diff --git a/body.tex b/body.tex
index a5ef2d0..7ece5f9 100644
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+++ b/body.tex
@@ -687,23 +687,29 @@ were made. Miller et al. used weights that were trained on MS COCO
 to predict on SceneNet RGB-D.
 
 As the source code was not available, I had to implement Miller's
-work myself. For the SSD network I used an implementation that
-is compatible with Tensorflow; this implementation had to be
+work myself. For the SSD network, I used an implementation that
+is compatible with
+Tensorflow\footnote{\url{https://github.com/pierluigiferrari/ssd\_keras}}; this implementation had to be
 changed to work with eager mode. Further changes were made to
 support entropy thresholding.
 
 For the Bayesian variant, observations have to be calculated:
 detections of multiple forward passes for the same image are averaged
 into an observation. This algorithm was implemented based on the
-information available in the paper.
+information available in the paper. Beyond the observation
+calculation, the Bayesian variant can use the same code as the
+vanilla version with one exception: the model had to be duplicated
+and two dropout layers added to transform SSD into a Bayesian
+network.
 
-To better understand the SceneNet RGB-D data set, I counted the
-number of instances per COCO class and a huge class imbalance was
-visible; not just globally but also between trajectories: some
-classes are only present in some trajectories. This makes training
-with SSD on SceneNet practically impossible.
-I tried to finetune the SSD on SceneNet because the
-pre-trained weights did not produce detection results.
+The vanilla SSD did not provide meaningful detections on SceneNet
+RGB-D with the pre-trained weights and fine-tuning it on SceneNet
+did not work either. Therefore, to better understand the SceneNet
+RGB-D data set, I counted the number of instances per COCO class and
+a huge class imbalance was visible; not just globally but also
+between trajectories: some classes are only present in some
+trajectories. This makes training with SSD on SceneNet practically
+impossible. 
 
 \section{Implementing an auto-encoder}