DETECTION AND CLASSIFICATION OF SEASON CLOTHES USING ML ON THE EDGE

Abstract

The creation of a machine learning (ML) model for the automatic identification of different types of seasonal clothes was the main goal of our research. The objective was to categorize into four main groups: winter clothes, trousers, long-sleeve, and short-sleeve items. Our goal was to facilitate the precise and efficient identification of different kinds of clothing products by utilizing Convolutional Neural Network (CNN) algorithms about their seasonal properties. We collected a dataset of over 1000 photos that represented the four-goal categories to train and evaluate our model. Carefully tagged images were used to enable supervised learning. The model gained the ability to recognize patterns and characteristics typical of various seasonal clothes through a process of training and validation, facilitating precise categorization. After a long training and validation process, our CNN model performed admirably in the classification of seasonal cloth. The classification accuracy of the model was higher than 80%. This high accuracy demonstrates the effectiveness of our method and its potential for useful use in real-world settings.