2014 CaffeConvolutionalArchitecturef

• (Jia et al., 2014) ⇒ Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. (2014). “Caffe: Convolutional Architecture for Fast Feature Embedding.” In: Proceedings of the ACM International Conference on Multimedia. ISBN:978-1-4503-3063-3 doi:10.1145/2647868.2654889

Subject Headings: Caffe Framework.

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Caffe%3A+Convolutional+Architecture+for+Fast+Feature+Embedding
• http://dl.acm.org/citation.cfm?id=2647868.2654889&preflayout=flat#citedby

Quotes

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 deployment from prototyping machines to cloud environments.

Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 CaffeConvolutionalArchitecturef	Trevor Darrell Yangqing Jia Jeff Donahue Evan Shelhamer Sergey Karayev Jonathan Long Ross Girshick Sergio Guadarrama			Caffe: Convolutional Architecture for Fast Feature Embedding				10.1145/2647868.2654889		2014