Object Classification: Performance Evaluation of CNN based Classifiers using Standard Object Detection Datasets

Abstract

The process of recognizing and classifying objects in a given image is known as Object Classification. Presently, the classification of objects in images is performed using Convolutional Neural Networks (CNNs). The CNNs have ability to extract features from a given image and by using Softmax Layer they can perform classification task. In recent years, several classifiers based on CNNs are introduced. The CNN architectures such as AlexNet, VGG Net, ZF Net, Google Inception Net, Microsoft ResNet, Dense Net and Spatial Transformer Networks are used as image classifiers. In this work, authors have used CNN based image classifiers to perform classification task on standard image datasets. The image datasets used in this work are Kaggle- Dog v/s Cat, MNIST, CIFAR-10 and Tiny ImageNet dataset. The authors have presented a comparison of CNN based image classifiers evaluated on different object detection datasets by measuring their accuracy, error rate, precision and recall metrics. The results obtained provide insight for selection of a particular CNN based image classifier on the basis of image dataset characteristics.