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