Near-duplicate image detection has become important and widely used in the era of big data. This category of task can be very challenging for deep learning methods due to a number of reasons. Firstly, because most of the data available is noisy and can be hard to clean, costing a vast amount of time and effort to generate a big dataset to feed the deep neural network. Moreover, it is crucial to select a well-suited architecture and optimized training that handles the input data well and outputs rightful solution. In this project, I developed an end to end deep convolutional neural network (CNN) to address the problem of recognizing the near-duplicate images in a set by producing a spatial representation for each image.