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Computer vision models are designed to translate visual data based on features and contextual information identified during training. This enables models to interpret images and videos and apply those interpretations to predictive or decision-making tasks. Here are the top 10 platforms where you can find computer vision models in 2022.
OpenCV is an open-source machine learning and computer vision software library. Created with a view of providing a common infrastructure for computer vision applications, OpenCV allows access to 2,500-plus classic and state-of-the-art algorithms. These algorithms are useful for several tasks, including face detection and recognition, red-eye removal, object identification, extraction of 3D models of objects, tracking moving objects, and stitching multiple frames together into a high-resolution image. OpenCV has multiple interfaces like C++, Python, Java, and MATLAB, and it supports most operating systems, including Windows, Android, Linux, and Mac.
TensorFlow is among the most popular end-to-end open-source machine learning platforms with a comprehensive set of tools, resources, and libraries. TensorFlow is especially useful for building and deploying applications related to vision that are powered by machine learning. TensorFlow is one of the easiest computer vision tools and allows users to develop computer vision-related machine learning models for tasks like facial recognition, image classification, object detection, and more. Tensorflow, like OpenCV, also supports various languages like Python, C, C++, Java, and JavaScript.
CUDA (short for Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model developed by NVIDIA. It allows developers to use the power of GPUs (Graphics Processing Units) to make processing-intensive applications faster. The toolkit includes the NVIDIA Performance Primitives (NPP) library that provides GPU-accelerated image, video, and signal processing functions for multiple domains, including computer vision. In addition, the CUDA architecture is useful for a wide range of tasks like face recognition, image manipulation, rendition of 3D graphics, and others. Real-time image processing with Nvidia CUDA is supported for Edge AI implementations, to run on-device AI inference on edge devices such as the Jetson TX2.
Viso Suite is an end-to-end computer vision platform for businesses to build, deploy and monitor real-world computer vision applications. The Viso platform allows businesses to build their own computer vision solution faster to market. It provides a robust infrastructure to deploy an AI model to a fleet of edge devices. The tools are optimized to build and monitor a large-scale computer vision system (for industrial automation, visual inspection, remote monitoring, and more). It provides a workspace that comes with a large library of AI models, application templates, edge device management, visual editor, dashboards, and more.
MATLAB is a programming platform that is useful for a range of different applications such as machine learning, deep learning, image, video, and signal processing. It comes with a computer vision toolbox that has multiple functions, apps, and algorithms to help you design solutions for tasks related to CV.
Keras is a Python-based open-source software library that acts as an interface for the machine learning platform TensorFlow. It is especially suited for beginners as it allows one to build a neural network model quickly while providing backend support.
SimpleCV is an open-source collection of libraries and software that allows you to develop machine vision applications easily. Through its framework, you gain access to several high-powered computer vision libraries such as OpenCV without the need of possessing in-depth knowledge about complex concepts like bit depths, color spaces, buffer management, or file formats. SimpleCV is written in Python and is compatible with multiple operating systems such as Mac, Windows, and Linux.
BoofCV is a Java-based computer vision software that is specially written for real-time computer vision solutions. It is open-source and is released under an Apache 2.0 license that makes it free to use for academic and commercial purposes. It is a complete library with all the basic and advanced features that one may require to develop a CV application.
CAFFE or Convolutional Architecture for Fast Feature Embedding is deep learning and computer vision framework developed at the University of California, Berkeley. This framework is written in the C++ programming language and supports multiple deep learning architectures related to image classification and segmentation. It is especially useful for research purposes and industrial implementation due to its excellent speed and image processing capabilities.
OpenVINO (Open Visual Inference and Neural Network Optimization) is a set of comprehensive computer vision tools that are useful for developing applications emulating human vision. Developed by Intel, it is a free-to-use cross-platform toolkit. The OpenVINO toolkit comes with models for several tasks like object detection, face recognition, colorization, movement recognition, and more.
