Creation and Evaluation of a Food Product Image Dataset for Product Property Extraction

TL;DR

This paper presents the creation and evaluation of a high-quality, annotated food product image dataset designed to support machine learning models for product recognition and classification in retail, based on GS1 standards.

Contribution

It introduces a detailed process for creating a standardized dataset with 1,034 images and comprehensive annotations, facilitating AI development in retail applications.

Findings

Dataset contains 1,034 images of food products.

Annotations include 5 class labels and 30 object detection labels.

Supports machine learning models for product recognition.

Abstract

The enormous progress in the field of artificial intelligence (AI) enables retail companies to automate their processes and thus to save costs. Thereby, many AI-based automation approaches are based on machine learning and computer vision. The realization of such approaches requires high-quality training data. In this paper, we describe the creation process of an annotated dataset that contains 1,034 images of single food products, taken under studio conditions, annotated with 5 class labels and 30 object detection labels, which can be used for product recognition and classification tasks. We based all images and labels on standards presented by GS1, a global non-profit organisation. The objective of our work is to support the development of machine learning models in the retail domain and to provide a reference process for creating the necessary training data.