Convolutional Neural Networks and Vision Transformers in Product GS1 GPC Brick Code Recognition
[ 1 ] Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ 2 ] Instytut Inżynierii Bezpieczeństwa i Jakości, Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ SzD ] doctoral school student | [ P ] employee
2024
chapter in monograph / paper
english
- GPC codes
- artificial intelligence
- machine learning
- ResNet-50
- VGG-16
- InceptionV4
- product data quality
EN Online stores and auctions are commonly used nowadays. It means that we buy much more on the Internet than in traditional stores. It leads to the case that during looking for the products we need to have precise categories assigned to each of them (to find only records that can be of interest for a consumer). Sometimes it is hard, users make simple mistakes by assigning wrong categories to the product they sell. In this paper, we propose an approach to the analysis of product images and their real categories assignment. The proposed algorithm is based on Convolutional Neural Networks (CNNs). Vision Transformers were also tested and compared with CNNs. Products categories were represented by GS1 GPC brick codes. The maximum accuracy reached around 80%. Based on the discussions with e-commerce experts, it was claimed that such precision is acceptable, as the differences between real and assigned categories were effectively small (change in the class does not segment or family).
20.01.2024
440 - 450
20
20
70