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\section*{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. Dropout sampling with entropy
thresholding is one possible avenue to answer the question.

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, usage of dropout layers during
testing, and the dropout keep ratio 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.
