An Efficient Multiple Convolutional Neural Network Model (MCNN-14) for Fashion Image Classification

The transformation of fashion through online platforms has spurred a need for high-quality clothing search engines, facilitating seamless product discovery for global consumers. However, this transition has brought forth challenges in categorization and description standards among retailers and search engines, stemming from the inherent complexity and variability of fashion items. To address these challenges, deep learning techniques like Multiple Convolutional Neural Networks (MCNNs) have gained prominence in the fashion industry. MCNNs offer autonomous learning and enhanced feature extraction capabilities, proving successful in various classification tasks, including clothing classification. In our research, we implemented a multiple-CNN architecture inspired by recent architectural innovations to elevate classification accuracy while preserving computational efficiency. We attained a classification accuracy of 93.08%, surpassing previous benchmarks. However, the integration of multiple CNNs introduces complexities such as heightened computational demands and resource requirements. To strike a balance between achieving superior accuracy and managing the inherent complexities in clothing image classification, we designed the MCNN-14 model.