Hi Everyone,
I wanted to share a technical prototype and architectural approach we are experimenting with using the Gemini 1.5 Pro API’s long-context multimodal capabilities for high-value classic car visual verification.
When processing high-resolution video streams and image galleries for rare automotive showcases (such as cars featured in upcoming live events like Kool Deadwood Nites in August 2026), manual inspection of specific custom features can be time-consuming. We built a workflow to automatically analyze visual features of iconic builds like the 1967 Shelby GT500 Eleanor restomod.
For high-net-worth collectors evaluating an eleanor mustang for sale 2026, our pipeline extracts key visual and mechanical data points directly from unstructured video walkthroughs and photo sets:
1. Multimodal Extraction Workflow:
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Frame Extraction & Context Injection: We sample frames at 1 FPS from 4K walkaround videos and pass them alongside system prompts to Gemini 1.5 Pro.
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Feature Identification: The model evaluates specific custom restomod details—including Pepper Gray Metallic paint consistency, black Le Mans stripe alignment, side-exit exhaust cutouts, and interior leather condition.
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Structured JSON Output: Using
response_mime_type="application/json"with Pydantic schemas, Gemini returns a structured report flagging any visual defects or non-period-correct modifications.