Fashion Vision using Convolutional Neural Network

The rapid expansion of e-commerce platforms and online fashion markets, there is an increasing demand for intelligent systems that can accurately identify and classify fashion items from images. However, traditional machine learning techniques such as SVMs, KNNs, and decision trees rely on manual feature extraction and struggle with high-dimensional image data. These limitations, along with the high variability in clothing styles, textures, lighting conditions, and visual similarities between categories, result in poor accuracy and limited scalability in real-world applications. To overcome these challenges, this paper proposes a deep learning-based solution using a lightweight and customized Convolutional Neural Network (CNN) architecture tailored for fashion image recognition. Developed in Python using TensorFlow and Keras on Google Colab, the model utilizes effective preprocessing techniques such as image normalization, augmentation, reshaping, and scaling to improve consistency, training performance, and generalization. The CNN was trained on the Fashion MNIST dataset and achieved over 91% classification accuracy, with strong precision and recall across multiple clothing types. Performance was validated through confusion matrix analysis and accuracy/loss plots, confirming the model's ability to distinguish even visually similar fashion items. The results demonstrate the practical value of CNNs in enhancing the accuracy, efficiency, and scalability of fashion recognition systems in modern e-commerce and mobile applications.