DATA AUGMENTATION METHOD TO IMPROVE THE QUALITY
 OF E-COMMERCE IMAGE RECOGNITION

Abstract

In the rapidly evolving landscape of e-commerce, the visual representation of products plays a pivotal role
 in engaging consumers and driving conversion rates. This article introduces a new approach for image augmentation
 that includes objects segmentation, dominant color determination, background replacement and realistic
 shadow generation. These steps collectively contribute to the creation of augmented images that are used not only
 in the electronic catalogues but enrich abilities of the neural networks with various and fortified training data. Developed
 system allows to solve problems related to class imbalance and to enhance model generalization as well as to improve the quality of recognition.