In this tutorial, we demonstrate how to fine-tune YOLO (You Only Look Once) for accurate product recognition in automated self-checkout systems. From dataset preparation and annotation to training and real-time inference, you’ll learn each step to build a smart retail vision system capable of identifying multiple products simultaneously — no barcodes needed.

We explore the full pipeline using YOLOv8, covering:
– How to collect and label a product dataset
– Modifying YOLO for specific retail products
– Training and evaluation best practices
– Real-time object detection for retail checkout
– Estimating total cost with recognized product classes

Whether you’re a computer vision engineer, AI researcher, or retail automation developer, this video will guide you through the practical implementation of vision-based checkout systems using state-of-the-art object detection.

🚀 By the end of this video, you’ll be able to:
– Train a YOLO model for custom product datasets
– Deploy it for real-time product detection
– Automate the pricing logic for checkout

This project is ideal for smart retail, cashierless stores, and AI-driven billing counters. If you’re looking to modernize checkout systems with deep learning and computer vision, this is the perfect place to start.

Resources:
Tutorial Notebook: https://github.com/Labellerr/Hands-On-Learning-in-Computer-Vision/blob/main/fine-tune%20YOLO%20for%20various%20use%20cases/Fine-Tune_YOLO_for_Product_Recognition_for_Price_Verification.ipynb

Labellerr’s Official Github: https://github.com/Labellerr/Hands-On-Learning-in-Computer-Vision

Interested in learning more about our services?
Website: https://www.labellerr.com
Book a Demo: https://www.labellerr.com/book-a-demo

Find us on Social Media Platforms:
LinkedIn: https://www.linkedin.com/company/labellerr
Twitter: https://x.com/Labellerr1

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