Deep Fashion Tagging

Deep Fashion Tagging will enrich your knowledge about your fashion data. You can improve user experience on your site leading to increased revenue. Process more products and save on expenses.
  • Recognize
  • Fashion Photos
  • Ready to use


Play with demo of Fashion Tagging See demo

Product Data Curation

Critical benefit of Deep Fashion Tagging is that you speed up your workflows. You can add tags automatically on incoming content 24/7. Sign-up for Ximilar & use the API to get instant predictions. We focus mainly on Fashion and we have a constantly growing list of fashion objects and tags we recognize in the following areas: Clothing, Footwear, Watch, Bags, Accessories, Jewellery, Underwear.



Top Categories Recognised

Clothing

Underwear

Footwear

Bags

Jewelery

Accessories

Watch

Product Photo Metadata

Recently, we have added a new feature to our Fashion Tagging &emdash; recognition of meta information about the product photo:

  • type of the product photo &emdash; e.g. a clothing product, a model wearing the product or a detail of the product
  • type of the background
  • view angle, etc.

It is a great way to automatically select the primary image of given product or to filter out images you don’t want to show in your store at all. Download the fashion taxonomy sheet to see the details.

Custom Solutions

Each business is unique. If our list of fashion tags is not sufficient for you, don’t worry.

  • We can map the fashion taxonomy to yours. We can change the naming or restructure category tree.
  • Please, talk to us about adding features that you miss in our fashion tagging.
  • Or feel free to train your image classifier using our custom classification service. We can help you with that or you can keep it all secret to preserve your competitive advantage.

The resulting workflow can take in account different languages, various taxonomy complexities or add business specific categories & labels. We’ve done that all. Fashion Tagging can be also combined with product similarity to further improve the quality of the similarity product search.

Technology Stack

We use state of the art neural network models & machine learning techniques. Constantly trying to improve the technology so you always have the best quality available. Each model has millions of parameters which can be processed by CPU or GPU. Our intelligent algorithm picks from several models & uses the best performing.

  • Tensorflow-Logo
  • Docker-Logo
  • Nvidia-Logo
  • IbmCloud-Logo
  • Python-Logo
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API Documentation

We take care of the complexity behind and wrap it in a few lines of code.

full documentation
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