Difference between TensorFlow and Caffe

In this article, we are going to see the difference between TensorFlow and Caffe. TensorFlow is basically a software library for numerical computation using data flow graphs, where Caffe is a deep learning framework written in C++ that has an expression architecture easily allowing you to switch between the CPU and GPU.

TensorFlow:

TensorFlow is an open-source and free software library widely used for numerical computation, it is developed in C++ and python that speeds up the computations using data flow graphs.  TensorFlow was developed by the Google Brain Team for the purposes of conducting machine learning and deep neural networks research. It was released to open source in 2015. We can use TensorFlow to run on both CPU and GPU, mobile and embedded platforms, etc. which made it cross-platform.  TensorFlow is basically a software library for numerical computation using data flow graphs where:

• nodes in the graph represent mathematical operations.
• edges in the graph represent the multidimensional data arrays (called tensors) communicated between them. (Please note that tensor is the central unit of data in TensorFlow).

Advantages of TensorFlow:

• It works for algorithms like reinforcement learning and others.
• Provides graph computing abstraction.
• Parallelism of data and models is available.
• It can run on a variety of CPUs and GPUs.

Drawbacks of TensorFlow:

• Since it doesn’t accept matrix operations, copying these huge matrices is a time-consuming method.
• Compared to other frameworks, it runs slow.
• No pre-trained models are available.
• Drops out of the program to load each new training batch in Python.
• Not so adaptable.
• On massive development programs, dynamic typing is vulnerable to errors.

Caffe: 

Caffe Convolutional Architecture for Fast Feature Embedding) was originally developed at the Berkeley Vision and Learning Center at the University of California and released on 18 April 2017. It is a deep learning framework written in C++ that has an expression architecture easily allowing you to switch between the CPU and GPU. Caffe also has a MATLAB and Python interface and Yahoo has also combined Apache Spark with Caffe to create CaffeOnSpark.

Caffe is the perfect framework for image classification and segmentation as it supports various GPU- and CPU-based libraries such as NVIDIA, cuDNN, Intel MKL, etc. And the more said about its speed the better! Caffe can currently process over 60M images in a day with a single NVIDIA K40 GPU which makes it one of the fastest options today. Because of all these reasons, Caffe is extremely popular in startups, academic research projects, and even multinational industrial applications in the domains of computer vision, speech, and multimedia.

Advantage of caffe:

• Fast
• Easy to use.
• Open-source.
• Actively developed
• Not well documented
• Supports GPU training.
• Caffe is aimed at the production of edge deployment.

Drawbacks of caffer:

• Multi-GPU training is partially supported.
• On massive development programs, dynamic typing is vulnerable to errors.

Table of Difference between TensorFlow and Caffe