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CAFFE (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed 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

• Comparison of deep learning software

References

External links

• Official website (GitHub)