Image Classification Using SingleStore DB, Keras, and Tensorflow

Abstract

Image classification can have many practical, valuable and life-saving benefits. The "Hello World" of image classification is often considered MNIST and, more recently, Fashion MNIST. This article will use Fashion MNIST and store the images in a SingleStore DB database. We'll also build an image classification model using Keras and Tensorflow and store the prediction results in SingleStore DB. Finally, we'll build a quick visual front-end to our database system using Streamlit that enables us to retrieve an image and determine if the model correctly identified it.

The SQL scripts, Python code and notebook files used in this article are available on GitHub. The notebook files are available in DBC, HTML and iPython formats.

How to Integrate Huawei ML Kit Image Classification in Flutter

Introduction

In this article, we will learn how to implement the Image Classification feature in a Flutter application. Image classification uses the transfer learning algorithm to perform multi-level learning training. Huawei ML Kit provides many useful machine learning-related features to developers, and one of them is Image Classification.

About Image Classification

Image classification is one of the features of the HMS ML Kit. Through this service, we can classify the objects in images. This service analyses an image, classifies it into possible categories in the real world, like people, animals, objects, etc. and it returns the recognized results.

How AI-Powered Computer Vision Is Transforming Healthcare

AI-Powered Computer Vision

The impact of AI on human lives can be felt the most in the healthcare industry. AI-powered computer vision technology can help bring affordable healthcare to millions of people. Computer vision practices are already in place for sorting and finding images in blogs and retail websites. It also has applications in medicine.

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Medical diagnosis depends on medical images such as CAT scans, MRI images, X-rays, sonograms, and other images.