Shypd.aiThe largest open index of AI toolsWeighted-Boxes-Fusion is an open-source Python library that provides methods for ensembling bounding boxes from different object detection models. It includes implementations of NMS, Soft-NMS, NMW, and the Weighted Boxes Fusion (WBF) method, offering improved results for object detection tasks.
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Weighted-Boxes-Fusion is a comprehensive Python library designed for advanced object detection tasks, specifically focusing on ensembling bounding boxes from multiple models. It offers implementations of several key methods, including Non-maximum Suppression (NMS), Soft-NMS, Non-maximum weighted (NMW), and its namesake, Weighted Boxes Fusion (WBF). The WBF method is highlighted for providing superior results compared to other ensembling techniques. The library supports various dimensions, with specific functions for 3D boxes and 1D line segments, the latter being particularly useful for Natural Language Processing (NLP) tasks like Named-entity recognition (NER). It is built with Python 3.*, Numpy, and Numba, ensuring efficient processing. Usage examples are provided for both multiple and single model predictions, making it accessible for developers looking to enhance their object detection pipelines.
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