  Data labeling is a time intensive process. As such, many data scientists use
various tools to aid in the data generation and labeling process. While these
tools help automate labeling, many still require user interaction throughout
the process. Additionally, most target only a few network frameworks. Any
researchers exploring multiple frameworks must find additional tools orwrite
conversion scripts. This paper presents an automated tool for generating
synthetic data in arbitrary network formats. It uses Robot Operating System
(ROS) and Gazebo, which are common tools in the robotics community. Through ROS
paradigms, it allows extensive user customization of the simulation environment
and data generation process. Additionally, a plugin-like framework allows the
development of arbitrary data format writers without the need to change the
main body of code. Using this tool, the authors were able to generate an
arbitrarily large image dataset for three unique training formats using
approximately 15 min of user setup time and a variable amount of hands-off run
time, depending on the dataset size. The source code for this data generation
tool is available at https://github.com/Navy-RISE-Lab/nn_data_collection
