2020/06/11 by Heemoon Yoon, Sanghee Lee, Sang-Hee Lee +6 · 1 citation
Computer Science · Engineering · Medicine · #Advanced Neural Network Applications #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Traffic Prediction and Management Techniques #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2006.06385
"The code of TF-GraF for TensorFlow object detection API is opened at https://github.com/boguss1225/ObjectDetectionGUI"
arxiv created 2020/06/11 · openalex publication_date 2020/06/11 · arxiv updated 2020/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.