https://blog.csdn.net/a1154761720/article/details/50864994
https://blog.csdn.net/hysteric314/article/details/54093734
https://www.cnblogs.com/shixiangwan/p/7215926.html?utm_source=itdadao&utm_medium=referral
请参考以上链接,你的疑惑就消除了。
P=检测正确/(检测正确+检测误以为正确) R=检测正确/(检测正确+检测误以为错误)
下面我主要说一下faster rcnn关于ap的计算:
根目录lib/dataset 下voc_eval.py有这样的代码
def voc_ap(rec, prec, use_07_metric=False):
""" ap = voc_ap(rec, prec, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False).
"""
if use_07_metric:
# 11 point metric
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap = ap + p / 11.
else:
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
什么意思呢?如果你的数据集是voc2007,ap就取最大的精确率,11点取平均循环加起来。否则,比如数据集voc2012那么就使用precision和recall积分求面积得到ap。你可以把此法用到voc2007上也没有关系,我就是这样做的。
感谢三个链接作者对我的启发帮助!
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