Welcome to SRGAN On Custom Dataset 👋

SRGAN On Custom Dataset: Learn how to train SRGAN on a custom dataset to achieve high-quality image super-resolution.

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🏠 Homepage

Introduction

Welcome to the SRGAN project! This repository contains code and instructions for training a Super-Resolution Generative Adversarial Network (SRGAN) on a custom dataset. SRGAN is a powerful neural network architecture that can upscale low-resolution images to high-resolution images with impressive detail and fidelity. The SRGAN model here are of two types:

• SRGAN_self_attention.py : This model incorporates self attention technique inside Generative Adversarial Network (GAN) architecture.
• srgan_model.py : This model is the normal GAN model that increases the resolution of the input image by 4.

✨ Features

• Custom Dataset Support: Train SRGAN on your custom dataset to achieve the best results for your specific use case.
• Flexible Environment: Easily set up and configure the environment using Anaconda and PyTorch.
• GPU & CPU Support: Train and test your model on both GPU and CPU hardware, depending on your resources.
• Simple Training & Testing: Straightforward commands for training and testing the SRGAN model.

Prerequisites

Ensure you have the following prerequisites before proceeding with the installation:

• Anaconda
• Python 3.7+
• Conda

📦 Environment Setup

1. Clone the repository:

   git clone https://github.com/Uddeshya1052/Super_Resolution.git
   cd Super_Resolution
   

2. Create and activate the conda environment:

   Open the Anaconda prompt and navigate to the folder where you have your environment.yml file:

   conda env create -f environment.yml
   conda activate srganenv_gpu
   

3. Set up the environment: Depending on your hardware, choose the appropriate PyTorch installation:

   • GPU

       conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch
     

   • CPU

     conda install pytorch-cpu==1.1.0 torchvision-cpu==0.3.0 cpuonly -c pytorch
     

🚀 Usage

Train Your Model:

To train your model on a custom dataset, execute the following command:

python main.py --LR_path Custom_dataset/LR_Images --GT_path Custom_dataset/HR_images

• --LR_path: Path to your low-resolution training images.
• --GT_path: Path to your ground truth high-resolution images.

Test Your Model

python main.py --mode test_only --LR_path test_data --generator_path ./model/srgan_custom.pt

• --mode test_only: Set this flag to indicate testing mode.
• --LR_path: Path to your low-resolution test images.
• --generator_path: Path to your trained SRGAN generator model.

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page. You can also take a look at the contributing guide.

👤 Author

• Name: Uddeshya Srivastava
• GitHub: Uddeshya1052
• LinkedIn: Uddeshya Srivastava