Original author(s) | Yangqing Jia |
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Developer(s) | Berkeley Vision and Learning Center |
Stable release | 1.0
[1]
/ 18 April 2017 |
Repository | |
Written in | C++ |
Operating system | Linux, macOS, Windows [2] |
Type | Library for deep learning |
License | BSD [3] |
Website |
caffe |
Part of a series on |
Machine learning and data mining |
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Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. [4] It is written in C++, with a Python interface. [5]
Yangqing Jia created the Caffe project during his PhD at UC Berkeley. [6] It is currently hosted on GitHub. [7]
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- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL. [9] [10]
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. [11]
In April 2017, Facebook announced Caffe2, [12] which included new features such as recurrent neural network (RNN). At the end of March 2018, Caffe2 was merged into PyTorch. [13]
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