How to Code Non-Maximum Suppression (NMS) in Plain NumPy

The issue of double detection in computer vision, where models mistakenly detect the same object multiple times, can complicate advanced analytics like counting or tracking. This challenge can be addressed using Non-Maximum Suppression (NMS), a technique that filters out redundant detections by evaluating the overlap of bounding boxes using the Intersection over Union (IoU) metric. By calculating IoU, determining the areas of overlap, and discarding overlapping boxes with the same class exceeding a threshold, NMS enhances the prediction quality of object detection models. The process can be efficiently implemented using vectorized operations in NumPy, and the supervision pip package offers a ready-to-use NMS solution. While NMS is not a cure-all, it can significantly improve model predictions, paving the way for more sophisticated object detection analytics.