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  5. TensorFlow vs Vuforia

TensorFlow vs Vuforia

OverviewDecisionsComparisonAlternatives

Overview

TensorFlow
TensorFlow
Stacks3.9K
Followers3.5K
Votes106
GitHub Stars192.3K
Forks74.9K
Vuforia
Vuforia
Stacks39
Followers50
Votes0
GitHub Stars0
Forks0

TensorFlow vs Vuforia: What are the differences?

Introduction

In this article, we will discuss the key differences between TensorFlow and Vuforia, two popular technologies used in the field of artificial intelligence and augmented reality.

  1. Scopes of Application: TensorFlow is primarily a machine learning and deep learning framework developed by Google. It provides a wide range of functionalities for building, training, and deploying machine learning models. On the other hand, Vuforia is an augmented reality platform that focuses on creating and deploying AR experiences for mobile devices.

  2. Development Language: TensorFlow is mainly written in Python, which is one of the most popular programming languages for data science and machine learning. It also supports other languages like C++, Java, and JavaScript. Vuforia, on the other hand, supports multiple programming languages including C++, Java, and .NET.

  3. Focus on AI vs AR: TensorFlow is designed specifically for AI applications, including machine learning, deep learning, and neural networks. It provides a comprehensive set of tools and libraries for training and deploying AI models. Vuforia, on the other hand, is primarily focused on creating augmented reality experiences by overlaying digital content on the real world.

  4. Community and Ecosystem: TensorFlow has a large and active community of developers, researchers, and enthusiasts. It has a well-established ecosystem with numerous open-source projects, libraries, and frameworks built on top of it. Vuforia, although it also has a community, has a smaller and more specialized ecosystem focused on AR development.

  5. Learning Curve: TensorFlow has a steep learning curve, especially for beginners with no prior experience in AI or machine learning. It requires understanding concepts like tensors, computational graphs, and neural networks. Vuforia, on the other hand, provides a more user-friendly interface and tools for creating AR experiences, making it easier for developers to get started.

  6. Supported Platforms: TensorFlow can be used on a wide range of platforms including desktop computers, servers, mobile devices, and even embedded systems. It supports multiple operating systems such as Windows, macOS, Linux, Android, and iOS. Vuforia is primarily focused on mobile platforms and supports iOS and Android devices.

In summary, TensorFlow is mainly used for AI applications like machine learning and deep learning, while Vuforia is focused on creating augmented reality experiences. TensorFlow has a larger community and ecosystem, but also has a steeper learning curve compared to Vuforia.

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Advice on TensorFlow, Vuforia

Adithya
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!!

107k views107k
Comments

Detailed Comparison

TensorFlow
TensorFlow
Vuforia
Vuforia

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 an augmented reality software development kit for mobile devices that enables the creation of augmented reality applications. It uses computer vision technology to recognize and track planar images and simple 3D objects

-
Allow you to recognize objects by shape using pre-existing 3D models;Attach content onto flat images, such as print media and product packaging;Object Targets are created by scanning an object; Recognize images wrapped onto objects that are approximately cylindrical in shape;Access video data from a camera outside of the one in a phone or tablet when creating AR experiences
Statistics
GitHub Stars
192.3K
GitHub Stars
0
GitHub Forks
74.9K
GitHub Forks
0
Stacks
3.9K
Stacks
39
Followers
3.5K
Followers
50
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
JavaScript
Android SDK
Android SDK
Unity
Unity
Cocoa Touch (iOS)
Cocoa Touch (iOS)

What are some alternatives to TensorFlow, Vuforia?

scikit-learn

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

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

Keras

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

Kubeflow

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

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

Polyaxon

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

Streamlit

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

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

H2O

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

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

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