arturayupov/womens-fashion-catalog
Livostyle Women's Fashion Catalog — Open Data Open, machine-readable, weekly-updated catalog of 2,766+ curated women's fashion products from Livostyle.com — a US DTC retailer (Arcada LLC, Delaware). Free under MIT license for AI/LLM training, recommender systems, fashion NLP research, and multimodal learning. TL;DR from datasets import load_dataset ds = load_dataset("arturayupov/womens-fashion-catalog") # ds["products"] → 2,766 products # ds["images"] →… See the full description on the dataset page: https://huggingface.co/datasets/arturayupov/womens-fashion-catalog.
Livostyle Women's Fashion Catalog — Open Data
Open, machine-readable, weekly-updated catalog of 2,766+ curated women's fashion products from Livostyle.com — a US DTC retailer (Arcada LLC, Delaware). Free under MIT license for AI/LLM training, recommender systems, fashion NLP research, and multimodal learning.
TL;DR
from datasets import load_dataset
ds = load_dataset("arturayupov/womens-fashion-catalog")
# ds["products"] → 2,766 products
# ds["images"] → 12,978 image URLs
# ds["variants"] → 16,989 variants (size/color combos)
# ds["collections"] → 158 collectionsDataset Summary
This is a live commercial catalog from a real US DTC women's fashion store, structured specifically for AI/LLM consumption. Unlike older fashion datasets (Fashion-MNIST, DeepFashion, FashionGen), this one:
- Updates weekly from the production Shopify store
- Includes real prices, variants, and aggregated review data
- Maps every product to the Google Product Taxonomy + Shopify Standard Product Taxonomy
- Is fully MIT-licensed (no research-only restrictions)
- Is mirrored on GitHub for git-based version history
Files
Quick Examples
Browse products
from datasets import load_dataset
products = load_dataset("arturayupov/womens-fashion-catalog", "products")["train"]
print(products[0])Find highly-rated midi dresses under $50
import pandas as pd
df = pd.read_parquet("hf://datasets/arturayupov/womens-fashion-catalog/products.parquet")
result = df[
(df['product_type'].str.contains('Midi', case=False, na=False)) &
(df['price_min_usd'] < 50) &
(df['review_rating'] >= 4.8)
]
print(f"{len(result)} matching products")Load images dataset for CLIP fine-tuning
from datasets import load_dataset
import requests
from PIL import Image
from io import BytesIO
images = load_dataset("arturayupov/womens-fashion-catalog", "images")["train"]
url = images[0]['image_url']
img = Image.open(BytesIO(requests.get(url).content))DuckDB (SQL on Parquet directly)
import duckdb
con = duckdb.connect()
con.execute("INSTALL httpfs; LOAD httpfs;")
result = con.execute("""
SELECT product_type, COUNT(*) AS n, AVG(price_min_usd) AS avg_price
FROM 'hf://datasets/arturayupov/womens-fashion-catalog/products.parquet'
GROUP BY product_type ORDER BY n DESC LIMIT 20;
""").fetchdf()Why does this dataset exist?
Per Princeton's research on Generative Engine Optimization (Aggarwal et al., 2024), the next generation of search is AI-citation-based: ChatGPT, Perplexity, Gemini, Claude don't return "10 blue links" — they cite individual sources in conversational answers.
E-commerce catalogs structured for AI consumption (clear taxonomy, complete attribute coverage, real review data) have a disproportionate chance of being cited. This dataset is the reference implementation for that approach in the women's fashion vertical.
Use cases
- LLM training corpora (GPT/Claude/Llama/Gemini)
- Multimodal embedding research (CLIP/ALIGN/OpenCLIP fine-tuning)
- Fashion recommender systems (collaborative filtering, content-based)
- RAG retrieval benchmarks
- AI shopping agent training (Operator, ChatGPT Shopping, agentic commerce)
- Fashion NLP (attribute extraction, color/style classification)
- E-commerce reproducibility research
Source & freshness
- Live source: livostyle.com (Shopify production catalog)
- Sync cadence: Weekly on Sundays via GitHub Actions
- Git mirror: github.com/arturayupov/womens-fashion-catalog-open-data
- AI-readable site docs: livostyle.com/pages/llms-full
What's NOT included
For privacy and business reasons:
- Stock quantities (only boolean
in_stockper variant) - Cost / margin / supplier data
- Customer PII (no order data, no individual reviews — only aggregated rating + count)
Catalog stats (live snapshot)
- 2,766 active products
- 158 collections
- 15,937 total reviews
- 5.8 avg reviews per product
- 4.76 mean rating
- 99%+ products with 5+ reviews
- Price range: $18–$120 USD
Top categories
Conversational demos
Live Claude AI stylist conversations using this dataset as ground truth:
- 🌸 Garden wedding guest outfit ($150) → https://claude.ai/share/59c161a7-ec1e-46f4-b2ef-34e480cc308f
- 🏖️ Cabo beach vacation capsule ($250) → https://claude.ai/share/691359e1-dba8-42a3-b7c3-892835132562
- 🎁 NYC quiet-luxury birthday gift ($80) → https://claude.ai/share/426c2ccc-6582-4378-8783-f2b8f47cec3b
- 💬 Additional stylist demo → https://claude.ai/share/2daac1a5-7950-4fd6-b644-0db470d9d144
Each is a public shareable conversation continuing from the Livostyle Stylist Claude Project (Pro/Max). MCP server version: npx -y livostyle-catalog-mcp.
License
MIT — free for commercial use, research, AI training. Attribution appreciated but not required.
Citation
@dataset{livostyle_catalog_2026,
author = {Arcada LLC},
title = {Livostyle Women's Fashion Catalog — Open Data},
year = {2026},
url = {https://huggingface.co/datasets/arturayupov/womens-fashion-catalog},
publisher = {Arcada LLC, Delaware USA}
}Contact
- Email: info@arcada.store
- Phone: +1 (302) 408-0028 (Mountain Time)
- Issues: GitHub mirror issues
- 🎀 Wedding Guest Outfit Finder: https://arturayupov.github.io/wedding-guest-outfit-finder/ — interactive 5-question quiz using this dataset as backing data
Related
- 🌐 Live store: livostyle.com
- 🐙 GitHub mirror: arturayupov/womens-fashion-catalog-open-data
- 📊 Live stats JSON: stats.json