Progressive 3-Layered Block Architecture for Image Classification

Convolutional Neural Networks (CNNs) have been used to handle a wide range of computer vision problems, including image classification and object detection. Image classification refers to automatically classifying a huge number of images and various techniques have been developed for accomplishing this goal. The focus of this article is to enhance image classification accuracy implemented on CNN models by using the concept of transfer learning and progressive resizing with split and train strategy. Furthermore, the Parametric Rectified Linear Unit (PReLU) activation function, which generalizes the standard traditional rectified unit, has also been applied on dense layers of the model. PReLU enhances model fitting with almost little significant computational cost and low over-fitting hazard. A "Progressive 3-Layered Block Architecture" model is proposed in this paper which considers the fine-tuning of hyperparameters and optimizers of the Deep network to achieve state-of-the-art accuracy on benchmark datasets with fewer parameters.