SAHI provides a framework-agnostic approach to sliced and tiled inference for object detection and instance segmentation tasks. It supports multiple detection models including YOLO, Detectron2, MMDetection, and Hugging Face models. The library is designed to handle large images such as satellite and remote sensing data by splitting them into smaller tiles for processing, then merging results. It also includes interactive UI components and error analysis visualization tools for evaluating detection performance.