DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
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.
