Caffe (software)
Original author(s) | Yangqing Jia |
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Developer(s) | Berkeley Vision and Learning Center |
Stable release |
1.0[1]
/ 18 April 2017 |
Written in | C++ |
Operating system | Linux, macOS, Windows[2] |
Type | Library for deep learning |
License | BSD[3] |
Website |
caffe |
Machine learning and data mining |
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Machine learning venues |
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Caffe is a deep learning framework, originally developed by Yangqing Jia as part of his PhD at UC Berkeley. It is open source, under a BSD license.[4] It is written in C++, with a Python interface.[5]
History
Yangqing Jia created the caffe project during his PhD at UC Berkeley.[6] Now there are many contributors to the project, and it is hosted at GitHub.[7]
Features
Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully connected neural network designs.[8] Caffe supports GPU based accleration using CuDNN of Nvidia.[9]
Applications
Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Yahoo! has also integrated caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework.[10]
In April 2017, Facebook announced Caffe2,[11] which includes new features such as Recurrent Neural Networks.
See also
References
- ↑ "Release 1.0".
- ↑ "Microsoft/caffe". GitHub.
- ↑ "caffe/LICENSE at master". GitHub.
- ↑ "BVLC/caffe". GitHub.
- ↑ "Comparing Frameworks: Deeplearning4j, Torch, Theano, TensorFlow, Caffe, Paddle, MxNet, Keras & CNTK".
- ↑ "The Caffe Deep Learning Framework: An Interview with the Core Developers". Embedded Vision.
- ↑ "Caffe: a fast open framework for deep learning.". GitHub.
- ↑ "Caffe tutorial - vision.princeton.edu" (PDF). Archived from the original (PDF) on April 5, 2017.
- ↑ "Deep Learning for Computer Vision with Caffe and cuDNN".
- ↑ "Yahoo enters artificial intelligence race with CaffeOnSpark".
- ↑ "Caffe2 Open Source Brings Cross Platform Machine Learning Tools to Developers".
External links
- Official website (GitHub)