# How Mozat's Stylepedia and Milvus Are Redefining Your Closet

Source: https://zilliz.com/blog/building-a-wardrobe-and-outfit-planning-app-with-milvus
Author: Fendy Feng
Published: 2024-01-16T12:00:00.000Z

Staying ahead in the fashion world means not just following trends but also changing how we interact with style. Singapore-based tech company [Mozat](https://zilliz.com/customers/mozat) has taken a bold step in this direction with its innovative wardrobe management approach. At the heart of Mozat's fashion-forward endeavors is Stylepedia, a wardrobe app designed to redefine how users engage with fashion. What sets Stylepedia apart is its integration of [Milvus](https://zilliz.com/what-is-milvus), an open-source vector database, to power its smart image search system. 

![](https://assets.zilliz.com/stylepedia_b99e5dc207.png)

This post will explore how Milvus helps Mozat overcome technical challenges and elevate its image search system.


## Mozat's ambition in fashion

Mozat envisions a community where users can seamlessly discover new styles, receive hyper-personalized fashion suggestions, and connect with like-minded individuals. Therefore, Mozat's Stylepedia app must offer personalized style recommendations, facilitate user connections, and enable image searches for similar clothing items. A robust image similarity search system became the linchpin in achieving these goals. It had to manage a rapidly growing database of clothing images, respond to user queries in milliseconds, and handle user-uploaded photos with varying resolutions. 


## Leveraging Milvus to power Stylepedia's image search system

Building such a powerful image search system is no small feat. Mozat recognized the need for a powerful vector search technology to efficiently store, recognize, comprehend, and compare the vast and ever-growing amount of image data. After careful consideration, Mozat chose Milvus as the neural network for Stylepedia's image search system. Milvus supports mainstream vector indexes, enabling real-time searches on billions of vectors of datasets. These capabilities made Milvus the ideal choice for the ambitious goals set by Mozat.

![](https://assets.zilliz.com/stylepedia_image_search_system_f6d73ba056.png)

Stylepedia's image search system comprises four distinct modules, each contributing to distinctive functionality:

- **Garment Detection**: Real-time object detection and identification of clothing items and classifications.

- **Feature Extraction**: Conversion of images into 512-dimensional vectors.

- **Vector Similarity Search**: Conducting large-scale vector similarity searches.

- **Post-processing**: Refining results using color and critical label filtering for image quality alignment.

Milvus plays a crucial role in the vector similarity search module. Object detection and feature extraction models convert the fashion image library into 512-dimensional feature vectors, which are then indexed and stored in Milvus. On the query side, users' images are transformed into feature vectors, and Milvus performs [Approximate Nearest Neighbor (ANN) searches](https://zilliz.com/glossary/anns) for the Top-K most relevant results. After post-processing, Stylepedia returns a collection of similar images to the user.

By leveraging Milvus, Stylepedia innovates three essential functions:

- **Effortless Image Uploads**: Users can effortlessly upload images of their clothing items, initiating an image search to discover product images that closely resemble their uploads.

- **Item Discovery**: Users can find fashion photographs containing specific items through Milvus-powered image search, generating outfit suggestions through item clustering.

- **Personalized Recommendations**: After analyzing user behavior and preferences, Stylepedia calculates similarity and delivers tailor-made fashion photograph recommendations, creating a personalized fashion exploration experience.


## Milvus drives Stylepedia's success

Empowered by Milvus, Stylepedia emerged as a powerhouse of fashion intelligence. Milvus seamlessly handled billions of image vectors, addressing the concern of increasing data volume. Stylepedia could now generate real-time responses to user queries, vastly improving the user experience. Users could upload images of their clothes and quickly receive a curated selection of similar garments, making Stylepedia a seamless fusion of technology and style expertise.