YOLOV8中的多分类预测框非极大值抑制

之前在研究目标检测推理时后处理时，只了解了会进行预测框之间的非极大值抑制(nms)，但在仔细研究了YOLOV8的nms源码后，发现它的做法并非传统的nms，而是会进行类别偏移处理。简单地说，就是如果我们的数据集存在的类别num_classes > 1时，在推理的过程中，并不会直接对重叠程度较高的预测框直接进行处理，而是如果预测框之间属于不同的类别，我们不能简单地进行过滤，因为很有可能两个不同类别的目标临近存在，直接过滤将导致漏检。

那么YOLOV8是怎么做的呢，我们直接看代码：
 

def non_max_suppression(
    prediction,
    conf_thres=0.25,
    iou_thres=0.45,
    classes=None,
    agnostic=False,
    multi_label=False,
    labels=(),
    max_det=300,
    nc=0,  # number of classes (optional)
    max_time_img=0.05,
    max_nms=30000,
    max_wh=7680,
    in_place=True,
    rotated=False,
):
    """
    Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box.

    Args:
        prediction (torch.Tensor): A tensor of shape (batch_size, num_classes + 4 + num_masks, num_boxes)
            containing the predicted boxes, classes, and masks. The tensor should be in the format
            output by a model, such as YOLO.
        conf_thres (float): The confidence threshold below which boxes will be filtered out.
            Valid values are between 0.0 and 1.0.
        iou_thres (float): The IoU threshold below which boxes will be filtered out during NMS.
            Valid values are between 0.0 and 1.0.
        classes (List[int]): A list of class indices to consider. If None, all classes will be consider