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Caffe

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MXNet

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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.

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    What is Caffe?

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

    What is MXNet?

    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.

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    What companies use Caffe?
    What companies use MXNet?
    See which teams inside your own company are using Caffe or MXNet.
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    What tools integrate with Caffe?
    What tools integrate with MXNet?

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    What are some alternatives to Caffe and MXNet?
    TensorFlow
    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
    Torch
    It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
    PyTorch
    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
    Caffe2
    Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    See all alternatives