Image Classification Using Convolutional Neural Networks

From Fashion-MNIST Benchmarking to Smart Retail AI and Edge Deployment

Abstract

The given paper explores the issue of Convolutional Neural Network (CNN)-based image classification on the Fashion-MNIST dataset and elaborates on the findings in relation to the impacts on smart retail artificial intelligence and the process of its implementation into practice through transfer learning and edge computing. It is a custom three-block sequential CNN, which consists of progressive convolutional layers consisting of 32, 64, and 128 filters, max-pooling, fully connected dense layer of 128 neurons, and a 10-class softmax output, trained, and evaluated on 10 epochs with the Adam optimizer and sparse categorical cross-entropy loss in TensorFlow/Keras. This model attains an approximate test accuracy of 88.8 per-class F1-scores are high in categories that are morphologically distinct, like Trouser, Bag, and Sandal (F1[?] 0.97) and lowly with those that are closely similar in appearance like Shirt and T-shirt/top by the greyscale as they may be inter-classes. These empirical results are conceptually mapped into real-world implementation scenarios: automated retailing, e-commerce product tagging with AI, virtual try-on, and fashion recognition on the edge. The article also discusses transfer learning models, specifically MobileNetV2 and EfficientNetB3, as computationally efficient frameworks to be used in computing resource-limited deployment settings, comparing their parameter efficiency and inference rate with the specialized architecture. The business environment of AI-in-fashion is the global market, which is currently USD 2.23 billion with a compound annual growth rate of 39 percent (SmartDev, 2025) and is expected to increase to USD 60 billion in 2034.