Shampoo Product Occlusion Image Dataset

The retail e-commerce industry is facing challenges related to accurate product recognition in images, especially when occlusions occur due to packaging similarities among brands like Head & Shoulders, Pantene, and Dove. Existing solutions often struggle with misidentification and reduced accuracy, leading to poor user experience and lower sales conversions. This dataset aims to address the need for robust algorithms capable of detecting and classifying shampoo products even when partially obstructed. The data was collected using high-resolution cameras under controlled lighting conditions to ensure clarity and consistency. Quality control measures included multiple rounds of annotation, consistency checks among annotators, and expert reviews to maintain high standards. The data is organized in JPG format, with images categorized based on occlusion type and brand.