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Properly introduced anchor boxes

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-10-02 15:47:43 +02:00
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@ -438,8 +438,8 @@ This chapter explains the functionality of \gls{vanilla} \gls{SSD}, Bayesian \gl
Vanilla \gls{SSD} is based upon the VGG-16 network (see figure
\ref{fig:vanilla-ssd}) and adds extra feature layers. The entire
image (always size 300x300) is divided up into anchor boxes. During
training, each of these boxes is mapped to a ground truth box or
image (always size 300x300) is divided up into so called anchor boxes.
During training, each of these boxes is mapped to a ground truth box or
background. For every anchor box both the offset to
the object and the class confidences are calculated. The output of the
\gls{SSD} network are the predictions with class confidences, offsets to the