The fashion industry has undergone a transformation thanks to the rapid
development of computer vision, which has enabled the automatic classification and
analysis of clothing products. In this work, Convolutional Neural Networks (CNNs) are
used to categorize a varied fashion dataset with more than 15,000 images from Kaggle.
The dataset's diversity of clothes and accessory categories reflects the intricacy of realworld fashion datasets. The authors focused on data preprocessing, augmentation, and
fine-tuning to enhance the model's classification accuracy using a CNN. The aim of
this work is to demonstrate how well CNNs handle visual diversity and identify
patterns in the dataset while addressing issues such as dataset imbalance and inter-class
similarity.
For validation of the proposed work, the authors have calculated accuracy, precision,
recall, and F1-score. The outcome of the CNN model demonstrates that Artificial
Intelligence (AI) has its application in the fashion industry, and performance metrics
justified the work. The confusion metric highlights the merits and demerits of the CNN
model. In the future, a bigger dataset could be utilized for the incorporation of transfer
learning, resulting in enhanced accuracy.
Keywords: Artificial Intelligence, Convolutional neural networks, Fashion industry, Detection, Classification, Image-based recommendation systems.
