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.
Video Caffe (software)
History
Yangqing Jia created the caffe project during his PhD at UC Berkeley. Now there are many contributors to the project, and it is hosted at GitHub.
Maps Caffe (software)
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. Caffe supports GPU-based acceleration using CuDNN of Nvidia.
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.
In April 2017, Facebook announced Caffe2, which includes new features such as Recurrent Neural Networks.
See also
- Comparison of deep learning software
References
External links
- Official website (GitHub)
Source of article : Wikipedia
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