Datasets:
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
tags:
- e-commerce
size_categories:
- 100K<n<1M
Introduction
EcomMMMU is a large-scale multimodal multitask understanding dataset for e-commerce applications, containing 406,190 samples and 8,989,510 product images across 34 product categories. It is designed to systematically evaluate how multimodal large language models (MLLMs) utilize visual information in real-world shopping scenarios.
Unlike prior datasets that treat all images equally, EcomMMMU explicitly investigates when and how multiple product images contribute to understanding. It includes a specialized vision-salient subset (VSS), designed to test scenarios where textual information alone is insufficient and visuals are crucial.
Dataset Sources
- Repository: GitHub
Quick Start
Run the following command to get the data:
from datasets import load_dataset
dataset = load_dataset("NingLab/EcomMMMU")
License
Please check the license of each subset in our curated dataset ECInstruct.
| Dataset | License Type |
|---|---|
| Amazon Review | Non listed |
| AmazonQA | Non listed |
| Shopping Queries Dataset | Apache License 2.0 |
Citation
@article{ling2025ecommmmu,
title={EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-Commerce Models},
author={Ling, Xinyi and Du, Hanwen and Zhu, Zhihui and Ning, Xia},
journal={arXiv preprint arXiv:2508.15721},
year={2025}
}