Juice Cup and Water Cup Recognition Dataset

Currently, the retail e-commerce sector faces challenges in accurately recognizing product types, particularly distinguishing between portable juice cups and regular water cups. Existing solutions often lack precision and struggle with variations in design and branding. This dataset aims to resolve the technical issues of object recognition and classification in diverse retail environments. The data was collected using high-resolution cameras in controlled lighting conditions, ensuring a consistent quality. Quality control measures included multi-round annotations, consistency checks among annotators, and expert reviews. The data is stored in JPG format, organized into labeled folders based on the product type. The dataset's core advantages include high annotation accuracy, achieved through rigorous quality checks that ensure over 95% consistency across annotations. Innovative labeling techniques and data augmentation methods have been employed to enhance the dataset's robustness. This dataset is expected to improve recognition performance by at least 20% compared to existing datasets, addressing real-world challenges in product identification.