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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 Vanilla \gls{SSD} is based upon the VGG-16 network (see figure
\ref{fig:vanilla-ssd}) and adds extra feature layers. The entire \ref{fig:vanilla-ssd}) and adds extra feature layers. The entire
image (always size 300x300) is divided up into anchor boxes. During image (always size 300x300) is divided up into so called anchor boxes.
training, each of these boxes is mapped to a ground truth box or During training, each of these boxes is mapped to a ground truth box or
background. For every anchor box both the offset to background. For every anchor box both the offset to
the object and the class confidences are calculated. The output of the the object and the class confidences are calculated. The output of the
\gls{SSD} network are the predictions with class confidences, offsets to the \gls{SSD} network are the predictions with class confidences, offsets to the