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Barrista - Caffe Well-Served


Conference Paper



The caffe framework is one of the leading deep learning toolboxes in the machine learning and computer vision community. While it offers efficiency and configurability, it falls short of a full interface to Python. With increasingly involved procedures for training deep networks and reaching depths of hundreds of layers, creating configuration files and keeping them consistent becomes an error prone process. We introduce the barrista framework, offering full, pythonic control over caffe. It separates responsibilities and offers code to solve frequently occurring tasks for pre-processing, training and model inspection. It is compatible to all caffe versions since mid 2015 and can import and export .prototxt files. Examples are included, e.g., a deep residual network implemented in only 172 lines (for arbitrary depths), comparing to 2320 lines in the official implementation for the equivalent model.

Author(s): Christoph Lassner and Daniel Kappler and Martin Kiefel and Peter Gehler
Book Title: ACM Multimedia Open Source Software Competition
Year: 2016
Month: October

Department(s): Autonomous Motion, Perceiving Systems
Research Project(s): Efficient and Scalable Inference
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.1145/2964284.2973803
Event Name: ACM OSSC16
Event Place: Amsterdam

Digital: True
State: Published
URL: https://github.com/classner/barrista
Attachments: pdf


  title = {Barrista - Caffe Well-Served},
  author = {Lassner, Christoph and Kappler, Daniel and Kiefel, Martin and Gehler, Peter},
  booktitle = {ACM Multimedia Open Source Software Competition},
  month = oct,
  year = {2016},
  url = {https://github.com/classner/barrista},
  month_numeric = {10}