| # 🖼️ AI Visual Matching & Product Resolution Sample Dataset |
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| 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. |
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| **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.** |
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| ## 📊 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. |
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| We’ve provided a sample of what a production-ready visual matching pipeline looks like. |
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| * **Format:** JSONL / Parquet (Replace with your actual format) |
| * **Domain:** E-commerce / Retail |
| * **Use Cases:** Multimodal fine-tuning, automated catalog matching, visual search training. |
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| ## 🗂️ 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 |
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| ## 🚀 Need 10 Million Rows of Custom Training Data? |
| Common Crawl is too noisy. Building your own scrapers is a waste of your engineering talent. |
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| 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): |
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| **Stop building scrapers. Let us build the pipeline.** |
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| 👉 **[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.* |
