| --- |
| 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() |
