SAR-SwinIR-SONDRA

Introduction

SAR-SwinIR-SONDRA is a project hosted on Hugging Face, focusing on leveraging advanced SwinIR techniques for SAR (Synthetic Aperture Radar) image processing. The project repository contains models and resources essential for enhancing SAR data interpretation and utility.

Architecture

The architecture of SAR-SwinIR-SONDRA is based on the SwinIR (Swin Transformer for Image Restoration) framework. This innovative design utilizes a transformer-based approach, optimizing image restoration tasks by efficiently capturing both local and global image features. The model is fine-tuned for SAR image data, which requires handling specific challenges such as noise reduction and detail preservation.

Training

The training process for SAR-SwinIR-SONDRA involves fine-tuning the SwinIR model on SAR datasets. This process includes data preprocessing, model configuration, and iterative optimization to enhance the model's performance on SAR-specific tasks. The training leverages supervised learning techniques and is typically executed on high-performance computing resources to manage the computational demands.

Guide: Running Locally

1. Clone the Repository:

   git clone https://huggingface.co/youssefadarrab/SAR-SwinIR-SONDRA
   

2. Install Dependencies: Ensure Python and necessary libraries are installed:

   pip install -r requirements.txt
   

3. Run the Model: Execute the main script to start the model:

   python main.py
   

Suggested Cloud GPUs

For optimal performance, consider utilizing cloud-based GPUs such as those offered by AWS, Google Cloud, or Azure. These platforms provide scalable resources suitable for handling the intensive computations involved in SAR image processing.

License

SAR-SwinIR-SONDRA is licensed under the MIT License, allowing for flexible use, modification, and distribution of the project resources.