OpenVINO vs Tensorflow Lite

Overview

Tensorflow Lite

Stacks74

Followers144

Votes1

OpenVINO

Stacks15

Followers32

Votes0

OpenVINO vs Tensorflow Lite: What are the differences?

OpenVINO and Tensorflow Lite are both popular frameworks used for running deep learning models on edge devices. While both frameworks have similar goals, there are several key differences between them.

Model Compatibility: OpenVINO is designed to optimize and run models that have been trained using various frameworks such as TensorFlow, Caffe, and ONNX. On the other hand, Tensorflow Lite is specifically built for running TensorFlow models, making it more limited in terms of model compatibility.
Conversion Process: OpenVINO requires a pre-processing step where the trained model is converted to an Intermediate Representation (IR) format. This IR format can then be loaded and executed by the OpenVINO inference engine. In contrast, TensorFlow Lite uses a converter tool that directly converts TensorFlow models into a format that can be executed by the TensorFlow Lite interpreter.
Hardware Support: OpenVINO is designed to work with a wide range of hardware architectures, including CPUs, GPUs, FPGAs, and VPUs. This allows developers to optimize their models based on the specific hardware platform they are targeting. Tensorflow Lite, on the other hand, primarily focuses on CPU and GPU acceleration, with limited support for other hardware accelerators.
Performance Optimization: OpenVINO provides a set of tools and libraries that enable developers to optimize their models for maximum performance. This includes features such as model quantization, which reduces the precision of model weights to improve inference speed. Tensorflow Lite also provides some performance optimization techniques, but they are more limited compared to OpenVINO.
Runtime Flexibility: OpenVINO allows developers to choose between synchronous and asynchronous execution modes, giving them more control over the trade-off between latency and throughput. Tensorflow Lite, on the other hand, primarily focuses on synchronous execution, which may be more suitable for certain edge devices where real-time performance is critical.
Deployment Flexibility: OpenVINO provides support for a wide range of programming languages, including C++, Python, and Java, allowing developers to choose the language that best suits their needs. Tensorflow Lite primarily focuses on Python and C++, limiting the deployment flexibility for developers who prefer other languages.

In Summary, while both OpenVINO and Tensorflow Lite are popular choices for running deep learning models on edge devices, OpenVINO offers more model compatibility, hardware support, performance optimization, runtime flexibility, and deployment flexibility compared to Tensorflow Lite, which is more limited in these aspects.

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CLI (Node.js)

Manual

Detailed Comparison

Tensorflow Lite	OpenVINO
It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.	It is a comprehensive toolkit for quickly developing applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends CV workloads across Intel® hardware, maximizing performance.
Lightweight solution for mobile and embedded devices; Enables low-latency inference of on-device machine learning models with a small binary size; Fast performance	Optimize and deploy deep learning solutions across multiple Intel® platforms; Accelerate and optimize low-level, image-processing capabilities using the OpenCV library; Maximize the performance of your application for any type of processor
Statistics
Stacks 74	Stacks 15
Followers 144	Followers 32
Votes 1	Votes 0
Pros & Cons
Pros 1 .tflite conversion	No community feedback yet
Integrations
Python Android OS iOS Raspberry Pi	No integrations available

What are some alternatives to Tensorflow Lite, OpenVINO?

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.

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.

Related Comparisons

OpenVINO vs Tensorflow Lite: What are the differences?

Model Compatibility: OpenVINO is designed to optimize and run models that have been trained using various frameworks such as TensorFlow, Caffe, and ONNX. On the other hand, Tensorflow Lite is specifically built for running TensorFlow models, making it more limited in terms of model compatibility.
Conversion Process: OpenVINO requires a pre-processing step where the trained model is converted to an Intermediate Representation (IR) format. This IR format can then be loaded and executed by the OpenVINO inference engine. In contrast, TensorFlow Lite uses a converter tool that directly converts TensorFlow models into a format that can be executed by the TensorFlow Lite interpreter.
Hardware Support: OpenVINO is designed to work with a wide range of hardware architectures, including CPUs, GPUs, FPGAs, and VPUs. This allows developers to optimize their models based on the specific hardware platform they are targeting. Tensorflow Lite, on the other hand, primarily focuses on CPU and GPU acceleration, with limited support for other hardware accelerators.
Performance Optimization: OpenVINO provides a set of tools and libraries that enable developers to optimize their models for maximum performance. This includes features such as model quantization, which reduces the precision of model weights to improve inference speed. Tensorflow Lite also provides some performance optimization techniques, but they are more limited compared to OpenVINO.
Runtime Flexibility: OpenVINO allows developers to choose between synchronous and asynchronous execution modes, giving them more control over the trade-off between latency and throughput. Tensorflow Lite, on the other hand, primarily focuses on synchronous execution, which may be more suitable for certain edge devices where real-time performance is critical.
Deployment Flexibility: OpenVINO provides support for a wide range of programming languages, including C++, Python, and Java, allowing developers to choose the language that best suits their needs. Tensorflow Lite primarily focuses on Python and C++, limiting the deployment flexibility for developers who prefer other languages.

OpenVINO vs Tensorflow Lite

Overview