Chocolate Box Quality Inspection with Computer Vision

James Gallagher's guide provides a comprehensive walkthrough on using computer vision to ensure quality control in chocolate box assembly by building an inspection system that verifies the correct number and arrangement of chocolates. The process begins with creating a project on Roboflow, where users can upload and annotate images of chocolate boxes to train a model capable of identifying different chocolate types. The guide details each step, from labeling images to generating dataset versions and training the model, to deploying the system using Roboflow Inference, an open-source server for managing computer vision models. The model's results help ensure compliance with chocolate box specifications by checking that each chocolate is in the correct position and that the box contains the right quantity and variety. The system can be integrated into a broader chocolate quality assurance framework to identify and address defects in individual chocolates before packaging.