模型评测之IoU,mAP,ROC,AUC

IOU

在目标检测算法中，交并比Intersection-over-Union，IoU是一个流行的评测方式，是指产生的候选框candidate bound与原标记框ground truth bound的交叠率，即它们的交集与并集的比值。最理想情况是完全重叠，即比值为1。一般来说，这个score ＞ 0.5 就可以被认为一个不错的结果了。

脚本实现：

def compute_iou(rec1, rec2):
     """
     computing IoU:
     param rec1: (y0, x0, y1, x1), which reflects (top, left, bottom, right)
     param rec2: (y0, x0, y1, x1)
     return: scala value of IoU
     """
     # computing area of each rectangles
     S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
     S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
     # computing the sum_area
     sum_area = S_rec1 + S_rec2
     # find the each edge of intersect rectangle
     left_line = max(rec1[1], rec2[1])
     right_line = min(rec1[3], rec2[3])
     top_line = max(rec1[0], rec2[0])
     bottom_line = min(rec1[2], rec2[2])
     # judge if there is an intersect
     if left_line >= right_line or top_line >= bottom_line:
         return 0
     else:
         intersect = (right_line - left_line) * (bottom_line - top_line)
         return (intersect / (sum_area - intersect))*1.0

mAP

ROC

AUC