The task at hand is to automate the labeling of images in a dataset for building a deep learning model that quantifies the calorie count of food on a plate. The goal is to label every frame with a correctly placed bounding box around each item of food, given only an image level classification and no bounding boxes. Algorithmic labelling is used to convert existing information into a solution in the form of a program, offering scalability, reusability, and insights into the data that can be applied to improve final trained models. By examining the dataset's organisational structure and common properties, developing a prototype algorithm, testing it on sample data, and refining it based on results, an efficient labeling process is achieved with minimal human intervention.