In this project we will do small image classification using CIFAR10 dataset in tensorflow. We will use convolutional neural network for this image classification problem.
First we will train a model using simple artificial neural network and then check how the performance looks like and then we will train a CNN and see how the model accuracy improves.
This tutorial will help us to understand why CNN is preferred over ANN for image classification.
.Dataset: CIFAR-10 is an image dataset which can be downloaded from here. It contains 60000 tiny color images with the size of 32 by 32 pixels. The dataset consists of 10 different classes (i.e. airplane, automobile, bird, cat, deer, dog, frog, horse, ship and truck), in which each of those classes consists of 6000 images. On the other hand, CNN is used in this project due to its robustness when it comes to image classification task.
--> link to download the dataset : https://www.cs.toronto.edu/~kriz/cifar.html