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1、BBox:边界框 一般分为人工标注、预测识别两种。 有两种标注方式: 1)xyxy:左上+右下的坐标 2)xywh:左上+长宽 一共四个值进行记忆。
2、Anchor:锚框,又叫Prior Box 人为设置的不同长宽比、面积的先验框。 一般是在特征图片上进行划定区域而进行的。 3、RoI:特定的感兴趣区域
4、Region Proposal:候选区域/框 5、RPN:Region Proposal Network——提取候选框网络,用来提取候选框的
6、IoU:Intersaction over Union——交并比
主要是用来评价预测框的质量,IoU越大则预测框与标注越接近,主要取的是BBox中人为标注的框和预测框之间的交集或者说是面积。
7、mAP
准确率:针对预测结果而言的
召回率:针对样本而言的
P-R曲线:实际上是用的是
累积的点所构成的曲线(此处累计,需要先根据预测得分进行排序,然后进行precision和recall进行的累计求解,从上到下,求出每个点的坐标)将每一个散点进行连接,就可以得到最后的P-R曲线。(在VOC中可能会进行拟合,再求AP的值,这在CoCo和voc中还是有点不一样的)
8、NMS非极大值抑制(去掉非极大值,只留下极大值)
一般是出现多个预测框(出现意见不一致),则会最终留下一个预测框。