Image-Classification-CIFAR-10

CIFAR-10 Dataset

The CIFAR-10 dataset (http://www.cs.toronto.edu/~kriz/cifar.html) is a popular dataset for image classification, collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. It is a labeled subset of the 80 million tiny images dataset.

The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images. The 10 classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.

The following 10 classes mentioned above will be classified using Convolutional neural networks. In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery and we will be using keras (deep learning library) to implement this.

I worked on python 3.6 (jupyter notebook) and used the following libraries for the project:

torch

matplotlib

numpy