Update README.md

README.md

@@ -24,4 +24,29 @@ This repository contains the trained weights for a high-performance Super Resolu
    - **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.
+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()