Machine Learning has become one of the cool technologies in the recent times, almost every software product out in market uses ML in one or the other way. Let’s see how to build an application that can upload images to server and make predictions on it (image classification ). These images can be accessed by an app and you can simply search an image by its content. We will use Flask (Python framework) as back end for our REST API, Flutter for mobile app and Keras for image classification. We will also use MongoDB as our database to store data about the images and classify images using Keras ResNet50 model.
Flutter is an open-source mobile application development framework created by Google. It is used to develop applications for Android and iOS, as well as being the primary method of creating applications for Google Fuchsia
Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.
Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions