Use of a deep learning CNN architecture in product image quality assessment for improving e-commerce customer experience

In the dynamic world of e-commerce, the visual impact of product images holds incredible sway over consumer perceptions and choices. This study delves into the intriguing interplay of artificial intelligence (AI) and e-commerce, proposing a groundbreaking AI-driven model that precisely assesses image quality from the customer's perspective. Through the careful curation of a diverse dataset, we crafted a sophisticated convolutional neural network (CNN) architecture. Impressively, our model achieved a remarkable 98% accuracy on the test dataset, demonstrating its prowess in categorizing images accurately. Moreover, it is imperative to highlight that the dataset itself was meticulously created from scratch, with the images designed and integrated directly into the model. This bespoke dataset served as the cornerstone for training the CNN architecture. While this accomplishment is noteworthy, it's important to acknowledge the limited size of our training data. This raises important considerations about the model's adaptability to a broader range of visual inputs. To address this, we applied innovative techniques such as data augmentation and model regularization, fortifying the model's ability to handle new, unseen data. Furthermore, our research extends beyond model development. We have created an interactive website (Visual Analysis Platform) that allows users to experience firsthand the capabilities of our model in assessing image quality based on the categories established in our research. This platform serves not only for analysis and user engagement but also for collecting user-generated images. These images contribute to our dataset enrichment, aiming for continuous improvement in the model's ability.