Discover your next favorite clothing piece with just a click! Upload a picture and let FashionAId instantly recommend clothing that matches your style and preferences.
With FashionAId users can upload a product image or describe a product in text. The system analyzes these inputs and returns the closest matching items from our inditex database. Each recommendation includes the description of the product and the similarity score, based on this description.
Our system's backbone is a Python-based backend. We leverage a RAG (Retrieval Augmented Generation) Architecture by Product Fashion Images with the tiny Llava-1.5b model into high dimensional vector embeddings and saved them to a vector database to get efficient similarity search via cosine similarity. Our results are showcased through an easy-to-use React-developed frontend, offering users an intuitive and engaging interface.
- Python
- Flask
- React
- chromaDB
- llava
The project was developed during the HackUPC together with Karl Jahnel, Donato Monti and Daniel Banov. The challenge was proposed by the company Inditex, where participants were given a large dataset of product images and were tasked with building a similarity matching system.
Our project submission received a honorable mention by the companies jury.
Check out our blog post on devpost: https://devpost.com/software/inditex-fashionaid