Moved software and source code design to appendices

Additionally, the entire LaTeX setup was adapted for this use case.

Signed-off-by: Jim Martens <github@2martens.de>

body.tex

@@ -366,9 +366,8 @@ be used to identify and reject these false positive cases.
 
 \label{chap:methods}
 
-This chapter explains the functionality of the Bayesian SSD, the
-decoding pipelines, and
-provides some information on the software and source code design.
+This chapter explains the functionality of the Bayesian SSD and the
+decoding pipelines.
 
 \section{Bayesian SSD for Model Uncertainty}
 
@@ -516,52 +515,6 @@ observation for removal.
 Per class confidence thresholding, non-maximum suppression, and
 top \(k\) selection happen like in vanilla SSD.
 
-\section{Software and Source Code Design}
-
-The source code of many published papers is either not available
-or seems like an afterthought: it is poorly documented, difficult
-to integrate into your own work, and often does not follow common
-software development best practices. Moreover, with Tensorflow,
-PyTorch, and Caffe there are at least three machine learning
-frameworks. Every research team seems to prefer another framework
-and sometimes even develops their own; this makes it difficult
-to combine the work of different authors.
-In addition to all this, most papers do not contain proper information
-regarding the implementation details, making it difficult to
-accurately replicate them if their source code is not available.
-
-Therefore, it was clear to me: I will release my source code and
-make it available as Python package on the PyPi package index.
-This makes it possible for other researchers to simply install
-a package and use the API to interact with my code. Additionally,
-the code has been designed to be future proof and work with
-the announced Tensorflow 2.0 by supporting eager mode.
-
-Furthermore, it is configurable, well documented, and conforms
-to the clean code guidelines: evolvability and extendability among
-others.
-%Unit tests are part of the code as well to identify common
-%issues early on, saving time in the process.
-% TODO: Unit tests (!)
-
-The code was designed to be modular: One module creates the command
-line interface (main.py), another implements the actions
-chosen in the CLI (cli.py), the MS COCO to SceneNet RGB-D mapping can
-be found in the definitions.py module,
-preparation of the data sets and retrieval of data is
-grouped in the data.py module, evaluation metrics have
-their separate module (evaluation.py), the configuration is
-accessed and handled by the config.py module, debug-only code
-can be found in debug.py, and the ssd.py module contains
-code to train the SSD and later predict with it. All
-code relating to the auto-encoder can be found in its own
-sub directory.
-
-Lastly, the SSD implementation from a third party repository
-has been modified to work inside a Python package architecture and
-with eager mode. It is stored as a Git submodule inside the package
-repository.
-
 \chapter{Experimental Setup and Results}
 
 \label{chap:experiments-results}