Estimation of Model Capacity for Image Classification

Convolutional neural network (CNN) has shown phenomenal results on several image classification applications. The performance of CNN is improved with deeper architectures. However, increasing depth comes with certain drawbacks like overfitting and need for more computational resources. Thus, it is necessary to choose optimal depth for a network. In this work, the empirical relation between model depth, capacity and complexity of data is estimated using proposed extended VGGNET (EVGGNET). The EVGGNET consists of feature extraction and classification network. The feature extraction network is divided into pre-trained and extended sections which extract the significant features. The classification network uses these features to classify input image into one of the categories. The basic idea behind EVGGNET is to extend the pre-trained feature extraction network by adding convolutional layers which help to establish the model capacity relationship. VGGNET is used as a pre-trained feature extraction network. The experiments are performed on benchmark datasets Caltech 101 and Caltech 256. The results show that the accuracy of EVGGNET is almost exponentially proportional to the complexity of data.