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Added gls commands where missing 

Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens 2019-10-01 12:54:38 +02:00
parent 3de5896dce
commit b172983dae
1 changed files with 8 additions and 8 deletions
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@ -940,9 +940,9 @@ all variants perform better than the average of all classes.
The performance for cars is slightly different (see table The performance for cars is slightly different (see table
\ref{tab:results-cars}): the \gls{vanilla} \gls{SSD} \ref{tab:results-cars}): the \gls{vanilla} \gls{SSD}
variant with entropy threshold and 0.01 confidence threshold has variant with \gls{entropy} threshold and 0.01 confidence threshold has
the best \(F_1\) score and recall. Vanilla SSD with 0.2 confidence the best \(F_1\) score and recall. Vanilla \gls{SSD} with 0.2 confidence
threshold, however, has the best precision. Both the Bayesian SSD threshold, however, has the best precision. Both the Bayesian \gls{SSD}
variant with \gls{NMS} and disabled dropout, and the one with 0.9 keep variant with \gls{NMS} and disabled dropout, and the one with 0.9 keep
ratio have a better precision (0.460 and 0.454 respectively) than the ratio have a better precision (0.460 and 0.454 respectively) than the
\gls{vanilla} \gls{SSD} variants with 0.01 confidence threshold (0.452 and \gls{vanilla} \gls{SSD} variants with 0.01 confidence threshold (0.452 and
@ -976,8 +976,8 @@ a better precision than the average and the Bayesian variant without
\end{table} \end{table}
The best \(F_1\) score (0.288) and recall (0.251) for the chairs class The best \(F_1\) score (0.288) and recall (0.251) for the chairs class
belongs to \gls{vanilla} \gls{SSD} with entropy threshold. Precision belongs to \gls{vanilla} \gls{SSD} with \gls{entropy} threshold. Precision
is mastered by Bayesian SSD with \gls{NMS} and disabled dropout (0.360). is mastered by Bayesian \gls{SSD} with \gls{NMS} and disabled dropout (0.360).
The variant with 0.9 keep ratio has the second-highest precision (0.343) The variant with 0.9 keep ratio has the second-highest precision (0.343)
of all variants. Both in \(F_1\) score and recall all Bayesian variants of all variants. Both in \(F_1\) score and recall all Bayesian variants
are worse than the \gls{vanilla} variants. Compared with the macro averaged are worse than the \gls{vanilla} variants. Compared with the macro averaged
@ -1011,7 +1011,7 @@ results, all variants perform worse than the average.
Bottles show similar performance to cars with overall lower numbers Bottles show similar performance to cars with overall lower numbers
(see table \ref{tab:results-bottles}). (see table \ref{tab:results-bottles}).
Again, all Bayesian variants are worse than all vanilla variants. Again, all Bayesian variants are worse than all vanilla variants.
The Bayesian SSD variant with \gls{NMS} and disabled dropout has the The Bayesian \gls{SSD} variant with \gls{NMS} and disabled dropout has the
best \(F_1\) score (0.224) and precision (0.328) among the Bayesian variants; the best \(F_1\) score (0.224) and precision (0.328) among the Bayesian variants; the
variant with 0.5 keep ratio has the best recall (0.172). All variants variant with 0.5 keep ratio has the best recall (0.172). All variants
perform worse than in the averaged results. perform worse than in the averaged results.
@ -1043,7 +1043,7 @@ perform worse than in the averaged results.
Last but not least the giraffe class (see table Last but not least the giraffe class (see table
\ref{tab:results-giraffes}) is analysed. Remarkably, all three \ref{tab:results-giraffes}) is analysed. Remarkably, all three
vanilla SSD variants have the identical performance, even before rounding. \gls{vanilla} \gls{SSD} variants have the identical performance, even before rounding.
The Bayesian variant with \gls{NMS} and disabled dropout outperforms The Bayesian variant with \gls{NMS} and disabled dropout outperforms
all the other Bayesian variants with an \(F_1\) score of 0.647, all the other Bayesian variants with an \(F_1\) score of 0.647,
recall of 0.642, and 0.654 as precision. All variants perform recall of 0.642, and 0.654 as precision. All variants perform
@ -1055,7 +1055,7 @@ This subsection compares \gls{vanilla} \gls{SSD}
with Bayesian \gls{SSD} with respect to specific images that illustrate with Bayesian \gls{SSD} with respect to specific images that illustrate
similarities and differences between both approaches. For this similarities and differences between both approaches. For this
comparison, a 0.2 confidence threshold is applied. Furthermore, the comparison, a 0.2 confidence threshold is applied. Furthermore, the
compared Bayesian SSD variant uses \gls{NMS} and dropout with 0.9 keep compared Bayesian \gls{SSD} variant uses \gls{NMS} and dropout with 0.9 keep
ratio. ratio.
\begin{figure} \begin{figure}