Caffe
Google (United States) · University of California, Berkeley · +1 more institution
Abstract
Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (approx 2 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and…
Citation impact
- FWCI
- 1099.31
- Percentile
- 100%
- References
- 11
Authors
8- YJYangqing JiaCorresponding
Google (United States)
- ESEvan Shelhamer
University of California, Berkeley, Berkeley College
- JDJeff Donahue
University of California, Berkeley, Berkeley College
- SKSergey Karayev
Berkeley College, University of California, Berkeley
- JLJonathan Long
University of California, Berkeley, Berkeley College
Topics & keywords
- Computer science
- Deep learning
- Python (programming language)
- CUDA
- Artificial intelligence
- Titan (rocket family)
- Convolutional neural network
- Software deployment
- Industry, innovation and infrastructure