Apply catalog categories
Suggest the approved category for new product descriptions.
STAYCHARTED AMT / RETAIL AND E-COMMERCE
Product categorization for retail and e-commerce
Apply your catalog taxonomy to product descriptions or photos using models trained on examples your team has already categorized.
FROM YOUR EXAMPLES
Insulated stainless steel bottle, 750 ml, screw-top lid.DrinkwareIllustrative category · Not a live prediction
Where this fits in your work
Sort product photos
Use Vision to classify images from category folders or picture links.
Prepare supplier imports
Review consistent category suggestions before importing a supplier file into your catalog.
PREPARE YOUR EXAMPLES
Start with a file your team has already labeled.
Start with approved product descriptions and their categories. Keep SKU identifiers for your own tracking, but choose meaningful fields such as the description for the model to read. Train on the categories you want back; classification does not create a new catalog taxonomy for you.
Use one row per example with an approved answer. Keep the input representative of what the model will receive later, and agree on how to label ambiguous cases before training.
| Product description | Approved category |
|---|---|
| Insulated stainless steel bottle, 750 ml | Drinkware |
| Canvas shopper with reinforced handles | Tote bags |
| Cotton crew-neck short-sleeve shirt | T-shirts |
Illustrative rows and categories. Use your own approved labels.
From examples to reviewed results
Prepare
Resolve inconsistent labels and remove unnecessary sensitive information.
Train
Choose a model on your plan and train on representative examples.
Validate
Inspect held-out accuracy by category and the mistakes that remain.
Use and improve
Publish, fill a file or call the API, then review and feed corrections into the next version.
KEEP REVIEW IN THE WORKFLOW
Know where the model stops.
Check new product families, ambiguous category boundaries, and unusual image backgrounds. Keep SKU identifiers for tracking; classification does not manage stock levels, identify every exact SKU, or build a taxonomy automatically.
Evidence and implementation
Add pictures with Vision
Train a separate picture model from category folders or a spreadsheet of image links and labels. Use it to suggest categories for new arrivals or organize catalog photos. Check duplicates and mislabeled images before training. Vision reads the image, not text extracted from a photographed label.
Compare against relevant evidence
The image benchmark compares SigLIP 2 and DINOv2 on product photos and pet breeds. Their relative performance changes with the task and number of examples. Read the report, then measure on your own product range, backgrounds, and category definitions.
Review before updating your catalog
Fill a file with predicted categories and confidence values, then review exceptions before importing it into your catalog system. For ongoing arrivals, use the API from your own software. Keep examples current as you add product lines or change category boundaries.
File fills return category suggestions for your existing import process. API calls return results to your own software; your integration decides how to apply them. Plan allowances and workspace rate limits still apply.
Common questions
Can text and pictures use the same categories?
You can define the same labels for separate text and picture models. Choose examples suited to each model; this workflow does not assume a combined text-and-image model.
Does it extract text from product packaging?
No. Vision classifies what a picture shows. Use an existing OCR or data-entry process if you need text extracted from packaging.
Can results go back into our store?
Download a filled file for your import process, or build an integration with the API. Review and apply the changes in your own catalog system.
Explore the details
Try it on your own examples.
Start with Free for text classification. Compare plans for AI models, Vision, and API access.