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?
Pros:
- An open license allowing for use in commercial products.
- Customizable source code allowing flexibility to train on many different dataset.
- Includes common image recognition model templates “out of the box.”
- Efficient performance even without specialized GPUs.
Cons:
- Limited documentation and most support is community based.
- Updates are not comprehensive, since the platform uses many 3d party programs that update on a separate schedule.
- Limited integration with other deep learning frameworks.
- Lower efficiency when analyzing non-multimedia datasets.
