ml5.js vs OpenVINO | What are the differences? | StackShare

OpenVINO vs ml5.js

Overview

ml5.js

Stacks5

Followers53

Votes0

GitHub Stars6.6K

Forks908

OpenVINO

Stacks15

Followers32

Votes0

OpenVINO vs ml5.js: What are the differences?

Developers describe OpenVINO as "A free toolkit facilitating the optimization of a Deep Learning model". 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. On the other hand, ml5.js is detailed as "Friendly machine learning for the web". ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.

OpenVINO and ml5.js can be categorized as "Machine Learning" tools.

ml5.js is an open source tool with 4.09K GitHub stars and 418 GitHub forks. Here's a link to ml5.js's open source repository on GitHub.

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Detailed Comparison

ml5.js	OpenVINO
ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.	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.
Pre-trained models for detecting human poses, generating text, styling an image with another, composing music, pitch detection, and common English language word relationships; API for training new models based on pre-trained ones as well as training from custom user data from scratch	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
GitHub Stars 6.6K	GitHub Stars -
GitHub Forks 908	GitHub Forks -
Stacks 5	Stacks 15
Followers 53	Followers 32
Votes 0	Votes 0

What are some alternatives to ml5.js, 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.