---
license: apache-2.0
tags:
  - super-resolution
  - denoising
  - pytorch
  - rrdbnet
  - esrgan
---

# RRDBNet — 4x Super-Resolution / Denoising

An RRDBNet (ESRGAN-style) model trained for 4x image super-resolution and
denoising, initialized from Real-ESRGAN's `RealESRGAN_x4plus` pretrained
weights and fine-tuned on a paired low-resolution / high-resolution dataset.

## Files in this repo

| File | Purpose |
|---|---|
| `model.py` | The `RRDBNet` architecture (with its `ResidualDenseBlock` / `RRDB` submodules) as plain PyTorch code. Import this to reconstruct the model before loading weights. |
| `config.json` | Architecture hyperparameters used to build the model (channel counts, block count, scale factor). |
| `model.safetensors` (or `best_model.pth`) | Trained weights (`state_dict`) for the architecture above. |
| `README.md` | This file. |

## Architecture

- **Type:** RRDBNet (Residual-in-Residual Dense Block Network), the generator
  architecture used in ESRGAN / Real-ESRGAN.
- **Feature channels:** 64
- **RRDB blocks:** 23
- **Growth channels (dense block):** 32
- **Upscale factor:** 4x (two successive 2x nearest-neighbor upsampling stages)

Full config: see [`config.json`](./config.json).

## Training details

- **Initialization:** pretrained on `RealESRGAN_x4plus.pth`, then fine-tuned.
- **Loss:** L1 loss between the super-resolved output and the ground-truth
  high-resolution image.
- **Optimizer:** Adam, initial LR `2e-4`, cosine annealing schedule.
- **Patch size:** 256 (HR), with the LR crop derived at `patch_size / scale_factor`.
- **Batch size:** 8
- **Epochs:** 45
- **Checkpoint selection:** best validation PSNR across epochs.