Synthetic and Augmented Training Data Pipeline for a Fashion Resale Platform's Listing AI
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Problem
Listing AI starved of data
A fashion resale platform processing thousands of secondhand garments daily across 11 hubs depended on an automated listing model to identify brand, category and condition. Training data could not keep pace: rare brands and damage types were badly under-represented, manual labelling costs kept climbing, and misclassified listings were driving returns and constant repricing work.
Solution
Synthetic training data pipeline
We built a synthetic and augmented training-data pipeline for the listing AI. Standardised studio capture rigs feed an augmentation service generating lighting, background and occlusion variants, while a 3D rendering workflow composites synthetic examples of rare garment categories and defects. Versioned datasets flow into scheduled retraining, and lister corrections loop back automatically as fresh labelled samples.
Measurable Impact
What changed after launch
Labelled and synthetic training corpus grew 6x in 4 months at roughly one-third the previous per-image cost
Attribute classification accuracy on long-tail brands and categories improved from 71% to 88%
Manual listing corrections per 1,000 intake items fell 43% across all 11 processing hubs
Average time to publish an intake garment cut from 9 minutes to under 4
Tech & Tools Used
What powered the build
Ready to Build your Fashion Resale & E-Commerce Business with Synthetic & Augmented Data
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