EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login
Home Data Science Data Science Tutorials Head to Head Differences Tutorial TensorFlow vs Caffe
Secondary Sidebar
Head to Head Differences Tutorial
  • Differences Tutorial
    • Scikit Learn vs TensorFlow
    • Azure Functions vs Logic Apps
    • Azure Data Factory vs Databricks
    • SHA1 vs MD5
    • Azure SQL Database vs Managed Instance
    • Azure SQL Database vs SQL Server
    • PostgreSQL vs MySQL
    • PostgreSQL vs MySQL Benchmark
    • ArangoDB vs MongoDB
    • Cloud Computing vs Big Data Analytics
    • T-SQL vs SQL
    • PostgreSQL vs MariaDB
    • Spark vs Impala
    • Datadog vs Splunk
    • Domo vs Tableau
    • Data Scientist vs Data Engineer vs Statistician
    • Big Data Vs Machine Learning
    • Predictive Analytics vs Business Intelligence
    • AI vs Machine Learning vs Deep Learning
    • Data Science vs Artificial Intelligence
    • Business Intelligence vs Data Warehouse
    • Apache Kafka vs Flume
    • Data Science vs Machine Learning
    • Business Analytics Vs Predictive Analytics
    • Data mining vs Web mining
    • Data Science Vs Data Mining
    • Data Science Vs Business Analytics
    • Analyst vs Associate
    • Apache Hive vs Apache Spark SQL
    • Apache Nifi vs Apache Spark
    • Apache Spark vs Apache Flink
    • Apache Storm vs Kafka
    • Artificial Intelligence vs Business Intelligence
    • Artificial Intelligence vs Human Intelligence
    • Al vs ML vs Deep Learning
    • SQL vs SQLite
    • Assembly Language vs Machine Language
    • AWS vs AZURE
    • AWS vs Azure vs Google Cloud
    • Big Data vs Data Mining
    • Big Data vs Data Science
    • Big Data vs Data Warehouse
    • Blu-Ray vs DVD
    • Business Intelligence vs Big Data
    • Business Intelligence vs Business Analytics
    • Business Intelligence vs Data analytics
    • Business Intelligence VS Data Mining
    • Business Intelligence vs Machine Learning
    • Business Process Re-Engineering vs CI
    • Cassandra vs Elasticsearch
    • Cassandra vs Redis
    • Cloud Computing Public vs Private
    • Cloud Computing vs Fog Computing
    • Cloud Computing vs Grid Computing
    • Cloud Computing vs Hadoop
    • Computer Network vs Data Communication
    • Computer Science vs Data Science
    • Computer Scientist vs Data Scientist
    • Customer Analytics vs Web Analytics
    • Data Analyst vs Data Scientist
    • Data Analytics vs Business Analytics
    • Data Analytics vs Data Analysis
    • Data Analytics Vs Predictive Analytics
    • Data Lake vs Data Warehouse
    • Data Mining Vs Data Visualization
    • Data mining vs Machine learning
    • Data Mining Vs Statistics
    • Data Mining vs Text Mining
    • Data Science vs Artificial Intelligence
    • Data science vs Business intelligence
    • Data Science Vs Data Engineering
    • Data Science vs Data Visualization
    • Data Science vs Software Engineering
    • Data Scientist vs Big Data
    • Data Scientist vs Business Analyst
    • Data Scientist vs Data Engineer
    • Data Scientist vs Data Mining
    • Data Scientist vs Machine Learning
    • Data Scientist vs Software Engineer
    • Data visualisation vs Data analytics
    • Data vs Information
    • Data Warehouse vs Data Mart
    • Data Warehouse vs Database
    • Data Warehouse vs Hadoop
    • Data Warehousing VS Data Mining
    • DBMS vs RDBMS
    • Deep Learning vs Machine learning
    • Digital Analytics vs Digital Marketing
    • Digital Ocean vs AWS
    • DOS vs Windows
    • ETL vs ELT
    • Small Data Vs Big Data
    • Apache Hadoop vs Apache Storm
    • Hadoop vs HBase
    • Between Data Science vs Web Development
    • Hadoop vs MapReduce
    • Hadoop Vs SQL
    • Google Analytics vs Mixpanel
    • Google Analytics Vs Piwik
    • Google Cloud vs AWS
    • Hadoop vs Apache Spark
    • Hadoop vs Cassandra
    • Hadoop vs Elasticsearch
    • Hadoop vs Hive
    • Hadoop vs MongoDB
    • HADOOP vs RDBMS
    • Hadoop vs Spark
    • Hadoop vs Splunk
    • Hadoop vs SQL Performance
    • Hadoop vs Teradata
    • HBase vs HDFS
    • Hive VS HUE
    • Hive vs Impala
    • JDBC vs ODBC
    • Kafka vs Kinesis
    • Kafka vs Spark
    • Cloud Computing vs Data Analytics
    • Data Mining Vs Data Analysis
    • Data Science vs Statistics
    • Big Data Vs Predictive Analytics
    • MapReduce vs Yarn
    • Hadoop vs Redshift
    • Looker vs Tableau
    • Machine Learning vs Artificial Intelligence
    • Machine Learning vs Neural Network
    • Machine Learning vs Predictive Analytics
    • Machine Learning vs Predictive Modelling
    • Machine Learning vs Statistics
    • MariaDB vs MySQL
    • Mathematica vs Matlab
    • Matlab vs Octave
    • MATLAB vs R
    • MongoDB vs Cassandra
    • MongoDB vs DynamoDB
    • MongoDB vs HBase
    • MongoDB vs Oracle
    • MongoDB vs Postgres
    • MongoDB vs PostgreSQL
    • MongoDB vs SQL
    • MongoDB vs SQL server
    • MS SQL vs MYSQL
    • MySQL vs MongoDB
    • MySQL vs MySQLi
    • MySQL vs NoSQL
    • MySQL vs SQL Server
    • MySQL vs SQLite
    • Neural Networks vs Deep Learning
    • PIG vs MapReduce
    • Pig vs Spark
    • PL SQL vs SQL
    • Power BI Dashboard vs Report
    • Power BI vs Excel
    • Power BI vs QlikView
    • Power BI vs SSRS
    • Power BI vs Tableau
    • Power BI vs Tableau vs Qlik
    • PowerShell vs Bash
    • PowerShell vs CMD
    • PowerShell vs Command Prompt
    • PowerShell vs Python
    • Predictive Analysis vs Forecasting
    • Predictive Analytics vs Data Mining
    • Predictive Analytics vs Data Science
    • Predictive Analytics vs Descriptive Analytics
    • Predictive Analytics vs Statistics
    • Predictive Modeling vs Predictive Analytics
    • Private Cloud vs Public Cloud
    • Regression vs ANOVA
    • Regression vs Classification
    • ROLAP vs MOLAP
    • ROLAP vs MOLAP vs HOLAP
    • Spark SQL vs Presto
    • Splunk vs Elastic Search
    • Splunk vs Nagios
    • Splunk vs Spark
    • Splunk vs Tableau
    • Spring Cloud vs Spring Boot
    • Spring vs Hibernate
    • Spring vs Spring Boot
    • Spring vs Struts
    • SQL Server vs PostgreSQL
    • Sqoop vs Flume
    • Statistics vs Machine learning
    • Supervised Learning vs Deep Learning
    • Supervised Learning vs Reinforcement Learning
    • Supervised Learning vs Unsupervised Learning
    • Tableau vs Domo
    • Tableau vs Microstrategy
    • Tableau vs Power BI vs QlikView
    • Tableau vs QlikView
    • Tableau vs Spotfire
    • Talend Vs Informatica PowerCenter
    • Talend vs Mulesoft
    • Talend vs Pentaho
    • Talend vs SSIS
    • TensorFlow vs Caffe
    • Tensorflow vs Pytorch
    • TensorFlow vs Spark
    • TeraData vs Oracle
    • Text Mining vs Natural Language Processing
    • Text Mining vs Text Analytics
    • Cloud Computing vs Virtualization
    • Unit Test vs Integration Test?
    • Universal analytics vs Google Analytics
    • Visual Analytics vs Tableau
    • R vs Python
    • R vs SPSS
    • Star Schema vs Snowflake Schema
    • DDL vs DML
    • R vs R Squared
    • ActiveMQ vs Kafka
    • TDM vs FDM
    • Linear Regression vs Logistic Regression
    • Slf4j vs Log4j
    • Redis vs Kafka
    • Travis vs Jenkins
    • Fact Table vs Dimension Table
    • OLTP vs OLAP
    • Openstack vs Virtualization
    • Cluster v/s Factor analysis
    • Informatica vs Datastage
    • CCBA vs CBAP
    • SPSS vs EXCEL
    • Excel vs Tableau
    • Cassandra vs MySQL
    • RabbitMQ vs Kafka
    • SAAS vs Cloud
    • RabbitMQ vs Redis
    • AMQP vs MQTT
    • Forward Chaining vs Backward Chaining
    • Google Data Studio vs Tableau
    • ActiveMQ vs RabbitMQ
    • Cloud vs Data Center
    • Cores vs Threads
    • Inner Join vs Outer Join
    • ZeroMQ vs Kafka
    • Mxnet vs TensorFlow
    • Redis vs Memcached
    • RDBMS vs NoSQL
    • AWS Direct Connect vs VPN
    • Cassandra vs Couchbase
    • Elegoo vs Arduino
    • Redis vs MongoDB
    • Chef vs Puppet
    • GSM vs GPRS
    • Keras vs TensorFlow vs PyTorch
    • Cloudflare vs CloudFront
    • Bitmap vs Vector
    • Left Join vs Right Join
    • IaaS vs PaaS
    • Blue Prism vs UiPath
    • GNSS vs GPS
    • Cloudflare vs Akamai
    • GCP vs AWS vs Azure
    • Arduino Mega vs Uno
    • Qualitative vs Quantitative Data
    • Arduino Micro vs Nano
    • PIC vs Arduino
    • PRTG vs Solarwinds
    • PostgreSQL vs SQLite
    • Metabase vs Tableau
    • Arduino Leonardo vs Uno
    • Arduino Due vs Mega
    • ETL Vs Database Testing
    • DBMS vs File System
    • CouchDB vs MongoDB
    • Arduino Nano vs Mini
    • IaaS vs PaaS vs SaaS
    • On-premise vs off-premise
    • Couchbase vs CouchDB
    • Tableau Dimension vs Measure
    • Cognos vs Tableau
    • Data vs Metadata
    • RethinkDB vs MongoDB
    • Cloudera vs Snowflake
    • HBase vs Cassandra
    • Business Analytics vs Business Intelligence
    • R Programming vs Python
    • MongoDB vs Hadoop
    • MySQL vs Oracle
    • OData vs GraphQL
    • Soft Computing vs Hard Computing
    • Binary Tree vs Binary Search Tree
    • Datadog vs CloudWatch
    • B tree vs Binary tree
    • Cloudera vs Hortonworks
    • DevSecOps vs DevOps
    • PostgreSQL Varchar vs Text
    • PostgreSQL Database vs schema
    • MapReduce vs spark
    • Hypervisor vs Docker
    • SciLab vs Octave
    • DocumentDB vs DynamoDB
    • PostgreSQL union vs union all
    • OrientDB vs Neo4j
    • Data visualization vs Business Intelligence
    • QlikView vs Qlik Sense
    • Neo4j vs MongoDB
    • Postgres Schema vs Database
    • Mxnet vs Pytorch
    • Naive Bayes vs Logistic Regression
    • Random Forest vs Decision Tree
    • Random Forest vs XGBoost
    • DynamoDB vs Cassandra
    • Looker vs Power BI
    • PostgreSQL vs RedShift
    • Presto vs Hive
    • Random forest vs Gradient boosting
    • Gradient boosting vs AdaBoost
    • Amazon rds vs Redshift
    • Bigquery vs Bigtable
    • Data Architect vs Data Engineer
    • DataSet vs DataTable
    • dataset vs dataframe
    • Dataset vs Database
    • New Relic vs Splunk
    • Data Architect and Management Designer
    • Data Engineer vs Data Analyst
    • Grafana vs Tableau
    • MySQL text vs Varchar
    • Relational Database vs Flat File
    • Datadog vs Prometheus
    • Neo4j vs Neptune
    • Data Mining vs Data warehousing
    • DocumentDB vs MongoDB
    • PostScript vs PCL
    • QRadar vs Splunk
    • Qlik Sense vs Tableau
    • DigitalOcean vs Google Cloud
    • PostgreSQL vs Elasticsearch
    • Redshift vs blueshift
    • Gitlab vs Azure DevOps

TensorFlow vs Caffe

By Priya PedamkarPriya Pedamkar

TensorFlow vs Caffe

Difference Between TensorFlow and Caffe

TensorFlow is an open source python friendly software library for numerical computation which makes machine learning faster and easier using data-flow graphs. TensorFlow eases the process of acquiring data, predicting features, training different models based on the user data and refining future results. TensorFlow is developed by brain team at Google’s machine intelligence research division for machine learning and deep learning research. Caffe is a deep learning framework for train and runs the neural network models and it is developed by the Berkeley Vision and Learning Center. Caffe is developed with expression, speed and modularity keep in mind. In Caffe models and optimizations are defined as plain text schemas instead of code with scientific and applied progress for common code, reference models, and reproducibility.

What is TensorFlow?

TensorFlow is cross-platform as we can use it to run on both CPU and GPU, mobile and embedded platforms, tensor flow units etc. TensorFlow is developed in python and C++ programming language which is well suitable for numerical computation and large-scale machine learning and deep learning (neural networks) models with different algorithms and made available through a common layer. TensorFlow can able to train and run different models of deep neural networks such as recognition of hand-written digits, image recognition, natural language processing, partial derivative equation-based models, models related to prediction, and recurrent neural networks.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

What is Caffe?

Caffe is developed in C++ programming language along with Python and Matlab. Caffe’s architecture encourages new applications and innovations. It allows execution of these models on CPU and GPU and we can switch between these using a single flag. Caffe speed makes it suitable for research experiments and industry development as it can process over 60M images in a single day. Caffe provides academic research projects, large-scale industrial applications in the field of image processing, vision, speech, and multimedia. Using Caffe we can train different types of neural networks.

Head To Head Comparison Between TensorFlow and Caffe (Infographics)

Below is the top 6 difference between TensorFlow vs Caffe

TensorFlow-vs-Caffe infographics

Key Differences Between TensorFlow and Caffe

Both are popular choices in the market; let us discuss some of the major difference:

  • The TensorFlow framework is more suitable for research and server products as both have a different set of target users where TensorFlow aims for researcher and servers whereas Caffe framework is more suitable for production edge deployment. Whereas both TensorFlow vs Caffe frameworks has a different set of targeted users. Caffe aims for mobile phones and computational constrained platforms.
  • Both TensorFlow vs Caffe have steep learning curves for beginners who want to learn deep learning and neural network models.
  • Caffe has more performance than TensorFlow by 1.2 to 5 times as per internal benchmarking in Facebook.
  • TensorFlow works well on images and sequences and voted as most-used deep learning library whereas Caffe works well on images but doesn’t work well on sequences and recurrent neural networks.
  • TensorFlow is easier to deploy by using python pip package management whereas Caffe deployment is not straightforward we need to compile the source code.
  • Caffe is targeted for developers who want to experience hands-on deep learning and offers resources for training and learning whereas TensorFlow high-level API’s takes care of where developers no need to worry.

TensorFlow vs Caffe Comparison Table

Below is the 6 topmost comparison between TensorFlow vs Caffe

The Basis Of Comparison 

TensorFlow

Caffe

Easier Deployment TensorFlow is easy to deploy as users need to install the python pip manager easily whereas in Caffe we need to compile all source files. In Caffe, we don’t have any straightforward method to deploy. We need to compile each and every source code in order to deploy it which is a drawback.
Life Cycle management and API’s TensorFlow offers high-level API’s for model building so that we can experiment easily with TensorFlow API’s. It has a suitable interface for python (which is the choice of language for data scientists) for machine learning jobs. Caffe doesn’t have higher level API’s due to which it will be hard to experiment with Caffe, the configuration in a non-standard way with low-level API’s. The Caffe approach of middle-to-low level API’s provides little high-level support and limited deep configurability. Caffe interface is more of C++ which means users need to perform more tasks manually such as configuration file creation etc.
GPU’s In TensorFlow, we can use GPU’s by using the tf.device() in which all necessary adjustments can be made without any documentation and further need for API changes. In TensorFlow, we can able to run two copies of a model on two GPU’s and a single model on two GPU’s. In Caffe, there is no support of tools in python. So all training needs to be performed based on a C++ command line interface. It supports a single style of multi-GPU configuration whereas TensorFlow supports multiple types of multi-GPU configurations.
Multiple Machine support In TensorFlow, the configuration of jobs is straightforward for multi-node tasks by setting the tf. Device to the number of jobs need to run. In Caffe, we need to use MPI library for multi-node support and it was initially used to break apart of massive multi-node supercomputer applications.
Definition A tensorflow framework is more suitable for research and server products as both have a different set of target users where TensorFlow aims for researcher and servers. Caffe framework is more suitable for production edge deployment. Whereas both frameworks have a different set of targeted users. Caffe aims for mobile phones and computational constrained platforms.
Performance, the learning curve A tensorflow framework has less performance than Caffe in the internal benchmarking of Facebook. It has a steep learning curve and it works well on images and sequences. It is voted as most-used deep learning library along with Keras. Caffe framework has a performance of 1.2 to 5 times more than TensorFlow in internal benchmarking of Facebook. It has a steep learning curve for beginners. It works well for deep learning on images but doesn’t work well on recurrent neural networks and sequence models.

Conclusion

Finally, it’s an overview of comparison between two deep learning frameworks. I hope you will have a good understanding of these frameworks after reading this TensorFlow vs Caffe article. TensorFlow framework is a fast-growing one and voted as most-used deep learning frameworks and recently Google has invested heavily in the framework. TensorFlow provides mobile hardware support, low-level API core gives one end-to-end programming control and high-level API’s which makes it fast and efficient whereas Caffe backward in these areas compared to TensorFlow. So TensorFlow has the potential to become dominant in deep learning framework.

Recommended Articles

This has a been a guide to the top difference between TensorFlow vs Caffe. Here we also discuss the key differences with infographics, and comparison table. You may also have a look at the following articles to learn more.

  1. Ubuntu vs Windows 10 – Top Comparison
  2. Winforms vs WPF – Useful Differences
  3. Distinguish Between SOAP vs JSON
  4. TensorFlow vs Keras | Top Differences
Popular Course in this category
TensorFlow Training (11 Courses, 3+ Projects)
  11 Online Courses |  3 Hands-on Projects |  55+ Hours |  Verifiable Certificate of Completion
4.5
Price

View Course

Related Courses

Data Scientist Training (85 Courses, 67+ Projects)4.9
Tableau Training (8 Courses, 8+ Projects)4.8
Azure Training (6 Courses, 5 Projects, 4 Quizzes)4.7
Hadoop Training Program (20 Courses, 14+ Projects, 4 Quizzes)4.7
Data Visualization Training (15 Courses, 5+ Projects)4.7
All in One Data Science Bundle (360+ Courses, 50+ projects)4.7
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2023 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

By continuing above step, you agree to our Terms of Use and Privacy Policy.
*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more