ResNet50 Utilization for Bag Classification: A CNN Model Visualization Approach in Deep Learning

The research work revolves around integrating user-centric interactive platforms and mobile applications with algorithms designed for bag classification. It is imperative to develop user-friendly interfaces that allow users to engage with and comprehend the model's classifications, fostering confidence. In this study, a lightweight CNN model known as ResNet50 is employed to categorize bag images within a dataset, with the primary goal being to leverage ResNet50's efficiency for swift and accurate bag classification. Beyond the enhancement of bag classification methods, ongoing research in these domains seeks to improve the reliability and adaptability of deep learning systems. Future investigations may concentrate on exploring ResNet50's generalization capabilities in bag categorization. Innovative strategies such as domain adaptation, transfer learning, or an augmentation of data could be explored to ensure the model's robust performance across diverse datasets. The deep learning model undergoes training using an extensive collection of bag images featuring diverse textures, colors, and shapes. In addition to showcasing ResNet50's classification prowess, the study employs visualization techniques to reveal the inner workings of the CNN. The characteristics and patterns discerned by the algorithm during the bag categorization process are highlighted through data visualization tools. The research underscores how ResNet50 adeptly extracts nuanced information from bag images, thereby facilitating precise and reliable classification through the visualization of the CNN model. Overall, this study underscores the potential of ResNet50 by showing 98% precision as a pragmatic and versatile deep learning model, offering implications for item recognition and classification across various domains.