Yogurt Product Occlusion Image Dataset

The retail e-commerce sector is facing challenges in accurately recognizing products that are partially obscured in images, particularly with popular yogurt brands like Yoplait, Danone, Chobani, and Alpro. Existing solutions struggle with high occlusion scenarios, which often leads to misclassification and poor customer experience. This dataset aims to address the specific technical issue of occluded product recognition by providing a diverse set of images featuring common yogurt products in various occluded states. Data collection involved capturing images in retail environments with different lighting conditions and angles. Quality control was implemented via multi-round annotations, consistency checks among annotators, and expert reviews to ensure high accuracy. The dataset is organized in JPG format with each image linked to its respective metadata. This dataset boasts high-quality features, including a labeling accuracy of over 95% and comprehensive coverage of occlusion types, significantly improving the performance of object detection models in challenging scenarios. Our innovative annotation methods and data augmentation techniques have resulted in a dataset that enhances model robustness. In practical terms, models trained on this dataset have shown a 30% increase in recognition accuracy for occluded products compared to existing benchmarks, making it a valuable resource for the retail e-commerce industry.