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 \clearpage
 \section*{Abstract}
 
-Ich bin ein Abstract
+Object detection in open set conditions is an important part of
+applying object detection to real world data sets. The key question
+is how to identify unknown or novel data. This thesis compares
+vanilla SSD and SSD with dropout sampling on the MS COCO data set.
+The hyper parameters confidence threshold, entropy threshold,
+usage of non-maximum suppression, and usage of dropout layers during
+testing are varied to create different scenarios. Both results from macro and
+micro averaging are presented.
+
+Results show a significant improvement of the object detection performance
+with a higher confidence threshold. The usage of non-maximum suppression
+improves performance as well. Usage of dropout layers during training does
+not provide a better result, a lower keep ratio decreases the performance.