Fashion Recommendation System

Recommend fashion items based upon uploaded image 

Project Description 

This project focuses on building a Fashion Recommendation System using deep learning techniques to suggest similar or relevant clothing items based on user input. The system is trained on an e-commerce fashion dataset (Myntra) and leverages a VGG16-based Convolutional Neural Network to extract visual features from product images. The application is deployed as a web-based platform, where the frontend and backend communicate through REST APIs to deliver real-time recommendations. 

Features 

• Image-based fashion product recommendations
  
  

• VGG16 CNN model for visual feature extraction
  
  

• Trained on real-world e-commerce (Myntra) dataset
  
  

• Web-based interface for uploading or selecting products
  
  

• Real-time recommendation results via Flask REST APIs
  
  

• Suitable for AI, deep learning, and final-year projects

Tech Stack

• Python

• VGG16 Architecture

• TensorFlow (Deep Learning)

• Convolutional Neural Networks (CNN)

• Flask (Backend Framework)

• HTML, CSS, JavaScript (Frontend)

• NumPy and other supporting Python libraries

System Requirements

• Python 3.10+

• Anaconda

• No GPU is required

Deliverables 

• Jupyter Notebook

• Dataset

• Setup over video call 

• WhatsApp support