Wrote skeleton of data set preparation
Signed-off-by: Jim Martens <github@2martens.de>
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body.tex
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body.tex
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@ -249,6 +249,32 @@ with eager mode.
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\section{Preparation of data sets}
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Usually, data sets are not perfect when it comes to neural
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networks: they contain outliers, invalid bounding boxes, and similar
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problematic things. Before a data set can be used, these problems
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need to be removed.
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For the MS COCO data set, all annotations were checked for
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impossible values: bounding box height or width lower than zero,
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x1 and y1 bounding box coordinates lower than zero,
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x2 and y2 coordinates lower or equal to zero, x1 greater than x2,
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y1 greater than y2, image width lower than x2,
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and image height lower than y2. In the last two cases the
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bounding box width or height was set to (image with - x1) or (image height - y1)
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respectively; in the other cases the annotation was skipped.
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If the bounding box width or height afterwards is
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lower or equal to zero the annotation is skipped.
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In this thesis SceneNet RGB-D is always used with COCO classes.
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Therefore, a mapping between COCO and SceneNet RGB-D and vice versa
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was necessary. It was created my manually going through each
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Wordnet ID and searching for a fitting COCO class.
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The ground truth for SceneNet RGB-D is stored in protobuf files
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and had to be converted into Python format to use it in the
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codebase. Only ground truth instances that had a matching
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COCO class were saved, the rest discarded.
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\section{Replication of Miller et al.}
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\section{Implementing an auto-encoder}
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