解读 Fast-RCNN（2）
今天来分析Section 2 Fast R-CNN architecture and training
先来看一下作者对网络框架的叙述，
1, A Fast R-CNN network takes input as an entire image and a set of object proposals
2, The network first processes the whole image with several CONV and max pooling layers to
produce a conv feature map
3, Then, for each object proposals a region of interest (RoI) pooling layer extracts a fixed-length vector
from the feature map
4, Each feature vector is fed into a sequence of fuly connected layers that finally branch into two
sibling output layers:
one that produces softmax probability estimates over K object classes
plus a catch-all "background" class
another layer that outputs four real-valued numbers for each of the K object class
Each set of 4 values encodes refined bounding-box positions for one of the K classes
恰如下图所示：
作者对RoI pooling layer的叙述，
1, The RoI pooling layer uses max pooling to convert the feature inside any valid region of interest into
a small feature map with fixed spatial extent of H×W where H and W are layer hyper-parameters that
are independent of any particular RoI
2, an RoI is a rectangular window into a conv feature map
3, Each RoI is defined by a four-tuple (r,c,h,w)
4, RoI max pooling works by dividing the h×w RoI window into an H×W grid of sub-windows of approximate
size h/H×w/W and then max-pooling the values in each sub-window into the corresponding output grid
cell
5, The RoI layer is simply the special-case of the spatial pyramid pooling layers used in SPPnets in which
there is only one pyramid level
总结一下，学习了这一段内容，了解到：
1，Fast R-CNN的整体架构
2，RoI pooling layers的作用以及思想源头，即SPPnets