Machine learning frameworks and image processing platforms

If you want to move beyond using simple AI algorithms, you can build custom image processing AI models. To make development a bit faster and easier, you can use special platforms and frameworks. Below, we take a look at some of the most popular ones:

TensorFlow

Google’s TensorFlow is a popular open-source framework with support for machine learning and deep learning. Using TensorFlow, you can create and train custom deep learning models. The framework also includes a set of libraries suitable for image processing projects and computer vision applications.

PyTorch

PyTorch is an open-source deep learning framework initially created by the Facebook AI Research (FAIR) lab. This Torch-based framework has Python, C++, and Java interfaces.

Among other things, you can use PyTorch for building computer vision and natural language processing applications.

Keras Core

Keras Core (also referred to as Keras 3.0) is a high-level API for creating and training deep learning models with a user-friendly interface.

Being rebased on top of a modular backend architecture, Keras Core makes traditional Keras workflows available on top of arbitrary deep learning frameworks like PyTorch and TensorFlow. With its help, you can design, train, and deploy all kinds of deep learning models.

MATLAB Image Processing Toolbox

MATLAB is an abbreviation for matrix laboratory. It’s the name of both a popular platform for solving scientific and mathematical problems and a programming language. MATLAB provides an Image Processing Toolbox (IPT) including multiple algorithms and workflow applications for AI-based picture analysis, processing, and visualization as well as for algorithm development.

MATLAB IPT allows you to automate common image processing workflows. This toolbox can be used for noise reduction, image enhancement, image segmentation, 3D image processing, and other tasks. Many of the IPT functions support C/C++ code generation, so they can be used for deploying embedded vision systems and desktop prototyping.

Microsoft Computer Vision

Computer Vision is a cloud-based service provided by Microsoft that gives you access to advanced algorithms for image processing and data extraction.

Microsoft Computer Vision allows you to analyze visual features and characteristics of an image, moderate image content, and extract text from images.

Google Cloud Vision

Cloud Vision is part of the Google Cloud platform that offers a set of image processing features. It provides an API for integrating such features as image labeling and classification, object localization, and object recognition.

Cloud Vision allows you to use pre-trained machine learning models or create and train custom AI for image processing.

Google Colaboratory (Colab)

Google Colaboratory, otherwise known as Colab, is a free cloud service that can be used for developing deep learning applications from scratch. Colab makes it easier to use popular libraries and frameworks such as OpenCV, Keras Core, and TensorFlow when developing an application for image processing using AI.

Google Colab is based on Jupyter Notebooks, allowing AI developers to share their knowledge and expertise in a comfortable way. Plus, in contrast to similar services, Colab provides free GPU resources.

In addition to different libraries, frameworks, and platforms, you may also need a large database of images to train and test your model.

There are several open databases containing millions of tagged images that you can use for training your custom machine learning applications and algorithms. ImageNet, Pascal VOC, MNIST, and MS COCO are among the most popular free databases for image processing.

To learn more, read the full article at the Apriorit blog, where we talk about digital image processing and AI and describe some AI image processing tools and techniques for developing intelligent applications. We also take a look at the most popular neural network models for working with images and videos.