Optimizing Image Classification Performance with MnasNet Model on Blurred Images - Puspita, Rani; Izdihar, Zahra Nabila (Universitas Islam Negeri Sultan Syarif Kasim Riau, 2024)
In this era, the development of fashion in clothing is increasing. Over the last 30 years, the fashion industry has experienced significant improvements, causing its growth and development to increase. Fashion has many types and variants, but blurry images can also make it difficult for people to classify whether this is a shirt, t-shirt, or something else. Because of that, we proposed image classification. By classifying images, we can help the fashion industry to separate categories and types of various fashion. The approach uses MnasNet which is included in the deep learning approach. The data used is 70,000 which is divided into 60,000 training data and 10,000 testing data. The MnasNet architectural model produces an accuracy of 89% and a loss of 0.4426. It can be seen that MnasNet is the right method for image classification so that the problems described in the background have been successfully solved.