Towards Fashion Image Annotation: A Clothing Category Recognition Procedure.

In contemporary clothing industry, design, development and procurement teams are constantly asked to present more products with fewer resources in a shorter time. Thus, clothing companies that aim to remain competitive in today’s market have to deploy new Artificial Intelligence techniques aiming at the automation of their traditional procedures. In this direction, the presented approach utilizes a deep learning model to accurately classify fashion images. The predictions are intended to be used on a personalized recommendation system, that acts as an assistant for the fashion designers. Two well established architectures are studied, VGG and ResNet, as well as a variation of ResNet. The realized experiments include: (a) architecture comparison, (b) hyperparameter tuning and classification, and (c) transfer learning. Two fashion datasets are used for the model training and classification: DeepFashion (for training the model from scratch) and iMaterialist (used to evaluate the transferability of the produced model). The results show that the first set of experiments achieved 80.5% accuracy, whereas the pre-trained model used on the second dataset led to a decrease of 60% on training time, while attaining satisfying results. CCS Concepts: • Computing methodologies → Object recognition; Supervised learning by classification; Neural networks; • Applied computing → Consumer products.