An Analysis of Convolution Neural Network for Image Classification using Different Models
L Sushma, K.P. Lakshmi · Zenodo (CERN European Organization for Nuclear Research) · 2020
This paper presents an analysis of the performance of the Convolution Neural Networks (CNNs) for image identification and recognition using different nets. A variety of nets are available to test the performance of the different networks. Popular benchmark datasets like ImageNet, CIFAR10, CIFAR100 are used to test the performance of a Convolution Neural Network. This study focuses on analysis of three popular networks: Vgg16, Vgg19 and Resnet50 on ImageNet dataset. These three networks are first realized using Keras and Tensorflow for image classification on ImageNet dataset. The random set of annotated images from the internet are subjected to these three networks for classification and analyzed for accuracy. It is observed that that ResNet50 is able to recognize images with better precision compared to Vgg16 and Vgg19.