How Mozat's Stylepedia and Milvus Are Redefining Your Closet
Staying ahead in the fashion world means not just following trends but also changing how we interact with style. Singapore-based tech company 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, an open-source vector database, to power its smart image search system.
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.
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 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.
Fendy FengFendy Feng is the Technical Marketing Writer at Zilliz. She has extensive experience developing and enhancing the impact of open-source projects in various global markets by producing high-quality, tailored content. Before joining Zilliz, Fendy worked as a Content Strategist at PingCAP, a fast-growing E-Series startup renowned for its open-source distributed SQL database.
- Mozat's ambition in fashion
- Leveraging Milvus to power Stylepedia's image search system
- Milvus drives Stylepedia's success
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