Smart Wardrobe : A Deep Learning Model

The concept of a smart wardrobe has been gaining traction in recent years, as it offers the potential to make our lives easier, more efficient, and more fashionable. A smart wardrobe is a deep-learning model that uses data algorithms to suggest the best clothing items for a given day. It also takes into account the user’s personal style, allowing them to create a unique and personalized wardrobe. In this paper, we will explore the potential of a smart wardrobe, from its technical components and implications to the potential for customization and personalization. We will also look at the current state of the smart wardrobe industry and the outlook of this technology. Every area of our lives is being diligently attempted to be digitalized in this age of digitalization. However, people are still managing their clothes in the same laborious and antiquated way, which takes a lot of their time and work. Finding clothing can be painful, difficult, and time-consuming. People can benefit greatly from an automated method for suggesting clothes that uses a straightforward web application. Our work will be able to suggest clothing styles based on the latest trends from the image uploaded of the clothes in the wardrobe. The dataset of forty thousand images is acquired from Kaggle.com for training the Deep Learning model. Based on user input the model gives the user five closest matching outputs of the same type of clothing. For our work we have implemented the TensorFlow image processing library wherein we have used the tensorflow.keras.applications.resnet50 pre-trained weights.