Pasta Product Occlusion Image Dataset

The retail e-commerce industry is experiencing significant growth, yet it faces challenges in accurately recognizing products that are partially occluded, which can lead to poor customer experiences and lost sales. Existing solutions often rely on clear product images, failing to account for the real-world scenarios where occlusions occur. This dataset aims to provide a robust collection of images featuring popular pasta products from brands like Barilla, Uncle Ben’s, and De Cecco, showcasing various occlusion scenarios to enhance machine learning models' performance in product detection tasks. Data collection involved capturing high-resolution images of pasta products under various conditions, including different lighting and angles, to ensure diversity. Quality control measures included multi-round annotations, consistency checks among annotators, and expert reviews to ensure high accuracy in labeling. The dataset is stored in JPG format, organized in folders categorized by product type and occlusion type to facilitate easy access and processing.