AmalthAI：面向文化遗产的开源计算机视觉平台

中文摘要 AI 辅助

计算机视觉（CV）和机器学习（ML）为文化遗产（CH）文物分析提供了新工具，但文化遗产领域专家大多无法使用CV/ML流程，他们缺乏配置、训练或评估模型的背景知识。我们提出AmalthAI，这是一款开源CV平台，旨在弥合这一差距，让非ML背景的CH专家能够独立产出并验证具有考古意义的发现。该平台的界面涵盖数据集管理、分类、分割和目标检测的训练与推理功能，其中Kubeflow和Katib负责可扩展训练与超参数搜索。Grad-CAM可定位预测背后的图像区域，视觉语言模型（VLM）还会为该区域添加文本描述供专家审核。由于考古数据常为国有或受权利限制，无法离开机构保管，AmalthAI的自托管部署确保敏感数据保留在机构内部。我们基于自定义黏土织物印痕数据集构建的考古用例对该平台进行测试，CH专家在此用例中训练并验证了用于假设检验的分割模型与分类模型。我们在该https链接提供了实现代码。

英文摘要

Computer vision (CV) and machine learning (ML) offer new tools for cultural heritage (CH) artifact analysis, but the CV/ML pipeline remains largely inaccessible to CH domain experts, who lack the background to configure, train, or assess models. We present AmalthAI, an open-source CV platform that bridges this gap, enabling non-ML CH experts to independently produce and validate archaeologically meaningful findings. The interface covers dataset management, training, and inference for classification, segmentation, and object detection, with Kubeflow and Katib handling scalable training and hyperparameter search. Grad-CAM localizes the image region behind a prediction, and a vision-language model (VLM) adds a text description of it for expert review. Since archaeological data is often state-owned or rights-encumbered and cannot leave institutional custody, AmalthAI's self-hostable deployment ensures sensitive data is kept within premises. We test the platform on an archaeological use case built on a custom dataset of clay textile imprints, where CH experts trained and validated segmentation, and classification models for hypothesis testing. We provide the implementation code at https://github.com/TEXTaiLES/AmalthAI.

发表机构

• Democritus University of Thrace（德谟克利特色雷斯大学）
• Athena Research Center（雅典娜研究中心）
• National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚大学）
• University of Warsaw（华沙大学）

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