MXNet vs Caffe: What are the differences?

Developers describe MXNet as "A flexible and efficient library for deep learning". A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. On the other hand, Caffe is detailed as "A deep learning framework". It is a deep learning framework made with expression, speed, and modularity in mind.

MXNet and Caffe can be categorized as "Machine Learning" tools.

Some of the features offered by MXNet are:

• Lightweight
• Portable
• Flexible distributed/Mobile deep learning

On the other hand, Caffe provides the following key features:

• Extensible code
• Speed
• Community

MXNet and Caffe are both open source tools. It seems that Caffe with 29.2K GitHub stars and 17.6K forks on GitHub has more adoption than MXNet with 17.9K GitHub stars and 6.35K GitHub forks.