Fashion Recommendation System Using Image Features

This repository demonstrates the process of building a Fashion Recommendation System using image features. By leveraging computer vision and pre-trained deep learning models, this system analyzes the visual characteristics of fashion items (e.g., color, texture, style) and recommends similar or complementary products.

Overview

Fashion recommendation systems play a crucial role in enhancing user experience by suggesting visually similar items based on user preferences. This project uses a pre-trained Convolutional Neural Network (CNN) model, VGG16, to extract deep feature representations from fashion images and compute similarities among them.

Key Features:

1. Pre-trained Model: Utilizes VGG16, trained on ImageNet, for feature extraction.
2. Feature Normalization: Extracted features are normalized for better similarity computation.
3. Similarity Computation: Recommends items by ranking based on feature similarity.
4. Customizable Pipeline: Flexible structure to adapt other datasets and models.

Outputs:

Future Enhancements

• Experiment with other pre-trained models like ResNet or InceptionV3.
• Add support for multi-modal recommendations (e.g., combining text and images).
• Improve recommendation speed by optimizing feature extraction and similarity computation.