Image Classification for Different Types of Artificial and Living Things

Abstract

Image classification is an essential requirement in today’s life. Classifying the images will resolve the major problems of getting ahead with the present world by extending this it will be the base structure to many other projects. Having a collection of many such images, and classifying them into living and artificial things is the major difficulty. A living thing can be described as the things that have life and they can be dead. An artificial thing is a thing that is human-made and has external help. In this project I will be concentrating on two aspects they are: firstly taking a dataset that contains a few artificial like mountains, buildings, glaciers, streets, sea and living things like forests with these, the model will be trained in such a way that model can predict the given image is belonging to which category, secondly it will show the confusion matrix which will help us to observe how the trained model is working and also the number of images in each category using deep learning algorithm like a conventional neural network. This project is aimed to find the best model with the highest accuracy and precision results.