Datasets:
metadata
license: apache-2.0
task_categories:
- image-classification
- text-classification
- zero-shot-classification
tags:
- e-commerce
- product-categorization
- taxonomy
- multimodal
- shopify
- retail
- product-catalog
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: product_title
dtype: string
- name: product_description
dtype: string
- name: product_image
dtype: image
- name: potential_product_categories
list: string
- name: ground_truth_brand
dtype: string
- name: ground_truth_is_secondhand
dtype: bool
- name: ground_truth_category
dtype: string
splits:
- name: train
num_bytes: 7425492387
num_examples: 38631
- name: test
num_bytes: 2085920280
num_examples: 9658
download_size: 9496974081
dataset_size: 9511412667
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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}
}