UBL Retail is a computer vision pipeline developed for Unilever Bangladesh to analyze product placement, sales trends, and distribution in retail stores. By leveraging AI, machine learning, and deep learning, it provides actionable insights to optimize sales strategies and improve product distribution.

Using the YOLO object detection model, UBL Retail identifies Unilever products on store shelves, monitors their placement, and analyzes sales patterns. This data-driven approach helps Unilever redirect sales efforts, enhance inventory management, and ensure optimal product availability, ultimately boosting retail efficiency and customer satisfaction.

UBL Retail leverages a computer vision pipeline powered by YOLO for real-time object detection of Unilever products on store shelves. The system uses deep learning models to process images, combined with machine learning techniques for sales trend analysis and distribution optimization.

Built with a modular architecture, UBL Retail integrates AI-driven analytics to provide insights into product placement and inventory levels. The pipeline processes store images, extracts product data, and generates reports to guide sales redirection and distribution strategies, ensuring scalability and accuracy for retail operations.

This was one of the most interesting projects I've worked on in a while. You can explore the UBL Retail project on GitHub, and see how computer vision can transform product analysis and sales strategies.