Thesis Details
Anotátor datové sady pro trénování neuronových sítí
The goal of this thesis is design and implementation of a system for image data annotation which will learn neural networks. An important feature of this system is the iterative approach of learning, where the annotated set is extended by the neural network itself and users make corrections of these extensions. The system consists of an information system with an embedded annotation tool. Information system communicates with neural network management program. The information system is designed according to the MVC architectural model. The frontend of the application is built on JavaScript library - React and is designed according to Material Design conventions using the Material-UI framework. The frontend maintains a permanent connection to the Python backend using the WebSocket protocol, which is also used by the backend to communicate with the neural network management program. The NoSQL database system uses MongoDB technology.
annotator, annotation tool, iterative learning, neural network, graphical user interface, web application, information system, JavaScript, React, Material Design, Python, MongoDB, WebSocket
Burgetová Ivana, Ing., Ph.D. (DIFS FIT BUT), člen
Kreslíková Jitka, doc. RNDr., CSc. (DIFS FIT BUT), člen
Peringer Petr, Dr. Ing. (DITS FIT BUT), člen
Strnadel Josef, Ing., Ph.D. (DCSY FIT BUT), člen
@bachelorsthesis{FITBT23292, author = "Martin Schneider", type = "Bachelor's thesis", title = "Anot\'{a}tor datov\'{e} sady pro tr\'{e}nov\'{a}n\'{i} neuronov\'{y}ch s\'{i}t\'{i}", school = "Brno University of Technology, Faculty of Information Technology", year = 2021, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/23292/" }