<ul data-eligibleForWebStory="true"><li>A computer vision project aimed at identifying physical damage on laptops faced challenges like hallucinations and unreliable outputs.</li><li>The team experimented with different approaches, including using an agentic framework to break down the image interpretation task into smaller agents.</li><li>They found that image quality significantly impacted the model's output and tried training it with a mix of high and low-resolution images to improve consistency.</li><li>Combining different AI techniques such as image captioning with language models and utilizing an agentic framework led to a more precise and manageable system for structured damage detection.</li></ul>

From hallucinations to hardware: Lessons from a real-world computer vision project gone sideways

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