Deep Learning with PyTorch: Image Segmentation Project

Overview

This repository contains the source code and documentation for my specialized project on Image Segmentation, focusing on the use of the Albumentations library and PyTorch framework. The project showcases the development of an in-depth understanding and application of segmentation datasets, particularly emphasizing the creation of custom dataset classes for image-mask datasets, implementation of data augmentation strategies, and utilization of state-of-the-art convolutional neural networks for segmentation tasks.

Project Description

The project aims to address the challenges of image segmentation, a crucial task in various applications such as medical imaging, autonomous driving, and scene understanding. By leveraging the Albumentations library, we implemented robust image and mask augmentation techniques to improve the model's generalization capabilities. Additionally, we explored the use of a pre-trained U-Net model, fine-tuning it for our specific segmentation tasks to achieve state-of-the-art performance.

Key Features:

• Custom Dataset Handling: Creation and management of custom dataset classes tailored for image-mask datasets.
• Data Augmentation: Utilization of the Albumentations library for advanced image and mask augmentation techniques.
• Model Training and Evaluation: Loading and fine-tuning a pre-trained U-Net model, along with the development of efficient training and evaluation loops.
• Real-world Application: Application of learned techniques to solve real-world segmentation problems, demonstrating the project's practical significance.

Technologies Used

• PyTorch: An open-source machine learning library widely used for applications in deep learning.
• Albumentations: A fast and flexible library for image augmentation, providing support for a wide range of augmentation techniques.
• U-Net Model: A convolutional network architecture for fast and precise segmentation of images.

Installation

To replicate this project, ensure you have Python 3.6+ installed. Then, install the required dependencies:

pip install torch torchvision albumentations matplotlib notebook

Usage

The project is structured as a Jupyter notebook, Deep_Learning_with_PyTorch_ImageSegmentation.ipynb, providing a step-by-step guide through the dataset preparation, model training, and evaluation process.

To start the notebook, run:

jupyter notebook Deep_Learning_with_PyTorch_ImageSegmentation.ipynb

Follow the instructions within the notebook to execute the code cells.

Results

The project successfully demonstrates the application of deep learning techniques in image segmentation, showcasing the effectiveness of data augmentation and the power of convolutional neural networks in improving segmentation performance.

License

This project is open-source and available under the MIT license.