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-based acceleration using CuDNN of Nvidia.
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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- "Comparing Frameworks: Deeplearning4j, Torch, Theano, TensorFlow, Caffe, Paddle, MxNet, Keras & CNTK".
- "The Caffe Deep Learning Framework: An Interview with the Core Developers". Embedded Vision.
- "Caffe: a fast open framework for deep learning". GitHub.
- "Caffe tutorial - vision.princeton.edu" (PDF). Archived from the original (PDF) on April 5, 2017.
- "Deep Learning for Computer Vision with Caffe and cuDNN".
- "Yahoo enters artificial intelligence race with CaffeOnSpark".
- "Caffe2 Open Source Brings Cross Platform Machine Learning Tools to Developers".
- Official website (GitHub)