Image Colorization and Segmentation

This repository contains implementations of two major computer vision tasks using PyTorch:

1. Image Colorization using a Variational Autoencoder (VAE).
2. Image Segmentation using the U-Net architecture.

Overview

Image Colorization (VAE)

The goal of this part is to colorize grayscale images. A Variational Autoencoder (VAE) is trained to map grayscale input images to their color distribution in the latent space and reconstruct the colored image.

Image Segmentation (U-Net)

This part focuses on semantic image segmentation. The U-Net architecture, known for its effectiveness in biomedical image segmentation, is implemented and trained to classify each pixel of an image into a corresponding class.

Key Features

• VAE Implementation: Custom encoder and decoder networks with reparameterization trick.
• U-Net Architecture: Full implementation of the U-Net model with contracting and expansive paths.
• Custom Datasets: Data loading and preprocessing pipelines for both tasks.
• Training Loops: Complete training, validation, and testing loops with loss visualization.

Dependencies

• Python 3.12
• PyTorch
• NumPy
• Matplotlib
• PIL (Python Imaging Library)
• ipywidgets (for progress bars)

Usage

Open the notebook Image_Colorization_and_Segmentation_VAE_UNet.ipynb in Jupyter Notebook or Google Colab to run the code. Ensure the datasets are placed in the correct directories as expected by the data loaders.