  TensorFlow is an open-source framework for deep learning dataflow and
contains application programming interfaces (APIs) of voice analysis, natural
language process, and computer vision. Especially, TensorFlow object detection
API in computer vision field has been widely applied to technologies of
agriculture, engineering, and medicine but barriers to entry of the framework
usage is still high through command-line interface (CLI) and code for amateurs
and beginners of information technology (IT) field. Therefore, this is aim to
develop an user friendly Graphical Framework for object detection API on
TensorFlow which is called TensorFlow Graphical Framework (TF-GraF). The
TF-GraF provides independent virtual environments according to user accounts in
server-side, additionally, execution of data preprocessing, training, and
evaluation without CLI in client-side. Furthermore, hyperparameter setting,
real-time observation of training process, object visualization of test images,
and metrics evaluations of test data can also be operated via TF-GraF.
Especially, TF-GraF supports flexible model selection of SSD, Faster-RCNN,
RFCN, and Mask-RCNN including convolutional neural networks (inceptions and
ResNets) through GUI environment. Consequently, TF-GraF allows anyone, even
without any previous knowledge of deep learning frameworks, to design, train
and deploy machine intelligence models without coding. Since TF-GraF takes care
of setting and configuration, it allows anyone to use deep learning technology
for their project without spending time to install complex software and
environment.
