The non-maximum suppression (NMS) algorithm filters out redundant predicted bounding boxes based on their confidence scores and spatial overlap. For a given predicted bounding box $B$, its confidence is the largest predicted class likelihood, denoted as $p$. First, all non-background bounding boxes on an image are sorted by confidence in descending order to form a list $L$. The algorithm iteratively selects the bounding box $B_1$ with the highest confidence in $L$ as a basis and removes any remaining boxes in $L$ whose Intersection over Union (IoU) with $B_1$ exceeds a predefined threshold $\epsilon$. Those with non-maximum confidence scores are thus suppressed. This process is repeated using the next highest confidence box as the new basis until every box in $L$ has been used as a basis, resulting in a final set of predictions where no two boxes have an IoU greater than $\epsilon$.