Inspiration
We were inspired by how people often struggle to match outfits, understand color harmony, and identify their personal style. We wanted to create an AI tool that makes fashion more accessible, confident, and fun using computer vision and color scienc
What it does
Fashion-Analyzer-Color-Sense (Coco) analyzes an uploaded outfit image, detects clothing items, extracts dominant colors, and evaluates color harmony based on theory. It then provides suggestions, matching palette options, and style improvements.
How we built it
We combined a deep-learning model for clothing detection with a color-extraction pipeline that uses clustering and color harmony rules. The frontend uses React/Streamlit, while the backend is powered by Python, FastAPI, and OpenCV for image processing.
Challenges we ran into
Handling diverse lighting conditions and extracting accurate colors was difficult. We also struggled with fine-tuning the detection model for different clothing categories and optimizing the pipeline to run fast on limited compute.
Accomplishments that we're proud of
We built a system that can reliably detect outfits, extract clean color palettes, and generate smart styling suggestions. Our biggest win is turning raw fashion images into meaningful insights using AI—clean, fast, and usable.
What we learned
We learned how to integrate computer vision with color science, build an end-to-end AI pipeline, and improve model accuracy for real-world fashion images. We also discovered how much design, UI, and user experience matter in a fashion-oriented tool.
What's next for Fashion-Analyzer-Color-Sense----Coco
Next, we plan to add virtual try-on features, personalized wardrobe recommendations, and an AI stylist chatbot. We also want to support skin-tone matching, seasonal palettes, and a mobile app for real-time outfit analysis.
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