Shopify/product-catalogue

The Catalogue: Product Taxonomy Classification Benchmark

A large-scale, multimodal benchmark dataset for product taxonomy classification, featuring real e-commerce products with images, descriptions, and hierarchical category labels.

Dataset Description

The Catalogue is a benchmark dataset designed to evaluate AI models on the task of classifying products into a standardized taxonomy. Each sample includes a product image, title, description, brand, and the ground-truth category from Shopify's product taxonomy.

This dataset is ideal for:

• Evaluating vision-language models on real-world product classification
• Benchmarking multimodal understanding in e-commerce contexts
• Testing hierarchical classification capabilities
• Comparing different approaches to product categorization

Dataset Statistics

Metric	Value
Total samples	48,289
Unique categories	10,476
Unique brands	28,913
Products with descriptions	92.9%
Products with brand	98.2%
Average category depth	4.5 levels
Category depth range	1-8 levels

Top-Level Category Distribution

Category	Count	Percentage
Home & Garden	7,912	16.4%
Sporting Goods	6,968	14.4%
Arts & Entertainment	5,558	11.5%
Hardware	5,137	10.6%
Vehicles & Parts	2,555	5.3%
Business & Industrial	2,435	5.0%
Electronics	2,344	4.9%
Apparel & Accessories	2,173	4.5%
Health & Beauty	2,162	4.5%
Food, Beverages & Tobacco	2,036	4.2%
Animals & Pet Supplies	1,922	4.0%
Furniture	1,707	3.5%
Baby & Toddler	1,226	2.5%
Toys & Games	1,153	2.4%
Office Supplies	1,129	2.3%
Cameras & Optics	967	2.0%
Other	900	1.9%

Dataset Structure

Data Fields

Field	Type	Description
product_title	string	Product title
product_description	string	Product description (may be empty)
product_image	Image	Product image
potential_product_categories	list[string]	Candidate category paths
ground_truth_brand	string	Brand name (may be empty)
ground_truth_is_secondhand	boolean	Whether the product is secondhand
ground_truth_category	string	Ground truth category path

Category Format

Categories follow a hierarchical path format:

Electronics > Computers > Laptops
Home & Garden > Kitchen & Dining > Cookware

Data Splits

Split	Samples	Percentage
Train	38,631	80%
Test	9,658	20%

Usage

from datasets import load_dataset

dataset = load_dataset("Shopify/product-catalogue")

# Access splits
train_data = dataset["train"]
test_data = dataset["test"]

# Example
sample = train_data[0]
print(f"Title: {sample['product_title']}")
print(f"Category: {sample['ground_truth_category']}")
sample['product_image'].show()

Evaluation

The primary task is to predict the ground_truth_category given the product's image, title, and optionally description/brand.

Suggested metrics:

• Hierarchical F1 for categories
• Precision and recall for brand and is_secondhand

Source

Products were sampled from Shopify's merchant catalog, representing real e-commerce products across diverse categories and industries.

License

Apache 2.0

Citation

@dataset{product-catalogue,
  title={The Catalogue: Product Taxonomy Classification Benchmark},
  author={Shopify},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/Shopify/the-catalogue-public-beta}
}