Multi-Modal-Image-Analysis

This project focuses on various image analysis tasks, including image classification, long-tailed recognition, and generative adversarial networks (GANs). The goal is to develop robust models and techniques to tackle real-world challenges in computer vision.

Problem 1: Convolutional Neural Network (CNN) for Image Classification

We build a CNN model using the CIFAR-10 dataset to accurately classify images into ten different categories. The model leverages deep learning techniques, including convolutional layers and fully connected layers. By optimizing the model architecture and parameters, we aim to achieve high accuracy on the CIFAR-10 test set.

Problem 2: Long-Tailed Recognition with Imbalanced Data

In this task, we address the challenge of imbalanced datasets and long-tailed recognition. We work with the CIFAR-100 dataset, constructing an imbalanced version (CIFAR-30) with varying class distributions. We explore resampling and reweighting techniques to improve model performance on tail classes, and compare the results with the balanced CIFAR-30 dataset.

Problem 3: Generative Adversarial Network (GAN) for Image Generation

The project includes implementing a GAN using the CelebA dataset to generate new celebrity faces. We employ deep learning techniques to train a generator and discriminator network in an adversarial setup. The GAN model learns to generate realistic and diverse facial images, demonstrating the potential of GANs in image synthesis.

This repository showcases our approaches, implementations, and results for each problem. It provides detailed code, instructions, and visualizations to reproduce our experiments. The project aims to explore advanced techniques in image analysis, promote understanding of convolutional neural networks, handle imbalanced data scenarios, and delve into the exciting field of generative adversarial networks.

Requirements

Python 3.7 or above
PyTorch
torchvision
Matplotlib
NumPy Please refer to the individual problem notebooks for specific installation and usage instructions.

Acknowledgments

We acknowledge the contributions of the open-source community and the creators of the datasets used in this project. The project builds upon the foundation of existing research and techniques in computer vision and deep learning.

License

This project is licensed under the MIT License.

Feel free to explore the notebooks, experiment with the code, and adapt the techniques to your own image analysis projects. If you have any suggestions, improvements, or contributions, please follow the guidelines outlined in the repository. Happy exploring!