Training AI on Meiji-Era Textiles: Lessons from Kimono Classification

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When we started working with Kimono Clarity to classify their collection of over 2,000 vintage Japanese textiles, we knew it wouldn't be a typical inventory project. These weren't generic products with SKUs and UPCs—they were pieces of cultural history, each with unique characteristics that mattered to buyers.

The Challenge

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What Made This Different

No standardized product categories
Highly specialized terminology
Buyers who knew more than most AI models
Items spanning 150+ years of history

Building the Classification System

Developing the Schema

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The first challenge was defining what to capture. Working with the Kimono Clarity team, we identified the attributes that actually mattered to buyers:

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Training the Model

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Handling Edge Cases

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Results

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What Worked

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What We'd Do Differently

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Lessons for Other Specialty Retailers

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The Kimono Clarity project taught us several things that apply to any specialty retail inventory:

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This article is part of our Field Notes series, sharing observations from the intersection of AI and artisan retail.