Teaching computers how to recognize people and objects from visual cues in images and videos is an interesting challenge. The computer vision and pattern recognition communities have already demonstrated the ability of intelligent algorithms to detect and classify objects in difficult conditions such as pose, occlusions and image fidelity. Recent deep learning approaches in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) are built using very large and deep convolution neural network architectures. In 2015, such architectures outperformed human performance (94.9% human vs 95.06% machine) for top-5 validation accuracies on the ImageNet dataset, and earlier this year deep learning approaches demonstrated a remarkable 96.43% accuracy. These successes have been made possible by deep architectures such as VGG, GoogLeNet, and most recently by deep residual models with as many as 152 weight layers. Training of these deep models is a difficult task due to compute intensive learning of millions of parameters. Due to the inevitability of these parameters, very small filters of size 3x3 are used in convolutional layers to reduce the parameters in very deep networks. On the other hand, deep networks generalize well on other datasets and outperform complex datasets with less features or Images.