Machine Learning for Text and Image Matching

This project focuses on developing and implementing machine learning techniques to perform matching between textual descriptions and images. The objective is to create models that can accurately associate text with relevant images and vice versa, enabling effective content-based search and retrieval systems.

Project Overview

The "Machine Learning for Text and Image Matching" repository contains a collection of algorithms, data processing techniques, and deep learning models aimed at solving the problem of multi-modal matching. The project leverages state-of-the-art machine learning models, such as Convolutional Neural Networks (CNNs) for images and Recurrent Neural Networks (RNNs) for text, to build a robust system capable of understanding both visual and textual data.

Key features include:

• Image Processing: Using CNN-based models to extract meaningful features from images.
• Text Processing: Implementing NLP-based techniques, including word embeddings and sequence modeling using RNNs or Transformers, to convert text into comparable vectors.
• Matching Algorithm: The system computes the similarity between the image features and the text embeddings to establish a match between them.
• Data Preprocessing: Preprocessing steps for both image and text data to ensure efficient model training and improved performance.
• Evaluation Metrics: Methods for evaluating the model's performance using standard metrics such as accuracy, precision, recall, and F1-score for matching tasks.

Installation

Clone the repository:

git clone https://github.com/javaidiqbal11/Machine-Learning-Text-Image-Matching.git

Install the required dependencies:

pip install -r requirements.txt

Datasets

The project supports various datasets containing both text and image pairs for training and testing purposes. You can use standard datasets such as:

• MS COCO
• Flickr30k
• Custom datasets

Make sure to preprocess the dataset into the required format before feeding it to the model.

Models

The repository contains the following models:

• CNN for Image Encoding: Extracts features from input images.
• RNN/LSTM for Text Encoding: Converts text descriptions into feature vectors.
• Transformer Models: Supports transformer-based architectures for enhanced matching performance.
• Similarity Matching: The final layer computes the cosine similarity between text and image embeddings to find the best match.

Contributing

Contributions to this project are welcome. If you'd like to contribute, please fork the repository and submit a pull request.