# A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels

arXiv: https://arxiv.org/abs/1901.00711 · HTML: https://mlsanity.com/paper/1901.00711/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2019.

## Built on by (most cited first, 12 of 12)

- [Unity Perception: Generate Synthetic Data for Computer Vision](https://mlsanity.com/paper/2107.04259/) (2021)
- [Fruit Ripeness Classification: a Survey](https://mlsanity.com/paper/2212.14441/) (2022)
- [Vision as Unified Multimodal Generation](https://mlsanity.com/paper/2607.06560/) (2026)
- [Variational Classification](https://mlsanity.com/paper/2305.10406/) (2023)
- [A Benchmark Grocery Dataset of Realworld Point Clouds From Single View](https://mlsanity.com/paper/2402.07819/) (2024)
- [SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights](https://mlsanity.com/paper/2507.04412/) (2025)
- [TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data](https://mlsanity.com/paper/2111.04798/) (2021)
- [Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities](https://mlsanity.com/paper/2305.01899/) (2023)
- [A Visual RAG Pipeline for Few-Shot Fine-Grained Product Classification](https://mlsanity.com/paper/2504.11838/) (2025)
- [Banana Sub-Family Classification and Quality Prediction using Computer Vision](https://mlsanity.com/paper/2204.02581/) (2022)
- [Enhancing Classification with Hierarchical Scalable Query on Fusion Transformer](https://mlsanity.com/paper/2302.14487/) (2023)
- [FIKA-Bench: From Fine-grained Recognition to Fine-Grained Knowledge Acquisition](https://mlsanity.com/paper/2605.13193/) (2026)

## Abstract (arXiv)

Image classification models built into visual support systems and other assistive devices need to provide accurate predictions about their environment. We focus on an application of assistive technology for people with visual impairments, for daily activities such as shopping or cooking. In this paper, we provide a new benchmark dataset for a challenging task in this application - classification of fruits, vegetables, and refrigerated products, e.g. milk packages and juice cartons, in grocery stores. To enable the learning process to utilize multiple sources of structured information, this dataset not only contains a large volume of natural images but also includes the corresponding information of the product from an online shopping website. Such information encompasses the hierarchical structure of the object classes, as well as an iconic image of each type of object. This dataset can be used to train and evaluate image classification models for helping visually impaired people in natural environments. Additionally, we provide benchmark results evaluated on pretrained convolutional neural networks often used for image understanding purposes, and also a multi-view variational autoencoder, which is capable of utilizing the rich product information in the dataset.