Convolutional Neural Network Based Image Classification and it’s Comparison with Vgg-16 to Measure Accuracy

This study aims to classify images using CNN and its performance is computed by comparing them with the VGG16 algorithm. 40, 000 images is taken from the CIFAR-10 dataset as a sample size for the training and testing purpose. The performance of the CNN algorithm is estimated from its accuracy value. The object classification accuracy for CNN and VGG16 are observed. The accuracy of the images is calculated from CNN and VGG16 algorithm, which shows that CNN performs better as compared to VGG16 algorithm in the classification of images.