Introduction

A model is a system or a product that acts to achieve a specified purpose. The object detection and facial detection models were made under the notion of data science. Therefore, the overall purpose of this study is to identify 3 different factors: training accuracy, validation accuracy and cross entropy.The motivation in creating these 2 models can be identified from our current era. This generation is one of high technological advancements. In this decade, the human civilization has created functions where technology can identify different objects. For instance, nowadays, the smartphone is able to detect the varying features of our face through the insertion, collection and utilization of such data. I was interested in identifying how technology and humanity intertwine. Through this process, I found a changed perspective to data science.  In this work, we explore the object detection and facial detection models using Tensorflow. The significance of data science in my 2 models is not how the model functions, but to what extent its accuracy is. In addition, I was able to enhance my compiling and analyzing attributes through exploring and understanding the patterns of the given data.

About TensorFlow

• TensorFlow is the recollection of data, training models, serving predictions and refines future possible results [1]

• TensorFlow involves the bundling of different machine learning and deep learning models for data use

• This system allows individuals to create a dataflow graphs and display structures which describe the movement of the given data according to the graph.