How to Create a Retail Planogram using Computer Vision

Planograms are essential tools for retailers to ensure precise store layouts that comply with business requirements and vendor agreements, while also gauging the impact of pricing and product offers. By harnessing computer vision technologies, retailers can automate the creation of dynamic and scalable planograms, ensuring proper product placement on shelves. This guide demonstrates the implementation of a retail planogram using drink vending machines as examples, detailing the process of building a computer vision model and a workflow on Roboflow. The guide covers steps such as training a model to detect SKUs, creating workflows for object detection, and writing scripts to upload SKU positions to Google Sheets. The use of Roboflow Workflows allows for the definition of multi-stage computer vision applications, leveraging external APIs like GPT-4o to detect product placement row by row, assess pricing, and identify offers. This process involves setting up a computer vision model, annotating datasets, using dynamic cropping and OpenAI blocks for accurate detection, and optionally uploading data to Google Sheets for further analysis. By following these steps, retailers can create dynamic planograms that not only maximize sales but also enhance customer experience.