{"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/a-hierarchical-grocery-store-image-dataset","title":"A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels","arxiv_id":"1901.00711","date":"2019-01-03","proceeding":null,"authors":["Marcus Klasson","Cheng Zhang","Hedvig Kjellström"],"abstract":"Image classification models built into visual support systems and other\nassistive devices need to provide accurate predictions about their environment.\nWe focus on an application of assistive technology for people with visual\nimpairments, for daily activities such as shopping or cooking. In this paper,\nwe provide a new benchmark dataset for a challenging task in this application -\nclassification of fruits, vegetables, and refrigerated products, e.g. milk\npackages and juice cartons, in grocery stores. To enable the learning process\nto utilize multiple sources of structured information, this dataset not only\ncontains a large volume of natural images but also includes the corresponding\ninformation of the product from an online shopping website. Such information\nencompasses the hierarchical structure of the object classes, as well as an\niconic image of each type of object. This dataset can be used to train and\nevaluate image classification models for helping visually impaired people in\nnatural environments. Additionally, we provide benchmark results evaluated on\npretrained convolutional neural networks often used for image understanding\npurposes, and also a multi-view variational autoencoder, which is capable of\nutilizing the rich product information in the dataset.","url_abs":"http://arxiv.org/abs/1901.00711v1","url_pdf":"http://arxiv.org/pdf/1901.00711v1.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":[{"paper_slug":"a-hierarchical-grocery-store-image-dataset","repo_url":"https://github.com/marcusklasson/GroceryStoreDataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-hierarchical-grocery-store-image-dataset","repo_url":"https://github.com/hemantnyadav/GrocerryImageClassification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-hierarchical-grocery-store-image-dataset","repo_url":"https://github.com/tommarvoloriddle/Grocery-Store-Dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[{"slug":"grocery-store","name":"Grocery Store","full_name":null}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}