diff --git a/body.tex b/body.tex
index d729a91..342f7ab 100644
--- a/body.tex
+++ b/body.tex
@@ -669,6 +669,31 @@ However, in case of a class imbalance the macro averaging
 favours classes with few detections whereas micro averaging benefits classes
 with many detections.
 
+\begin{table}[ht]
+    \begin{tabular}{rcccc}
+        \hline
+        Forward & max & abs OSE & Recall & Precision\\
+        Passes & \(F_1\) Score & \multicolumn{3}{c}{at max \(F_1\) point} \\
+            \hline
+            vanilla SSD - 0.01 conf & 0.255 & 3176 & 0.214 & 0.318 \\
+            vanilla SSD - 0.2 conf & \textbf{0.376} & 2939 & \textbf{0.382} & 0.372 \\
+            SSD with Entropy test - 0.01 conf & 0.255 & 3168 & 0.214 & 0.318 \\
+            % entropy thresh: 2.4 for vanilla SSD is best
+            \hline
+            Bayesian SSD - no DO - 0.2 conf - no NMS \; 10 & 0.006 & 164 & 0.004 & 0.005 \\
+            no dropout - 0.2 conf - NMS \; 10 & 0.371 & \textbf{2335} & 0.365 & \textbf{0.378} \\
+            % entropy thresh: 1.2 for Bayesian - 2 is best, 0.4 for 3
+            % 0.5 for Bayesian - 6, 1.4 for 7
+        \hline
+    \end{tabular}
+    \caption{Results for micro averaging. SSD with Entropy test and Bayesian SSD are represented with
+    their best performing entropy threshold. Vanilla SSD with Entropy test performed best with an
+    entropy threshold of 2.4, Bayesian SSD with no non-maximum suppression performed best for 0.5,
+    and Bayesian SSD with non-maximum suppression performed best for 1.4 as entropy
+    threshold.}
+    \label{tab:results-micro}
+\end{table}
+
 In both cases, vanilla SSD with a per-class confidence threshold of 0.2
 performs best (see tables \ref{tab:results-micro} and \ref{tab:results-macro})
 with a maximum \(F_1\) score of 0.376/0.375 (always micro/macro) compared to both vanilla SSD with a per-class
@@ -681,50 +706,27 @@ not very uncertain. The best performing entropy threshold is not any better than
 the corresponding vanilla SSD without entropy threshold. Therefore, in this
 case the per-class confidence score is far more important for the result.
 
-\begin{table}
-    \begin{tabular}{rcccc}
-        \hline
-        Forward & max & abs OSE & Recall & Precision\\
-        Passes & \(F_1\) Score & \multicolumn{3}{c}{at max \(F_1\) point} \\
-            \hline
-            vanilla SSD - 0.01 conf & 0.255 & 3176 & 0.214 & 0.318 \\
-            vanilla SSD - 0.2 conf & \textbf{0.376} & \textbf{2939} & \textbf{0.382} & \textbf{0.372} \\
-            SSD with Entropy test - 0.01 conf & 0.255 & 3168 & 0.214 & 0.318 \\
-            % entropy thresh: 2.4 for vanilla SSD is best
-            \hline
-            Bayesian SSD - no bg - 0.2 conf \; 10 & 0.003 & 2145 & 0.005 & 0.002 \\
-            no bg > 0.8 conf - 0.2 conf \; 10 & 0.003 & 151 & 0.004 & 0.003 \\
-            % entropy thresh: 1.2 for Bayesian - 2 is best, 0.4 for 3
-        \hline
-    \end{tabular}
-    \caption{Results for micro averaging. SSD with Entropy test and Bayesian SSD are represented with
-    their best performing entropy threshold. Vanilla SSD with Entropy test performed best with an
-    entropy threshold of 2.4, Bayesian SSD with no background performed best for 1.2,
-    and Bayesian SSD with no background prediction higher than 0.8 performed best for 0.4 as entropy
-    threshold.}
-    \label{tab:results-micro}
-\end{table}
-
-\begin{table}
+\begin{table}[ht]
     \begin{tabular}{rcccc}
         \hline
         Forward & max & abs OSE & Recall & Precision\\
         Passes & \(F_1\) Score & \multicolumn{3}{c}{at max \(F_1\) point} \\
             \hline
             vanilla SSD - 0.01 conf & 0.370 & 1426 & 0.328 & 0.424 \\
-            vanilla SSD - 0.2 conf & \textbf{0.375} & \textbf{1218} & \textbf{0.338} & 0.424 \\
+            vanilla SSD - 0.2 conf & \textbf{0.375} & 1218 & \textbf{0.338} & 0.424 \\
             SSD with Entropy test - 0.01 conf & 0.370 & 1373 & 0.329 & \textbf{0.425} \\
             % entropy thresh: 1.7 for vanilla SSD is best
             \hline
-            Bayesian SSD - no bg - 0.2 conf \; 10 & 0.002 & 1784 & 0.005 & 0.002 \\
-             no bg > 0.8 conf - 0.2 conf \; 10 & 0.002 & 122 & 0.003 & 0.002 \\
+            Bayesian SSD - no DO - 0.2 conf - no NMS \; 10 & 0.006 & 1453 & 0.009 & 0.005 \\
+            no dropout - 0.2 conf - NMS \; 10 & 0.363 & \textbf{1057} & 0.321 & 0.420 \\
             % entropy thresh: 1.2 for Bayesian - 2 is best, 0.4 for 3
+            % entropy thresh: 0.7 for Bayesian - 6 is best, 1.5 for 7
         \hline
     \end{tabular}
     \caption{Results for macro averaging. SSD with Entropy test and Bayesian SSD are represented with
     their best performing entropy threshold. Vanilla SSD with Entropy test performed best with an
-    entropy threshold of 1.7, Bayesian SSD with no background performed best for 1.2,
-    and Bayesian SSD with no background prediction higher than 0.8 performed best for 0.4 as entropy
+    entropy threshold of 1.7, Bayesian SSD with no non-maximum suppression performed best for 0.7,
+    and Bayesian SSD with non-maximum suppression performed best for 1.5 as entropy
     threshold.}
     \label{tab:results-macro}
 \end{table}