Evaluation of GPT-4o & GPT-4o-mini's Vision Capabilities for Salt Evaporite Identification

Identifying salts from images of their'stains' has diverse practical applications. While specialized AI models are being developed, this paper explores the potential of OpenAI's state-of-the-art vision models (GPT-4o and GPT-4o-mini) as an immediate solution. Testing with 12 different types of salts, the GPT-4o model achieved 57% accuracy and a 0.52 F1 score, significantly outperforming both random chance (8%) and GPT-4o mini (11% accuracy). However, GPT-4o mini also had significantly biased responses, diminishing the representativeness of its accuracy. Results suggest that current vision models could serve as an interim solution for salt identification from their stain images.