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目标检测 #19

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maiff opened this issue Feb 10, 2020 · 1 comment
Open

目标检测 #19

maiff opened this issue Feb 10, 2020 · 1 comment

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@maiff
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maiff commented Feb 10, 2020 •
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一文读懂faster rcnn
feture上的anchor box是通过缩放到原图上的

目标检测|YOLO原理与实现 - 小小将的文章 - 知乎

目标检测|YOLOv2原理与实现

目标检测|SSD原理与实现
１、negative example过多造成它的loss太大，以至于把positive的loss都淹没掉了，不利于目标的收敛；

２、大多negative example不在前景和背景的过渡区域上，分类很明确(这种易分类的negative称为easy negative)，训练时对应的背景类score会很大，换个角度看就是单个example的loss很小，反向计算时梯度小。梯度小造成easy negative example对参数的收敛作用很有限，我们更需要loss大的对参数收敛影响也更大的example，即hard positive/negative example。
这里要注意的是前一点我们说了negative的loss很大，是因为negative的绝对数量多，所以总loss大；后一点说easy negative的loss小，是针对单个example而言。
@maiff
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maiff commented Mar 9, 2020 •
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  • Region Proposal Networks(RPN) 目标检测，anchor
  • ROI RoI Pooling Layer RoI Pooling确实是从Spatial Pyramid Pooling
    Global Context
  • FPN
    kHpccR.png

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@maiff