Datasets:

Octoparse
/

ecommerce-visual-matching-dataset

This dataset is a sample corpus designed to train computer vision models, multimodal LLMs, and e-commerce AI agents on Visual Matching and Product Resolution tasks.

This clean, structured sample was extracted and normalized by the Octoparse Managed Data Service team.

📊 Dataset Overview

Training models to match identical products across different websites requires high-quality image-to-text and image-to-image pairs. Building the pipeline to extract these images, bypass anti-bot protections, and structure the metadata takes months of engineering.

We’ve provided a sample of what a production-ready visual matching pipeline looks like.

Format: JSONL / Parquet (Replace with your actual format)
Domain: E-commerce / Retail
Use Cases: Multimodal fine-tuning, automated catalog matching, visual search training.

🗂️ Data Structure (Schema)

(Note: 替换成你真实的字段)

image_url: High-resolution source image link
product_title: Extracted product name
source_platform: Website where the data was extracted
matched_id: Unique identifier for identical products across platforms
metadata: JSON object containing variants, colors, and dimensions

🚀 Need 10 Million Rows of Custom Training Data?

Common Crawl is too noisy. Building your own scrapers is a waste of your engineering talent.

If your team is building an AI agent or fine-tuning an LLM and needs highly specific, deduplicated data (text, images, or social signals from platforms like Xiaohongshu/Douyin):

Stop building scrapers. Let us build the pipeline.

👉 Request a Free Custom Sample Dataset from Octoparse We scope the project, handle the extraction, and deliver analysis-ready data to your S3/Snowflake in days.