AI Driven Fashion Accessories Identifier Using Machine Learning Techniques by Image Classification

In this project, we study image classification using the Fashion MNIST data with 70,000 grayscale images classed into t-shirts, dresses and sneakers. We want to create a model that can spot various clothing types correctly, as this will support fashion recommendation systems, automate tagging and handle inventory tasks. The work is performed using Convolutional Neural Networks (CNNs) and is carried out using TensorFlow and Keras. Normalisation of data is done so that the model’s training process will use consistent pixel values. In CNN, layers called convolutional are used to detect features, max-pooling for decrease in data and dense layers for classifying what’s found. Using ReLU activations helps the neural network better learn, also stopping overfitting. A sparse categorical cross entropy loss is used to compile the model and it is optimised using the Adam optimizer, trained periodically using a validation split. It is clear from the model that both accuracy and loss decrease during the training process. Following testing on the test set, its strength shows with a 91% accuracy. This result confirms that CNNs can beapplied to perform real-world image classification tasks.