srforge 0.1.0

Super‑resolution research framework for PyTorch with a focus on simplicity and flexibility using config files.

SR FORGE

Super-Resolution Framework for Oriented Restoration and Guided Enhancement

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SR FORGE (Super-Resolution Framework for Oriented Restoration & Guided Enhancement) is a unified, modular, and task-driven framework for training and evaluating deep learning models in the field of super-resolution.

Key Features

• Structured Workflow
  SR FORGE provides an organized approach to super resolution. Every step—from data loading to final evaluation—follows a clear, modular structure.

• Task-driven restoration
  Built-in utilities to help fine-tune models for specific tasks or objectives (e.g., OCR, remote sensing, medical imaging, etc.).

• Config-Driven Experiments
  Simple YAML/JSON configuration files let you customize your pipeline without modifying code directly.

• Flexible Model Plug-In
  Easily integrate popular SISR (EDSR, RCAN, ESRGAN, etc.) and MISR (RAMS, HighRes-net, PIUNET, TR-MISR, MagNAt) or your own custom architecture.

• Unified Metrics
  Evaluate your models with a suite of standard metrics (PSNR, SSIM, LPIPS) and straightforward logging.

• Visualization Tools
  Quickly visualize results (side-by-side comparisons, zoom-ins, or overlays) for interpretability and debugging.

Installation

1. Clone the Repository

   git clone https://github.com/your-username/sr-forge.git