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

2020-06-22 21:39:56
Jingtian Peng, Chang Xiao, Xun Wei, Yifan Li
arXiv_CV
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2006.12634

PDF

https://arxiv.org/pdf/2006.12634.pdf

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