Learn what is Keras and OpenCV with their applications. See Keras vs OpenCv to understand differences between OpenCv and keras for proper understanding.
OpenCV is the open-source library for computer vision and image processing tasks in machine learning. OpenCV provides a huge suite of algorithms and aims at real-time computer vision. Keras, on the other hand, is a deep learning framework to enable fast experimentation with deep learning. In this Keras Tutorial, we will learn about Keras Vs OpenCV.
First, we will see both the technologies, their application, and then the differences between keras and OpenCv.
Computer Vision is defined for understanding meaningful descriptions of physical objects from the image.
OpenCV was built to provide an infrastructure for computer vision. This library has a huge range of optimized machine learning and computer vision algorithms. These algorithms include object identification, detecting and recognizing faces, object movement tracking, etc. OpenCV provides support for C++, Python, Java and MATLAB programming languages and works on Windows, Linux, Android and Mac Operating Systems.
The common features in OpenCV are read and write images, save and capture images/videos, filter or transform the image, detecting faces,eyes,cars in images or videos, perform feature detection, background subtraction, and tracking objects.
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Learn about Keras Ecosystem components like Keras tuner, auto keras, TFX, Model Optimization Toolkit, Tensorflow Lite, Tensorflow.js and their features.Keras, other than being a high-level deep learning API also has some other initiatives for machine learning workflow. There is a wide range of machine learning frameworks whose development is based on Keras. In this article, we will discuss Keras Ecosystem. This ecosystem of frameworks tries to ease and optimize the current approach of training and deploying ML models. Keras Ecosystem Keeping you updated with latest technology trends, Join DataFlair on Telegram Keras Ecosystem Some of the frameworks for Keras Ecosystem are: 1. Auto Keras This framework was built at the DATA lab with an ambition of making machine learning accessible to everyone. It is a simple interface to perform many machine learning tasks. The supported tasks in auto Keras are image classifier, image regression, text classification, text regression, structured data classification, and structured data regression.