Provides Python implementations of several box ensembling techniques for object detection: Non-maximum Suppression, Soft-NMS, Non-maximum Weighted, and Weighted Boxes Fusion (WBF). These methods combine and optimize bounding box predictions from multiple models by matching boxes across models using IoU thresholds and confidence-weighted averaging. Designed to improve final detection accuracy by leveraging ensemble predictions.