Fashion Closet: Real-Time Virtual Try On

Novel VTON technology in real-time optimizing E-Commerce through embeddable widgets

Comment

Inspiration

The inspiration behind "Fashion Closet" was born out of a collective frustration with the limitations of online shopping, particularly the difficulty in envisioning how clothing would look and fit without trying it on physically. Our vision was to create an innovative solution that seamlessly integrates real-time virtual try-on technology, setting a new standard for the online fashion experience.

What it does

"Fashion Closet" is a cutting-edge application that harnesses the power of advanced image recognition and machine learning to bring virtual try-ons to life in real-time. Developed entirely in Python, the application takes images of clothing and dynamically overlays them onto the user's live video feed. This not only provides users with a captivating and lifelike virtual try-on experience but also eliminates the need for the traditional try-before-you-buy approach.

How we built it

The foundation of "Fashion Closet" lies in a meticulously curated dataset comprising over 2000 images for training and 500 testing pairs. Our machine learning model, constructed using a blend of BigQuery ML and state-of-the-art algorithms delivers unparalleled accuracy. The application is containerized using Google Compute Engine, showcasing our commitment to accessibility and scalability. The codebase, intricately woven with Streamlit's web socket and component-based model, achieves an exceptional 50 frames per second on a single Nvidia GPU while consuming a mere 30 MB of memory.

Challenges we ran into

Our journey was not without its share of challenges. Fine-tuning the machine learning model to achieve the perfect balance of accuracy and efficiency required some quantum-level problem-solving. Integrating the real-time virtual try-on functionality, while maintaining high performance and low memory usage, demanded a fusion of creativity and technical finesse. The team's collaborative spirit and innovative thinking ultimately led to overcoming these hurdles.

Accomplishments that we're proud of

We take immense pride in delivering a highly performant system, "Fashion Closet," that seamlessly operates in real-time, providing users with an immersive and lifelike virtual try-on experience. Our success in containerizing the application and deploying it through Google Compute Engine underscores its scalability and accessibility. The distinctiveness of "Fashion Closet" as a groundbreaking application in the market further amplifies our sense of accomplishment.

What we learned

The development of "Fashion Closet" provided us with invaluable learning experiences. From navigating the intricacies of training machine learning models on a custom dataset to mastering the art of deploying containerized applications on the cloud, every challenge served as a learning opportunity. Our exploration of Streamlit's web socket and component-based model enriched our understanding of crafting interactive and responsive user interfaces.

What's next for Fashion Closet: Real-Time Virtual Try On

Looking ahead, our roadmap for "Fashion Closet" involves expanding its horizons and enhancing its usability. We are committed to developing iOS and Android apps, making the virtual try-on experience accessible to a broader audience. Furthermore, we envision seamless integrations, including partnerships with e-commerce giants for iframe embedding and the creation of a versatile Chrome extension. Our dedication is to continually elevate and refine "Fashion Closet" to be at the forefront of real-time virtual try-on technology.

Built With

Share this project:

Updates