原文: Faster R-CNN: Down the rabbit hole of modern object detection - 2018.01.18作者：Javier ReyObject...
你是跑的哪个 demo.py 呢？ bbox_normalize_stds 应该是 bbox 归一化的方差吧
楼主你好，我想问一下，他在跑demo.py的时候，self._predictions["bbox_pred"] *= stds这步是什么操作呀，为什么对bbox_pred还要再进行一下修正，换句话说，配置文件中的FLAGS2["bbox_normalize_stds"] = (0.1, 0.1, 0.1, 0.1)是什么意思？
是的，Faster R-CNN 论文里的 IoU 阈值是 0.7 和 0.3. 论文细节为：For training RPNs, we assign a binary class label (of being an object or not) to each anchor. We assign a positive label to two kinds of anchors: (i) the anchor/anchors with the highest Intersectionover-Union (IoU) overlap with a ground-truth box, or (ii) an anchor that has an IoU overlap higher than 0.7 with any ground-truth box. Note that a single ground-truth box may assign positive labels to multiple anchors. We assign a negative label to a non-positive anchor if its IoU ratio is lower than 0.3 for all ground-truth boxes. Anchors that are neither positive nor negative do not contribute to the training objective.
你好，感谢你的整理。4.1 rpn的iou阈值应该分别是0.7 0.3吧。除了这个区分正负样本，应该还有一个与gt最匹配的anchor作为正样本（保证每个gt box都能匹配到）