{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/rpc-a-large-scale-retail-product-checkout","title":"RPC: A Large-Scale Retail Product Checkout Dataset","arxiv_id":"1901.07249","date":"2019-01-22","proceeding":null,"authors":["Xiu-Shen Wei","Quan Cui","Lei Yang","Peng Wang","Lingqiao Liu"],"abstract":"Over recent years, emerging interest has occurred in integrating computer\nvision technology into the retail industry. Automatic checkout (ACO) is one of\nthe critical problems in this area which aims to automatically generate the\nshopping list from the images of the products to purchase. The main challenge\nof this problem comes from the large scale and the fine-grained nature of the\nproduct categories as well as the difficulty for collecting training images\nthat reflect the realistic checkout scenarios due to continuous update of the\nproducts. Despite its significant practical and research value, this problem is\nnot extensively studied in the computer vision community, largely due to the\nlack of a high-quality dataset. To fill this gap, in this work we propose a new\ndataset to facilitate relevant research. Our dataset enjoys the following\ncharacteristics: (1) It is by far the largest dataset in terms of both product\nimage quantity and product categories. (2) It includes single-product images\ntaken in a controlled environment and multi-product images taken by the\ncheckout system. (3) It provides different levels of annotations for the\ncheck-out images. Comparing with the existing datasets, ours is closer to the\nrealistic setting and can derive a variety of research problems. Besides the\ndataset, we also benchmark the performance on this dataset with various\napproaches. The dataset and related resources can be found at\n\\url{https://rpc-dataset.github.io/}.","url_abs":"http://arxiv.org/abs/1901.07249v1","url_pdf":"http://arxiv.org/pdf/1901.07249v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"rpc","name":"RPC","full_name":"Retail Product Checkout"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1901.07249","atlas_url":"https://app.syntology.ai/?focus=1901.07249","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}