用多智能体自动构建高质量真实图像数据集
DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images
- 四智能体协同，结合多模态大模型与图像优化工具
- 可扩展现有数据集或从零创建新数据集，支持多种视觉任务训练
- 适合需要高效构建真实世界图像数据集的研究者
构建图像数据集通常依赖耗时低效的人工采集与标注。大模型虽可生成数据，但真实世界数据仍更具价值。为此，本文提出DatasetAgent——一种基于多智能体协作系统的自动化真实图像数据集构建方法。该系统由四个配备多模态大语言模型（MLLMs）的智能体及一套图像优化工具包组成，可根据用户指定需求生成高质量图像数据集。通过两类实验验证：在多个开源数据集上进行数据集扩展与全新构建。所生成的数据集用于训练图像分类、目标检测和图像分割等多种视觉模型，证明其有效性。
原文摘要 · Abstract (English)
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.
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