Deep learning is a new era of machine learning research, where many layers of information processing stages are exploited for unsupervised feature learning. Using multiple levels of representation and abstraction, it helps a machine to understand about data (e.g., images, sound and text) more accurately. Many deep learning models have been proposed for solving the problem of different applications. Therefore, a comprehensive knowledge of these models is demanded to select the appropriate one for a specific application areas in signal or data processing. This paper reviews several deep learning models proposed for different application area in the field of computer vision, and makes a comprehensive evaluation of two well-known models namely AlexNet and VGG_S in nine different benchmark datasets. The experimental results show that these two models perform better than the existing state-of-the-art deep learning models in one dataset.