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. It is written in C++, with a Python interface.
|Original author(s)||Yangqing Jia|
|Developer(s)||Berkeley Vision and Learning Center|
1.0 / 18 April 2017
|Operating system||Linux, macOS, Windows|
|Type||Library for deep learning|
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. Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as NVIDIA cuDNN and Intel MKL.
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.
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- "Yahoo enters artificial intelligence race with CaffeOnSpark".
- "Caffe2 Open Source Brings Cross Platform Machine Learning Tools to Developers".
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- Official website (GitHub)