What is CAFFE?

CAFFE (Convolutional Architecture for Fast Feature Embedding) is an open-source deep learning architecture design tool, originally developed at UC Berkeley and written in C++ with a Python interface.

What are the Uses of CAFFE?

Caffe is used as the core foundation for a wide variety of academic research, prototypes, and large-scale industrial applications, in particular those supporting image classification and image segmentation. 

What are the Pros/Cons of using CAFFE?


  1. An open license allowing for use in commercial products.
  2. Customizable source code allowing flexibility to train on many different dataset.
  3. Includes common image recognition model templates “out of the box.”
  4. Efficient performance even without specialized GPUs.


  1. Limited documentation and most support is community based.
  2. Updates are not comprehensive, since the platform uses many 3d party programs that update on a separate schedule. 
  3. Limited integration with other deep learning frameworks.
  4. Lower efficiency when analyzing non-multimedia datasets.