Caffe (Convolutional Architecture for Fast Feature Embedding) is a

deep learning Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience a ...

framework, originally developed at

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. It is

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, under a

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. It is written in C++, with a Python interface.

History

Yangqing Jia created the Caffe project during his PhD at UC Berkeley, while working the lab of Trevor Darrell. The first version, called "DeCAF", made its first appearance in spring 2013 when it was used for the ILSVRC challenge (later called ImageNet). The library was named Caffe and released to the public in December 2013. It reached end-of-support in 2018. It is hosted on

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Features

Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN,

LSTM Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem commonly encountered by traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hi ...

and fully-connected neural network designs. Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as

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cuDNN and Intel MKL.

Applications

Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia.

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has also integrated Caffe with

Apache Spark Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance. Originally developed at the University of Californ ...

to create CaffeOnSpark, a distributed deep learning framework.

Caffe2

In April 2017,

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announced Caffe2, which included new features such as

recurrent neural network Recurrent neural networks (RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, and time series, where the order of elements is important. Unlike feedforward neural networks, which proces ...

(RNN). At the end of March 2018, Caffe2 was merged into PyTorch.

References

External links