# Retail-786k: a Large-Scale Dataset for Visual Entity Matching

arXiv: https://arxiv.org/abs/2309.17164 · HTML: https://mlsanity.com/paper/2309.17164/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 46 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [Fine-Grained Image Analysis with Deep Learning: A Survey](https://mlsanity.com/paper/2111.06119/) (2021)
- [Multi-Modal Knowledge Graph Construction and Application: A Survey](https://mlsanity.com/paper/2202.05786/) (2022)
- [Deep Learning for Instance Retrieval: A Survey](https://mlsanity.com/paper/2101.11282/) (2021)
- [Precise Detection in Densely Packed Scenes](https://mlsanity.com/paper/1904.00853/) (2019)
- [Visual Pivoting for (Unsupervised) Entity Alignment](https://mlsanity.com/paper/2009.13603/) (2020)
- [Products-10K: A Large-scale Product Recognition Dataset](https://mlsanity.com/paper/2008.10545/) (2020)
- [Neural Networks for Entity Matching: A Survey](https://mlsanity.com/paper/2010.11075/) (2020)
- [Weakly Supervised Learning with Side Information for Noisy Labeled Images](https://mlsanity.com/paper/2008.11586/) (2020)
- [MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid](https://mlsanity.com/paper/2212.14454/) (2022)
- [RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification](https://mlsanity.com/paper/2006.12634/) (2020)
- [LargeEA: Aligning Entities for Large-scale Knowledge Graphs](https://mlsanity.com/paper/2108.05211/) (2021)
- [MVTec D2S: Densely Segmented Supermarket Dataset](https://mlsanity.com/paper/1804.08292/) (2018)
- [Robust and Decomposable Average Precision for Image Retrieval](https://mlsanity.com/paper/2110.01445/) (2021)
- [Rethinking Object Detection in Retail Stores](https://mlsanity.com/paper/2003.08230/) (2020)
- [The Freiburg Groceries Dataset](https://mlsanity.com/paper/1611.05799/) (2016)
- [Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorization](https://mlsanity.com/paper/1908.08984/) (2019)
- [A Retail Product Categorisation Dataset](https://mlsanity.com/paper/2103.13864/) (2021)
- [ARC: A Vision-based Automatic Retail Checkout System](https://mlsanity.com/paper/2104.02832/) (2021)

## Built on by (most cited first, 2 of 2)

- [Can Visual Language Models Replace OCR-Based Visual Question Answering Pipelines in Production? A Case Study in Retail](https://mlsanity.com/paper/2408.15626/) (2024)
- [A Visual RAG Pipeline for Few-Shot Fine-Grained Product Classification](https://mlsanity.com/paper/2504.11838/) (2025)

## Abstract (arXiv)

Entity Matching (EM) defines the task of learning to group objects by transferring semantic concepts from example groups (=entities) to unseen data. Despite the general availability of image data in the context of many EM-problems, most currently available EM-algorithms solely rely on (textual) meta data. In this paper, we introduce the first publicly available large-scale dataset for "visual entity matching", based on a production level use case in the retail domain. Using scanned advertisement leaflets, collected over several years from different European retailers, we provide a total of ~786k manually annotated, high resolution product images containing ~18k different individual retail products which are grouped into ~3k entities. The annotation of these product entities is based on a price comparison task, where each entity forms an equivalence class of comparable products. Following on a first baseline evaluation, we show that the proposed "visual entity matching" constitutes a novel learning problem which can not sufficiently be solved using standard image based classification and retrieval algorithms. Instead, novel approaches which allow to transfer example based visual equivalent classes to new data are needed to address the proposed problem. The aim of this paper is to provide a benchmark for such algorithms. Information about the dataset, evaluation code and download instructions are provided under https://www.retail-786k.org/.