Caffe: Convolutional Architecture for Fast Feature Embedding

@article{Jia2014CaffeCA,
  title={Caffe: Convolutional Architecture for Fast Feature Embedding},
  author={Yangqing Jia and Evan Shelhamer and Jeff Donahue and Sergey Karayev and Jonathan Long and Ross B. Girshick and Sergio Guadarrama and Trevor Darrell},
  journal={Proceedings of the 22nd ACM international conference on Multimedia},
  year={2014}
}
Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a… Expand
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