Description:

In recent years, the importance of deep learning has significantly increased in pattern recognition, computer vision, and artificial intelligence research, as well as in industry. However, despite the existence of multiple deep learning frameworks, there is a lack of comprehensible and easy-to-use high-level tools for the design, training, and testing of deep neural networks (DNNs). In this paper, we introduce Barista, an open-source graphical high-level interface for the Caffe deep learning framework. While Caffe is one of the most popular frameworks for training DNNs, editing prototext files in order to specify the net architecture and hyper parameters can become a cumbersome and errorprone task. Instead, Barista offers a fully graphical user interface with a graph-based net topology editor and provides an end-to-end training facility for DNNs, which allows researchers to focus on solving their problems without having to write code, edit text files, or manually parse logged data.

Changes to previous version:

• new Caffe version manager
• added separate executable for remote server
• new Session cloning behavior
• improved Host Manager design
• change database location in Input Manager
• many smaller improvements and bugfixes

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Windows, Os X

Data Formats: Hdf, Leveldb, Lmdb, Prototxt

Tags: Neural Networks, Machine Learning

Archive: download here

Other available revisons

Version	Changelog	Date
0.3	new Caffe version manager added separate executable for remote server new Session cloning behavior improved Host Manager design change database location in Input Manager many smaller improvements and bugfixes	April 16, 2018, 17:13:03
0.2	Initial Announcement on mloss.org.	February 21, 2018, 15:51:11

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