RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification

TL;DR

RP2K is a comprehensive large-scale retail product dataset with over 500,000 images across 2000 products, designed to enhance fine-grained image classification for retail applications.

Contribution

The paper introduces RP2K, the largest retail product dataset with detailed annotations, captured in real store environments, to advance retail object recognition research.

Findings

Largest scale dataset for retail product classification

Images captured in real store conditions with natural lighting

Rich annotations including size, shape, and scent information

Abstract

We introduce RP2K, a new large-scale retail product dataset for fine-grained image classification. Unlike previous datasets focusing on relatively few products, we collect more than 500,000 images of retail products on shelves belonging to 2000 different products. Our dataset aims to advance the research in retail object recognition, which has massive applications such as automatic shelf auditing and image-based product information retrieval. Our dataset enjoys following properties: (1) It is by far the largest scale dataset in terms of product categories. (2) All images are captured manually in physical retail stores with natural lightings, matching the scenario of real applications. (3) We provide rich annotations to each object, including the sizes, shapes and flavors/scents. We believe our dataset could benefit both computer vision research and retail industry. Our dataset is publicly available at https://www.pinlandata.com/rp2k_dataset.