Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.08648 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913938351849472 |
|---|---|
| author | Sun, Haoran Bian, Haoyu Zeng, Shaoning Rao, Yunbo Xu, Xu Mei, Lin Gou, Jianping |
| author_facet | Sun, Haoran Bian, Haoyu Zeng, Shaoning Rao, Yunbo Xu, Xu Mei, Lin Gou, Jianping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08648 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images Sun, Haoran Bian, Haoyu Zeng, Shaoning Rao, Yunbo Xu, Xu Mei, Lin Gou, Jianping Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.08648 |