StyleCast: an AI outfit planner that runs on your phone

Описание и применение

StyleCast автоматически определяет одежду, учитывает погоду и историю носки, предлагая подходящие комплекты и бюджетные покупки, работая полностью на устройстве.

Задача

Users manually guess outfits based on weather, leading to mismatches and wasted time.

Результат

StyleCast improved outfit accuracy from 55–72% to 77% for clothing type and 90–93% for category, reduced model download to ~300 MB, and enabled fully local, privacy‑preserving outfit planning and budget shopping.

Рабочий процесс

• Snap wardrobe once; browser runs CLIP ViT‑B/32 to tag items.
• Read local weather via Open‑Meteo and user’s planned occasion.
• Apply JavaScript rules to pick an outfit, avoiding recent repeats and items in the wash.
• Detect gaps in wardrobe for current weather.
• If user asks for budget shopping, Gemma 3 runs locally to plan searches, which a Render server executes via SerpApi.
• Display outfit or shopping suggestions to the user.

Доказательства

Snap your wardrobe once. An open vision model running in the browser tags each item: type, colour, warmth, waterproof, formality.

Источник

CLIP doesn’t describe photos the way a chat model does. It compares a photo with a list of descriptions and picks the closest.

Источник

Ask "I have €50, what should I buy?" and this happens: Gemma reads your wardrobe and the gaps the rules found, and plans up to three searches.

Источник