The Use of MobileNetV2 for Bag Classification Visualisation of a CNN model based on Deep Learning

The research involves integrating user-centric interactive platforms and mobile applications with bag classification algorithms. Developing user-friendly interfaces that let users interact with and comprehend the model's classifications is necessary to establish confidence. A lightweight CNN model named MobileNetV2 is used in this study to classify bag pictures within a dataset and access it. The major objective is to use MobileNetV2's efficiency to categorise bags fast and accurately. Beyond enhancing bag classification methods, more research in these domains aims to enhance the reliability and flexibility of deep learning systems. Further research might focus on MobileNetV2's generalisation capabilities in bag categorization. The use of innovative domain adaptation strategies, transfer learning schemes, or an increase in augmentation data may be explored in order to ensure the model's robust performance over a range of datasets. The deep learning model is trained using an extensive collection of bag images with varying textures, colours, and shapes. In addition to demonstrating MobileNetV2's classification abilities, the study makes use of visualisation techniques to expose the CNN's inner workings. The traits and patterns that the algorithm found while categorising bags are highlighted via the use of data visualisation tools. The work demonstrates how MobileNetV2 effectively extracts delicate information like precision from bag photos, supporting precise and reliable classification, via the visualisation of the CNN model. This study demonstrates the potential of MobileNetV2 as a practical and flexible deep learning model, with implications for item recognition and classification in many domains.