· 2014
Caffe
- 11,200citations
- 2014year
Short summary
Caffe is a BSD-licensed C++ deep learning framework with Python/MATLAB bindings, enabling efficient training and deployment of CNNs on commodity hardware, processing over 40 million images/day on a single GPU.
AI-generated from the title and abstract; the full text is not read.
Key points
- Caffe is a BSD-licensed C++ deep learning framework with Python and MATLAB bindings.
- It is designed for efficient training and deployment of general-purpose convolutional neural networks (CNNs).
- The framework can process over 40 million images per day on a single K40 or Titan GPU.
- Caffe separates model representation from implementation, allowing for platform flexibility.
AI-generated from the title and abstract; the full text is not read.
Abstract
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 day on a single K40 or Titan GPU (approx 2 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments.
The authors' abstract, as published at the source., 2014 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science