From e2194dd8f1cfd6d25af5e1adda83af48eb8546c3 Mon Sep 17 00:00:00 2001 From: Jim Martens Date: Sun, 4 Aug 2019 13:45:52 +0200 Subject: [PATCH] Wrote skeleton of data set preparation Signed-off-by: Jim Martens --- body.tex | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/body.tex b/body.tex index 2b97567..470614f 100644 --- a/body.tex +++ b/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}