How does AI detect fabric patterns and why your eCommerce needs it?

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Learn how our Fabric Pattern Detection API benefits e-commerce, enabling automated tagging of products and refined search filtering by fabric patterns.

Imagine you’re browsing an online fashion store and finding exactly that checkered pattern shirt you’re looking for in seconds. Sounds like a dream, right? In the fast-paced world of e-commerce, every second counts and making the user experience seamless can make or break a sale.

Our Fabric Pattern Detection API automatically identifies textile patterns in product images, providing accurate categorization and product filtering instantly. This AI solution benefits both retailers and customers by improving the shopping experience and inventory management. In this blog, we’ll explore the ins and outs of this tool, possible use cases and the advantages it brings to e-commerce.

What is the Fabric Pattern detection API?

Fabric Pattern Detection API uses advanced AI algorithms to identify and categorize textile patterns in product images. These textile patterns play a crucial role in defining the style and aesthetic appeal of certain clothing or furniture items.

By using AI, online retailers can improve inventory management and automate the tagging of products with fabric patterns. This not only eliminates manual tagging but also significantly improves the shopping experience by enabling customers to quickly and accurately filter clothes by patterns that align with their specific style preferences.

Why pattern recognition?

Consider the typical online fashion shopper. They browse through countless items, considering price, color, style and trends. Patterns play an equally important role in expressing personal style, yet surprisingly, many leading online fashion retailers have not yet implemented robust pattern search filtering, missing a significant opportunity to improve user experience and boost sales. Below, you’ll find an example of a pattern search filter on an e-commerce site.

Current challenges with fabric pattern filters on e-commerce sites

• Category imbalance: Plain colored is by far the most dominant category, with over 38.000 listed items, while some patterns barely have any products listed (camouflage, fishbone, etc.).
• Category overlap: Similar categories (e.g., logo/motif/motto print) create confusion as they represent pretty much the same thing.
• Low accuracy: Ecommerce filters often misclassify products, leading to frustration as they just don’t work very well. For instance, a polka dot filter might return results with no polka dots in sight. See the example below - four products, zero polka dots!

The absence of quality product filtering by fabric patterns limits users’ ability to navigate efficiently on online platforms and find what they are looking for quickly.

Benefits of integrating AI fabric pattern recognition

• Automated product tagging: Manually tagging product images is time-consuming, inconsistent and impractical for large datasets. AI automates this process, ensuring accuracy and consistency in product tagging.
• Better search performance with refined search filters: With fabric pattern filters, customers can search for products quickly and accurately based on item material patterns.
• Improved user experience: Shoppers can easily discover products that match their desired product patterns, whether it’s classic stripes, vibrant florals, or bold geometric designs.
• Increased sales: By enabling shoppers to quickly find what they like, online fashion stores can simplify the shopping process and potentially boost sales.
• Elevated marketplace efficiency: AI fabric pattern detection tags product photos accurately upon upload by users, improving search functionality and user satisfaction.

Integrating AI fabric pattern recognition in e-commerce platforms & online marketplaces presents a valuable opportunity to better meet the evolving needs of modern consumers, providing a seamless shopping experience and staying competitive in the market.

Which patterns does Fabric Pattern Detection API recognize?

There are many patterns and trying to recognize them all is infeasible, so we’ve decided on the following 10 patterns, which are both representative (they cover a large portion of available clothing items) and detectable with high accuracy:

Within the floral/oriental category, there’s a spectrum of patterns ranging from traditional florals to intricate designs such as paisley, ikat and damask.

The zigzag category also contains multiple similar patterns: chevron, houndstooth, and herringbone.

How does it work?

Behind the API lies a machine learning model trained on hand-labeled photos of clothing items. Both catalog images and user-generated photos were used during training. This way the model learns to focus on the main object in the image, resulting in implicit background removal and allowing the model to achieve high accuracy on a wide range of fashion datasets.

The figure below illustrates the performance of the model, where we can see that even when it fails, the answers are understandable (for example, the zebra pattern in the first row is misclassified as “striped”).

Images in a green frame were correctly classified, while images with a red frame were misclassified, and above them are the model’s top 2 predictions. Images are from the publicly available Kaggle dataset, which was used only for evaluation.

Key features and integration

The key features of our Fabric Pattern Detection API are:

• High accuracy: reliable detection and tagging of fabric patterns
• Versatility: applicable to a wide range of images beyond clothing, such as textile & fabrics, accessories, furniture, home decor, etc
• Scalability: efficiently handles both small and large datasets
• Easy integration: REST JSON API enables easy integration with any backend or e-commerce system
• Customization: adjustable for specific business needs, including adding new fabric pattern categories and fine-tuning on your data

The model performs well on both catalog and user-generated photos and can deal with poor lighting conditions, bad camera angles, and poor image quality in general. Although its primary application is for clothing items, it can be used for a wide range of images, such as fabrics, accessories, home decor items, and more!

Whether you’re processing just a few images or a very large dataset, the API is designed to scale with your needs and efficiently provide you with accurate predictions.

It can be used as a Software-as-a-Service (SaaS) deployed in the cloud or deployed on-premise to satisfy security requirements and privacy policy. The API is implemented as a REST JSON API, enabling easy integration with any backend or e-commerce system.

Last but not least, the model is highly customizable, making it possible for us to adjust it for your specific needs (per request). That includes changing the current categories or even adding new ones, as well as fine-tuning the model on your data to provide even more reliable pattern tagging.

You can try it for free when you sign up by clicking here.

Velebit AI tools for Ecommerce

Fabric Pattern Detection API is the latest among Velebit AI’s solutions and can be combined with our other products specifically designed for online marketplaces and e-commerce businesses.

It can be seamlessly integrated with other Velebit AI products:

• Color Detection APIs: now offers 2 features - main color detection for detecting the primary color in the image and multi color detection for detecting up to 3 colors in the image, automating product tagging and filtering. You can read more about how Color Detection API can improve Product Discovery in our blog post.
• Visual Search: allows users to search for products by taking a picture with their phone or uploading a photo, recommending visually similar items and improving the shopping experience. To learn more, check out our detailed article: What Do You Have to Know About Visual Search?

Get in touch today if you want to automate e-commerce tasks with AI and improve product search & discoverability!