization of model predictions. The model can be integrated into a serverless cloud API or deployed on personal hardware, showcasing its versatility and practical application potential.