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Small Introduction About Caffe Deep Learning?

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What is Caffe?

Caffe is a deep learning framework made with expression, speed, and modularity in mind

  • Expression: models and optimizations are defined as plaintext schemas instead of code.
  • Speed: for research and industry alike speed is crucial for state-of-the-art models and massive data.
  • Modularity: new tasks and settings require flexibility and extension.
  • Openness: scientific and applied progress call for common code, reference models, and reproducibility.
  • Community: academic research, startup prototypes, and industrial applications all share strength by joint discussion and development in a BSD-2 project.


Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.

Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.

Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convent implementations available.

The Video for Caffe

posted Apr 30 by Chahat Sharma

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