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]

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- and CPU-based acceleration computational kernel libraries such as NVIDIA cuDNN and Intel MKL.[9][10]

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.[11]

Caffe2

In April 2017, Facebook announced Caffe2,[12] which included new features such as Recurrent Neural Networks. At the end of March 2018, Caffe2 was merged into PyTorch.[13]

See also

References

External links