# 🖼️ AI Visual Matching & Product Resolution Sample 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](https://www.octoparse.com/data-service/web-data-for-ai) 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](https://www.octoparse.com/data-service/web-data-for-ai)**
*We scope the project, handle the extraction, and deliver analysis-ready data to your S3/Snowflake in days.*