NMS (Non-Maximum Suppression) is a post-processing technique widely used in computer vision tasks, particularly in object detection algorithms. Its core purpose is to eliminate redundant bounding boxes by suppressing non-maximum confidence scores, thereby retaining the most accurate and representative bounding box for each detected object.
