Convolutional Neural Networks for Image Classification

From Fashion-MNIST to Sustainable Circular Economy AI

Abstract

In this paper, an experimental design, implementation and evaluation of a regularized Convolutional Neural Network (CNN) to classify multi-label images on the Fashion-MNIST benchmark are presented and extended to a new and urgent research area: a application of CNN-based image classification to sustainable fashion and textile circular economy systems. The architecture used by the custom CNN consists of three convolutional blocks with increasing filter depth (32, 64, 128 channels), batch normalization, data augmentation (rotation, shift, zoom), dropout regularization, early stopping, and 256-unit dense classification head with categorical cross-entropy loss and Adam as the optimizer and up to 20 epochs. The model attains a test accuracy of about 90% on 10,000-sample Fashion-MNIST test set, and a Macro-average F1-score of very high discriminative power on morphologically distinct categories (Trouser, Bag, Sandal: F1[?]0.97) and poorer on the visually similar garments (Shirt: F1[?]0.74). These findings are put in perspective of the fashion sustainability crisis on the global scale: according to the estimates provided by the United Nations Environment Programme (UNEP), 92 million tonnes of textiles are dumped into landfill each year, which makes up to 8 per cent of the total global greenhouse emissions. CNN-based visual classification The identical convolutional feature extractor, which is proven on Fashion-MNIST, is already used in intelligent textile sorting systems with 93% classification accuracy in 2025 pilot line, and in post-consumer fabric recycling pipelines to sort textiles into recycling streams at 95 percent accuracy at a rate of one item/second (Tsai and Yuan, 2025). The paper builds a technical roadmap of extending the Fashion-MNIST CNN engineering capabilities to the use in the circular economy, recognizes four AI-enabled pillars of the circle economy that are supported by the literature, and suggests specific future research directions on the cross-section of computer vision, sustainability, and regulatory compliance.