AmalthAI: An Open-Source Computer Vision Platform for Cultural Heritage

2026-08-13

AI Summary

This work addresses the limited machine learning expertise among cultural heritage specialists, which hinders their ability to independently conduct vision-based archaeological analysis. To bridge this gap, we present the first end-to-end, self-hosted computer vision platform tailored to this domain, enabling non-technical users to manage data, train models, and perform inference for classification, segmentation, and object detection tasks—all while keeping sensitive data within institutional boundaries. The system integrates Grad-CAM for prediction visualization and leverages a vision-language model to generate explanatory textual descriptions, enhancing interpretability. Built on Kubeflow and Katib, the backend supports scalable training and automated hyperparameter optimization. Experiments on a newly curated dataset of pottery textile impressions demonstrate the platform’s effectiveness, empowering domain experts to autonomously test hypotheses and produce archaeologically meaningful insights.

Abstract

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.

Problem

• computer vision
• cultural heritage
• machine learning
• domain experts
• accessible AI

Innovation

• open-source platform
• vision-language model
• Grad-CAM interpretability
• self-hostable deployment
• cultural heritage analysis