Nowadays fashion image retrieval is one of the key tasks in the e-commerce sphere. In recent years, several different approaches have emerged to help solve this problem. In this paper, the algorithm for fashion image retrieval in the e-commerce sphere using YOLOv8 and attention model is presented. The innovation of the proposed algorithm is its modular architecture, which allows for more efficient extraction of image features and their further analysis using an attention model. One more advantage of the algorithm is the possibility to use its modules independently so that not the only image retrieval task could be solved. The algorithm is validated in the real e-commerce application and compared with existing e-commerce retrieval algorithms proving to successfully solve the task. Experiments showed that the developed algorithm achieved state-of-the-art results, reaching an accuracy of 94% for top-3 and 96% for top-5 image retrieval, with real-time speed on a GPU.