Multi-Class Image Classification on Fashion-MNIST Using a Custom CNN

Abstract

This paper presents a custom lightweight Convolutional Neural Network (CNN) designed from scratch for multi class classification on Fashion-MNIST. The architecture employs progressive convolutional blocks (32/64/128 filters), batch normalization for stable training, max/global pooling for dimensionality reduction, dropout for regularization, and data augmentation to enhance generalization. Implemented in TensorFlow/Keras and trained over 100 epochs with Adam optimization, the model totals just 111,370 parameters.