Models and means of clothing elements patterns classification using machine learning

Abstract

The task of pattern classification remains relevant in the fields of trends, style, fashion, personalization, manufacturing, and design. Research aimed at the design and development of models and means of classification of patterns of clothing elements using machine learning is highlighted. The study addresses a pertinent issue in computer vision, namely: increasing the efficiency of classification of patterns of clothing elements. The research was conducted with a proprietary dataset comprising 600 images. The following patterns are defined for classification: “checkered”, “dotted”, “vegetation/floral”, “print”, “solid”, “striped”. A convolutional neural network was developed using the Python programming language and deep learning frameworks Keras and TensorFlow. The scalable Keras-Tuner framework was used to optimize the hyperparameters of the developed network. The structure of the convolutional neural network includes an input layer, a feature extraction part, and a pattern type determination part. The architecture of the applied convolutional neural network is described. The CUDA Toolkit, the cuDNN library and the WSL layer are applied to train a convolutional neural network using a GPU, significantly speeding up the training process. Metrics including accuracy, precision, and recall were used to evaluate the developed convolutional neural network. The web application is developed in the Python programming language with the FastAPI framework. The web application has a described API for interacting with a convolutional neural network, and uses the Pillow (PIL) libraries for working with images and Rembg for image background removal. The user interface is developed in the JavaScript programming language with HTML, CSS and the React framework. The user interface is presented as an intuitive tool for interacting with the system. The developed software uses the modular principle, which allows for rapid modernization of the software. As a result of applying transfer learning, a testing accuracy of 93.33% was achieved, and with fine-tuning, the final version of the convolutional neural network for the classification of patterns of clothing elements with a test accuracy of 95% was obtained. The trained neural network was tested on new images of the specified types of patterns, examples for two patterns are given.