Using AI-Powered Image Analytics to Recognize Product Attributes in Fashion

Anna is a fashionista and a merchandise manager at a large fast-fashion retailer. As part of her job, she regularly browses through the Web for the most popular designs and trends in contemporary fashion, so she can augment her product assortment with fresh and fast-moving products.

She spots a picture on social media of a fashion blogger sporting a mustard colored, full-sleeved, woolen coat, a yellow sweatshirt, purple polyester leggings, and a pair of pink sneakers with laces. She finds that the picture has garnered several thousand “likes” and several hundred “shares”. She also sees that a few other online fashion influencers have blogged about similar styles in coats and shoes being in vogue.

Anna thinks it’s a good idea to house a selection of similar clothing and accessories for the next few weeks, before the trend dies down.

But, she is in a bit of a pickle.

Different brands represent their catalog differently. Some have only minimalistic text-based product categorization, while others are more detailed. The ones that are detailed don’t categorize products in a way that helps her narrow down her consideration set. Product images, too, lack standardization as each brand has its own visual merchandising norms and practices.

Poring through thousands of products across hundreds of brands, looking for similar products is time-consuming and debilitating for Anna, restricting her ability to spend time on higher-value activities. Luckily, at DataWeave, we’ve come across several merchandise managers facing challenges like hers, and we can help.

AI-powered product attribute tagging in fashion

DataWeave’s AI-powered, purpose-built Fashion Tagger automatically assigns labels to attributes of fashion products at great granularity.

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