Fashion Search Demo

• Project: Fashion Search Demo
• Focus: Cross-Modal Retrieval · CLIP Fine-Tuning · ANN Search · FastAPI · Next.js
• Live Demo: fashionsearch.woodygoodenough.com

Project Overview

This project is a composite demo that brings together model training, retrieval systems, backend APIs, and user-facing interaction. The system accepts a natural-language query and returns product images by ranking text-image similarity in a learned shared embedding space.

It was designed as an end-to-end multimodal search application rather than just a model experiment. That means the project covers the full workflow from representation learning and retrieval indexing to API serving and interactive front-end presentation.

What I Built

• A customized CLIP-based retrieval model fine-tuned in PyTorch for fashion-oriented image-text matching
• A Python retrieval backend that exposes search functionality through FastAPI JSON endpoints
• An ANN-based retrieval layer to accelerate nearest-neighbor search over the image corpus
• A Next.js frontend for interactive querying and visual result exploration
• Interactive analysis components that help inspect model behavior, retrieval quality, and representation patterns

Why It Matters

This demo sits at the intersection of applied machine learning and product-minded systems work. It shows how multimodal research can be translated into a usable interface, where the model, retrieval infrastructure, API layer, and frontend experience all need to work together coherently.

It also reflects a broader interest of mine: building systems that make complex ML behavior inspectable rather than opaque.