FindsAIDatabase/findsai-products

---
dataset_info:
features:
- name: availability
dtype: string
- name: brand
dtype: string
- name: canonical_url
dtype: string
- name: category_norm
dtype: string
- name: category_raw
dtype: string
- name: color
dtype: string
- name: created_at
dtype: string
- name: currency
dtype: string
- name: gender
dtype: string
- name: id
dtype: int64
- name: image_primary
dtype: string
- name: price
dtype: float64
- name: raw_json_ld
dtype: string
- name: retailer
dtype: string
- name: retailer_product_id
dtype: string
- name: title
dtype: string
- name: updated_at
dtype: string
splits:
- name: train
num_bytes: 3042006
num_examples: 3826
download_size: 885413
dataset_size: 3042006
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# findsAi Products Dataset

Fashion product catalog for findsAi image matching model.

## Dataset Information

- **Total Products**: 1,505
- **Retailers**: Neiman Marcus (505), The RealReal (500), Nordstrom (500)
- **Categories**: Tops (300), Bottoms (300), Shoes (302), Bags (300), Accessories (303)

## Files

- `findsai_products.json` - Complete product data in JSON format
- `findsai_products.csv` - Product data in CSV format
- `dataset_info.json` - Dataset metadata and statistics

## Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Kanishkagarwal6101/findsai-products")

# Access the data
products = dataset['train']
print(f"Loaded {len(products)} products")

# Example: Get first product
first_product = products[0]
print(f"Title: {first_product['title']}")
print(f"Brand: {first_product['brand']}")
print(f"Image: {first_product['image_primary']}")
```

## Fields

- `id` - Unique product ID
- `retailer` - Retailer name (neiman_marcus, therealreal, nordstrom)
- `retailer_product_id` - Retailer's internal product ID
- `title` - Product title
- `brand` - Brand name
- `price` - Product price
- `currency` - Currency code
- `availability` - Availability status
- `canonical_url` - Product URL
- `image_primary` - Primary product image URL
- `category_norm` - Normalized category
- `category_raw` - Raw category
- `color` - Product color
- `gender` - Target gender
- `raw_json_ld` - Raw JSON-LD data
- `created_at` - Creation timestamp
- `updated_at` - Last update timestamp

## License

MIT License

## Tags

fashion, e-commerce, product-catalog, image-matching, embeddings