Common knowledge indicates that the process of constructing image datasets usually depends on the time-intensive and inefficient method of manual collection and annotation. Large models offer a solution via data generation. Nonetheless, real-world data are obviously more valuable comparing to artificially intelligence generated data, particularly in constructing image datasets. For this reason, we propose a novel method for auto-constructing datasets from real-world images by a multiagent collaborative system, named as DatasetAgent. By coordinating four different agents equipped with Multi-modal Large Language Models (MLLMs), as well as a tool package for image optimization, DatasetAgent is able to construct high-quality image datasets according to user-specified requirements. In particular, two types of experiments are conducted, including expanding existing datasets and creating new ones from scratch, on a variety of open-source datasets. In both cases, multiple image datasets constructed by DatasetAgent are used to train various vision models for image classification, object detection, and image segmentation.
@article{arxiv.2507.08648,
title = {DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images},
author = {Haoran Sun and Haoyu Bian and Shaoning Zeng and Yunbo Rao and Xu Xu and Lin Mei and Jianping Gou},
journal= {arXiv preprint arXiv:2507.08648},
year = {2025}
}