Image Captioning Project

This repository contains an image captioning system that generates descriptive captions for images using deep learning techniques.

Project Overview

The project utilizes a neural network model to analyze images and produce relevant textual descriptions. This involves processing images through a convolutional neural network (CNN) to extract features, which are then used by a recurrent neural network (RNN) to generate captions.

Repository Structure

• .github/workflows/: Contains GitHub Actions workflows for continuous integration and deployment.
• Image caption-Copy1.ipynb: Jupyter Notebook demonstrating the image captioning model and its implementation.
• Mini project.pdf: Documentation detailing the project's objectives, methodologies, and findings.
• app2.py: Python script for running the image captioning application.
• requirements.txt: Lists the Python dependencies required to run the project.
• Various pickle files (.pkl) and text files (.txt) for storing model data, tokenizers, and evaluation metrics.

Getting Started

Prerequisites

Ensure you have Python installed on your system. Install the necessary packages using:

pip install -r requirements.txt

Running the Application

To generate captions for your images:

Place your images in the appropriate directory.

Run the app2.py script:
python app2.py

Follow the on-screen instructions to input your image and receive the generated caption.

Model Architecture

The image captioning model combines a CNN for feature extraction from images and an RNN (specifically, an LSTM) for generating text sequences. The architecture is visualized in the model.png file.

Evaluation

The model's performance is evaluated using BLEU scores, which are provided in the bleu_scores.txt file.