Caffe vs TensorFlow

Overview

TensorFlow

Stacks3.9K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

Caffe

Stacks66

Followers73

Votes0

GitHub Stars34.7K

Forks18.6K

Caffe vs TensorFlow: What are the differences?

# Introduction

1. **Architecture**: Caffe utilizes a declarative programming model where the network structure is defined beforehand in a configuration file, while TensorFlow uses a more versatile imperative programming model that allows for dynamic network construction during runtime.
  
2. **Graph Execution**: In Caffe, networks are defined as a series of layers with pre-specified connections, making it less flexible compared to TensorFlow's dynamic graph execution which enables easier debugging and visualization of the network.
  
3. **Language Support**: Caffe is primarily written in C++ and does not offer as wide language support as TensorFlow, which supports multiple languages like Python, C++, and Java, making it more accessible to a larger community of developers.
  
4. **Deployment**: TensorFlow has better support for deployment on various platforms such as mobile devices and web applications due to its flexibility and compatibility with TensorFlow Lite and TensorFlow.js, while Caffe lacks such extensive deployment options.
  
5. **Community and Documentation**: TensorFlow has a larger community base and extensive documentation resources available compared to Caffe, providing better support for users in terms of troubleshooting, updates, and overall development assistance.
  
6. **Built-in Models and Pre-trained Networks**: TensorFlow offers a broader range of built-in models and pre-trained networks through its TensorFlow Hub, making it easier for users to leverage existing models for their projects without starting from scratch, a feature that is not as robust in Caffe.

In Summary, TensorFlow provides more flexibility, language support, deployment options, community resources, and pre-trained models compared to Caffe.

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Advice on TensorFlow, Caffe

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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Detailed Comparison

TensorFlow	Caffe
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.	It is a deep learning framework made with expression, speed, and modularity in mind.
-	Extensible code; Speed; Community;
Statistics
GitHub Stars 192.3K	GitHub Stars 34.7K
GitHub Forks 74.9K	GitHub Forks 18.6K
Stacks 3.9K	Stacks 66
Followers 3.5K	Followers 73
Votes 106	Votes 0
Pros & Cons
Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful	No community feedback yet
Integrations
JavaScript	Keras Amazon SageMaker Pythia

What are some alternatives to TensorFlow, Caffe?

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

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.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Related Comparisons

Caffe vs TensorFlow: What are the differences?

# Introduction

1. **Architecture**: Caffe utilizes a declarative programming model where the network structure is defined beforehand in a configuration file, while TensorFlow uses a more versatile imperative programming model that allows for dynamic network construction during runtime.
  
2. **Graph Execution**: In Caffe, networks are defined as a series of layers with pre-specified connections, making it less flexible compared to TensorFlow's dynamic graph execution which enables easier debugging and visualization of the network.
  
3. **Language Support**: Caffe is primarily written in C++ and does not offer as wide language support as TensorFlow, which supports multiple languages like Python, C++, and Java, making it more accessible to a larger community of developers.
  
4. **Deployment**: TensorFlow has better support for deployment on various platforms such as mobile devices and web applications due to its flexibility and compatibility with TensorFlow Lite and TensorFlow.js, while Caffe lacks such extensive deployment options.
  
5. **Community and Documentation**: TensorFlow has a larger community base and extensive documentation resources available compared to Caffe, providing better support for users in terms of troubleshooting, updates, and overall development assistance.
  
6. **Built-in Models and Pre-trained Networks**: TensorFlow offers a broader range of built-in models and pre-trained networks through its TensorFlow Hub, making it easier for users to leverage existing models for their projects without starting from scratch, a feature that is not as robust in Caffe.

In Summary, TensorFlow provides more flexibility, language support, deployment options, community resources, and pre-trained models compared to Caffe.

Caffe vs TensorFlow

Overview