Using CNN for Image Classification on CIFAR-10 Dataset

CIFAR-10 Image classification using CNN (Image Source: Author)

Convolutional neural network, CNN is a type of deep learning neural network which is commonly used for image recognition, image classification, objects detection etc.

CIFAR-10 is a very popular computer vision dataset provided by the Canadian Institute For Advanced Research (CIFAR). This dataset is used in many types of deep learning research for object recognition. Details about the CIFAR-10 dataset are available here.

Image Source: https://www.cs.toronto.edu/~kriz/cifar.html

The ‘10’ in this CIFAR-10 dataset refers to 10 classes. These 10 classes as shown in the above image are — Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship and Truck respectively.

There are 60,000, 32x32 color images from these 10 classes, with 6000 images from each class. The training dataset contains 50,000 training images and the test dataset contains 10,000 test images.

The CIFAR-10 dataset images are of color images with a (32 x 32) resolution. All the images in the dataset are of shape (32,32,3) where 3 represents the number of channels i.e R-G-B (Red,Green & Blue).

Let’s start by importing all required libraries and the dataset.