我正在尝试为我的对象检测模型计算一个混淆矩阵。但是我似乎偶然发现了一些陷阱。我目前的方法是将每个预测框与每个地面真实框进行比较。如果它们的IoU>某个阈值，则将预测插入到混淆矩阵中。插入后，我删除预测列表中的元素，然后继续下一个元素。
因为我还希望将分类错误的提案插入混淆矩阵中，所以我将IoU低于阈值的元素视为与背景混淆。我当前的实现如下所示：
def insert_into_conf_m(true_labels, predicted_labels, true_boxes, predicted_boxes):
matched_gts = []
for i in range(len(true_labels)):
j = 0
while len(predicted_labels) != 0:
if j >= len(predicted_boxes):
break
if bb_intersection_over_union(true_boxes[i], predicted_boxes[j]) >= 0.7:
conf_m[true_labels[i]][predicted_labels[j]] += 1
del predicted_boxes[j]
del predicted_labels[j]
else:
j += 1
matched_gts.append(true_labels[i])
if len(predicted_labels) == 0:
break
# if there are groundtruth boxes that are not matched by any proposal
# they are treated as if the model classified them as background
if len(true_labels) > len(matched_gts):
true_labels = [i …