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对最后一维(2个数字嘛)做了softmax出来,为什么取序号为1的那列呢?是把1的那列当作包含物体的概率?为什么啊?这一点我没找到什么资料。
The text was updated successfully, but these errors were encountered:
你可以试着取另一维,然后跑跑看结果。如果几乎没有差别,那就说明这个选取没有一定的要求。 而事实上可能真的就是这样:神经网络不在乎你取哪一个,他总是会根据你的选择完成拟合。
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def _create_label(self, inside_index, anchor, bbox): # label: 1 is positive, 0 is negative, -1 is dont care label = np.empty((len(inside_index),), dtype=np.int32) label.fill(-1) argmax_ious, max_ious, gt_argmax_ious = \ self._calc_ious(anchor, bbox, inside_index) # assign negative labels first so that positive labels can clobber them label[max_ious < self.neg_iou_thresh] = 0 # positive label: for each gt, anchor with highest iou label[gt_argmax_ious] = 1 # positive label: above threshold IOU label[max_ious >= self.pos_iou_thresh] = 1
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对最后一维(2个数字嘛)做了softmax出来,为什么取序号为1的那列呢?是把1的那列当作包含物体的概率?为什么啊?这一点我没找到什么资料。
The text was updated successfully, but these errors were encountered: