Evaluation of GPT-4o and GPT-4o-mini's Vision Capabilities for Compositional Analysis from Dried Solution Drops

Fuente: arXiv
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Autori principali: Dangi, Deven B., Dangi, Beni B., Steinbock, Oliver
Natura: Preprint
Pubblicazione: 2024
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author Dangi, Deven B.
Dangi, Beni B.
Steinbock, Oliver
author_facet Dangi, Deven B.
Dangi, Beni B.
Steinbock, Oliver
contents When microliter drops of salt solutions dry on non-porous surfaces, they form erratic yet characteristic deposit patterns influenced by complex crystallization dynamics and fluid motion. Using OpenAI's image-enabled language models, we analyzed deposits from 12 salts with 200 images per salt and per model. GPT-4o classified 57% of the salts accurately, significantly outperforming random chance and GPT-4o mini. This study underscores the promise of general-use AI tools for reliably identifying salts from their drying patterns.
format Preprint
id arxiv_https___arxiv_org_abs_2412_10587
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Evaluation of GPT-4o and GPT-4o-mini's Vision Capabilities for Compositional Analysis from Dried Solution Drops
Dangi, Deven B.
Dangi, Beni B.
Steinbock, Oliver
Computer Vision and Pattern Recognition
Artificial Intelligence
Computation and Language
When microliter drops of salt solutions dry on non-porous surfaces, they form erratic yet characteristic deposit patterns influenced by complex crystallization dynamics and fluid motion. Using OpenAI's image-enabled language models, we analyzed deposits from 12 salts with 200 images per salt and per model. GPT-4o classified 57% of the salts accurately, significantly outperforming random chance and GPT-4o mini. This study underscores the promise of general-use AI tools for reliably identifying salts from their drying patterns.
title Evaluation of GPT-4o and GPT-4o-mini's Vision Capabilities for Compositional Analysis from Dried Solution Drops
topic Computer Vision and Pattern Recognition
Artificial Intelligence
Computation and Language
url https://arxiv.org/abs/2412.10587