---
license: mit
pipeline_tag: image-to-image
tags:
- super-resolution
- real-esrgan
- swinir
- pytorch
---

# NPPE3 Super Resolution - Hybrid Ensemble

## Overview
This repository contains the trained weights for a high-performance Super Resolution ensemble used in the NPPE3 competition. The solution achieved a **98.1% grading equivalent (39.50+ dB)** by combining the local geometric edge-detection of a Pure CNN with the global texture-reconstruction of a Transformer.

## Models Included in this Repository
1. **`best_realesrnet.pth` (The Architect)**
   - **Architecture:** RRDBNet (Residual-in-Residual Dense Block)
   - **Training:** 90 Epochs using Charbonnier Loss and FP16 Mixed Precision. 
   - **Purpose:** Acts as a hardcore edge-detector to perfectly reconstruct geometric boundaries.

2. **`best_swinir.pth` (The Painter)**
   - **Architecture:** SwinIR-Medium (Swin Transformer)
   - **Purpose:** Uses self-attention to flawlessly reconstruct repeating global textures (fabric, grass, etc.) that CNNs typically over-smooth.

## The Ensemble Strategy
To achieve the final Kaggle Leaderboard score, the predictions from these two architectures were mathematically blended using a **75% RealESRNet / 25% SwinIR** ratio. This specific weighting ensures maximum edge preservation while injecting just enough Transformer attention to patch localized texture blind spots.

## Validation Results (Competition Metric-Style PSNR)

- **RealESRNet (Validation Set):** 39.58 dB
- **SwinIR (Validation Set):** 39.47 dB
- **Final 75/25 Ensemble (Kaggle Leaderboard):** 39.501 dB

## Files

- `best_realesrnet.pth` — best RealESRNet checkpoint (state_dict)
- `best_swinir.pth` — best SwinIR-Medium checkpoint (state_dict)

## Usage

```python
import torch
from model import RRDBNet  # see Kaggle notebook for the class definition

# Initialize the RealESRNet Architecture
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32)

# Load the golden weights
ckpt = torch.load("best_realesrnet.pth", map_location="cpu")
model.load_state_dict(ckpt['params_ema'] if 'params_ema' in ckpt else ckpt, strict=True)
model.eval()