Frequently Asked Questions

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Fashion Tagging

Fashion Tagging is a visual AI service that automatically recognizes the fashion product in a product image, categorizes it and provides tags. It can be easily combined with automatic object detection to categorize and tag all the fashion products in complex images separately.

We provide both Fashion Tagging and a more complex service called Fashion Search, which implements Fashion Tagging, as well as Visual & Similarity Search (Search by Photo) and Object Detection.

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The automated Fashion Tagging is used on fashion product images of e-shops, price comparators, fashion brands, and specialized collections. It is based on numerous image recognition tasks trained to recognize separate product categories, as well as object detection models. That is why it works on both single product images and more complex images, including user-generated content or social media images.

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Fashion Tagging is one of our most complex ready-to-use services. It works with over a hundred recognition tasks, hundreds of labels, and dozens of fashion attributes. It identifies the top category of product (e.g., accessories, bags, jewellery, watch, clothing, underwear, footwear), then the category (e.g., accessories/belts or underwear/bras), and its features such as colour, design, material, or pattern. Furthermore, it can be combined with object detection to ensure even more complex images are properly tagged. If you miss any important attributes, the taxonomy can be adjusted to fit your use case.

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You can download our full fashion taxonomy in a PDF under the link below, try out our public demo, or check the API documentation for news and additions.

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Fashion Tagging can be combined with object detection to categorize and tag individual items in a more complex fashion image. You can also use Fashion Search, which automatically detects apparel, footwear and accessories in your images, provides tags, and finds the most similar products or images.

These fashion services work on both product images and real-life photos, e.g., fashion influencer pictures.

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Yes! Ximilar has a free and public Fashion Tagging demo. You can either upload images or their URLs and see for yourself how automatic fashion tagging works.

You can also use Fashion Tagging in our App. See our Pricing for details. If you have large volumes of images to be processed every month or need customization, contact us to discuss a custom plan.

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Yes, you can! Automated Fashion Tagging works on product images as well as real-life photos. Our tagging combines object detection, which identifies fashion items in an image, with image recognition, which categorizes these items and provides you with tags.

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We recommend downloading the Ximilar Fashion Tagging taxonomy, checking the API documentation, and trying how the service works in our public demo and App before setting up a custom solution. If you do not find the attributes you need, contact us to modify the service to fit your use case.

You can also try to train your own custom categorization or object detection tasks using the Custom Image Recognition services.

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With Ximilar, you can use your own taxonomy and get the results of Fashion Tagging in your own language. It is achieved by mapping your taxonomy to ours. Contact us to set up a custom profile.

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Home Decor & Furniture Tagging

Automated Home Decor & Furniture Tagging is a visual AI service that automatically recognizes categories and sub-categories in furniture or home decor product images, and provides tags describing the main products.

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The automated Home Decor & Furniture Tagging works mainly with home decor and furniture product images from price comparators, sellers, hotels, architectural studios, designers, and specialized collections. You can try how it works on your images in a public demo.

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This service categorizes and tags the dominant home decor or furniture item in the image. It identifies the top category of image (all rooms, bathroom, bedroom, kitchen), then the category (e.g., bedroom/duvet covers), and its features such as colour, shape, pattern, and material.

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The full taxonomy is available in the API documentation.

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