Advanced Computer Vision Techniques

Description

This repository showcases the implementation of two computer vision techniques: a Denoising Autoencoder and a Region Proposal Network (RPN). These Jupyter notebooks provide a deep dive into the mechanisms behind image denoising and efficient object detection, demonstrating the power and versatility of deep learning in image processing tasks.

Notebooks Overview

• DenoisingAutoEncoder.ipynb: Offers a deep dive into the design, implementation, and training of a Denoising Autoencoder. It emphasizes the use of TensorBoard for tracking model performance, featuring a comprehensive guide on interpreting loss graphs and adjusting model parameters for optimal denoising results.

• RPN_Implementation.ipynb: Provides a step-by-step tutorial on building a Region Proposal Network from the ground up. This notebook covers everything from data preprocessing to model training on a sample dataset, elucidating the practical aspects of developing effective object detection systems.

• Gaussian-Bernouli_RBM.ipynb: Provides a implemention of a Gaussian-Bernoulli Restricted Boltzmann Machine (RBM). Restricted Boltzmann Machines are a class of neural networks that serve as generative stochastic models, capable of learning complex distributions over binary data. The Gaussian-Bernoulli RBM variant is particularly suited for handling continuous data.

• CVAE.ipynb: This ia an implementation of a Conditional Variational Autoencoder (CVAE). CVAE is an extension of the Variational Autoencoder (VAE), a popular generative model which can learn the underlying probability distribution of a dataset, allowing them to generate new data points that resemble the original data. CVAEs build upon this by incorporating conditional variables into the model, enabling the generation of data points based on specified conditions. This makes CVAEs particularly useful for tasks where control over certain aspects of the generated data is desired.

Key Features

• In-depth Tutorials: Each notebook serves as a detailed tutorial, walking through the theory and practice of constructing and training deep learning models in computer vision.
• Performance Tracking: Demonstrates the application of TensorBoard for real-time tracking of model performance, with guidance on how to leverage visual data for model improvement.

Installation

To get started with these notebooks, you will need Python and Jupyter installed on your system. Clone this repository and install the required dependencies:

git clone https://github.com/relar-Ritik/Computer-Vision.git
cd Computer-Vision
pip install -r requirements.txt

Usage

Navigate to the cloned repository's directory and start Jupyter Notebook:

jupyter notebook

Open either `DenoisingAutoEncoder.ipynb` or `RPN_Implementation.ipynb` to begin exploring the techniques discussed.

Requirements

This project is built using Python 3.x and requires the following libraries:

• PyTorch (for deep learning models)
• NumPy (for numerical computations)
• Matplotlib (for data visualization)
• OpenCV (for image processing)
• Jupyter (for interactive notebooks)

Example Images:

Object Detection:

Image Denoising: