RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification
This dataset addresses the need for large-scale, real-world retail product recognition, benefiting computer vision research and the retail industry, though it is incremental as it builds on existing dataset efforts.
The authors introduced RP2K, a large-scale dataset with over 500,000 images of 2000 retail products, captured in physical stores to advance fine-grained image classification for applications like shelf auditing and product retrieval.
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.