Wrote skeleton of data set preparation

Signed-off-by: Jim Martens <github@2martens.de>

body.tex

@@ -249,6 +249,32 @@ with eager mode.
 
 \section{Preparation of data sets}
 
+Usually, data sets are not perfect when it comes to neural
+networks: they contain outliers, invalid bounding boxes, and similar
+problematic things. Before a data set can be used, these problems
+need to be removed.
+
+For the MS COCO data set, all annotations were checked for
+impossible values: bounding box height or width lower than zero,
+x1 and y1 bounding box coordinates lower than zero,
+x2 and y2 coordinates lower or equal to zero, x1 greater than x2,
+y1 greater than y2, image width lower than x2,
+and image height lower than y2. In the last two cases the
+bounding box width or height was set to (image with - x1) or (image height - y1)
+respectively; in the other cases the annotation was skipped.
+If the bounding box width or height afterwards is
+lower or equal to zero the annotation is skipped.
+
+In this thesis SceneNet RGB-D is always used with COCO classes.
+Therefore, a mapping between COCO and SceneNet RGB-D and vice versa
+was necessary. It was created my manually going through each
+Wordnet ID and searching for a fitting COCO class.
+
+The ground truth for SceneNet RGB-D is stored in protobuf files
+and had to be converted into Python format to use it in the
+codebase. Only ground truth instances that had a matching
+COCO class were saved, the rest discarded. 
+
 \section{Replication of Miller et al.}
 
 \section{Implementing an auto-encoder}