SAHI: Slicing Aided Hyper Inference

A lightweight vision library for performing large scale object detection & instance segmentation

SAHI (Slicing Aided Hyper Inference) is an open-source framework that provides a generic slicing-aided inference and fine-tuning pipeline for small object detection. Detecting small objects and those far from the camera is a major challenge in surveillance applications, as they are represented by a small number of pixels and lack sufficient detail for conventional detectors.

SAHI addresses this by applying a unique methodology that can be used with any object detector without requiring additional fine-tuning. Experimental evaluations on the Visdrone and xView aerial object detection datasets show that SAHI can increase object detection AP by up to 6.8% for FCOS, 5.1% for VFNet, and 5.3% for TOOD detectors. With slicing-aided fine-tuning, the accuracy can be further improved, resulting in a cumulative increase of 12.7%, 13.4%, and 14.5% AP, respectively. The technique has been successfully integrated with Ultralytics (YOLOv8, YOLO11, YOLO26), HuggingFace Transformers (detection,segmentation), RT-DETR, TorchVision, MMDetection, Detectron2, YOLOv5, YOLOE, YOLO-World, and Roboflow RF-DETR models.