SkinMatch

Introducing SkinMatch: Revolutionizing Skincare with AI

In a world where skincare can be overwhelming, SkinMatch is your guide to effortless, effective skincare. Our web app is designed to empower users with knowledge about their skin, making informed decisions easy and accessible.

The Backstory

Inspired by the desire to bring awareness to skin health and simplify skincare routines, SkinMatch was born. Our team recognized the need for a platform that not only identifies skin issues like acne but also provides personalized solutions and product recommendations.

How It Works

Cutting-edge Technology: SkinMatch leverages the latest in AI and machine learning, using YOLOv8 models for image segmentation and classification tasks.
Easy and Accessible: Upon loading the website, users can simply take a few pictures of their skin. The product then analyzes these images to detect acne and assess complexion and skin attributes.
Personalized Recommendations: By utilizing vector databases, we aim to provide tailored product recommendations, eliminating the guesswork from skincare shopping.

Why SkinMatch?

Comprehensive Solution: SkinMatch offers a one-stop-shop for skincare, combining detection, analysis, and product recommendations in one platform.
Accessible Learning: Our sleek and user-friendly website makes skincare education and product selection easy for everyone.
Empowering Users: SkinMatch empowers users to take control of their skincare journey, promoting confidence and self-care.

What's Next for SkinMatch

Continued Innovation: Our goal is to further enhance our AI models and expand our database to cover a wider range of skin concerns and products.
Community Engagement: We plan to foster a community where users can share their skincare experiences and tips, creating a supportive environment for skincare enthusiasts.
Global Impact: By making skincare accessible and easy, SkinMatch aims to improve skin health worldwide, one user at a time.

Join us on this exciting journey to healthier, happier skin with SkinMatch!

Built With

classification
computer-vision
css
detection
flask
html
image-segmentation
javascript
machine-learning
python
pytorch
react
vector-databases
yolo

Submitted to

HackIllinois 2024

Created by

I mainly worked on training and finetuning the YOLOv8 models using PyTorch and creating inference endpoints. I also worked on the backend functionality, utilizing Python and JS functions.

Neil Ganguly
I worked on React, Flask, Python API, AWS

Aniruddha Lappathi
I worked on the backend mainly, I implemented a Pinecone vector database to store embedded vectors using cosine similarity. I also worked on some of the React, Tailwind CSS, and Python API files

Adesh Kumar
Theodore Ng