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| Content Provider | ACM Digital Library |
|---|---|
| Author | Lassner, Christoph Gehler, Peter Kappler, Daniel Kiefel, Martin |
| Abstract | 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. |
| Starting Page | 1210 |
| Ending Page | 1213 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450336031 |
| DOI | 10.1145/2964284.2973803 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-10-01 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Deep learning Computer vision Neural networks Machine learning Caffe Open source |
| Content Type | Text |
| Resource Type | Article |
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