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+# ECG Digitization Experiments
+
+This repository contains archived PyTorch `.pth` checkpoints from ECG digitization research experiments (Kaggle competition).
+
+## About
+
+These are model weights for ECG image-to-signal digitization - extracting ECG waveform values from paper ECG images.
+
+**Competition:** [ECG Image Digitization Challenge](https://www.kaggle.com/competitions/ecg-image-digitization)
+
+## Experiments Overview
+
+| Experiment | Architecture | Description | Best SNR |
+|------------|--------------|-------------|----------|
+| v7 | EfficientNet-B4 | Early baseline | ~15 dB |
+| v9 | EfficientNet-B4 | Improved training | ~16 dB |
+| v10 | EfficientNet-B4 | Multi-scale features | ~17 dB |
+| v10_1 | EfficientNet-B4 | Refinements | ~17 dB |
+| v11 | EfficientNet-B4 | 1.5x scale | ~17 dB |
+| v14 | ConvNeXt + SimDR | SimDR heatmap approach | ~18 dB |
+| v15 | Per-Lead CNN | Per-lead extraction | ~18 dB |
+| v16 | Per-Lead + BiLSTM | Temporal coherence | ~19 dB |
+| v18 | Refiner Network | Post-processing refiner | ~19 dB |
+| v19 | Augraphy Augmentation | Paper degradation aug | ~20 dB |
+| v20 | Integral Regression | Integral loss | ~20 dB |
+| v21 | GRU Refiner | GRU-based refinement | ~20 dB |
+| v22 | ConvNeXt-Base + U-Net | Multi-scale fusion + Height Attention + BiLSTM | ~22 dB |
+| **v23** | **V22 + DSNT** | **DSNT Sub-Pixel Head (Rank 3 technique)** | **~22.35 dB** |
+| mixed | Various | Early mixed training | ~16 dB |
+| mixed_v4 | Various | Mixed v4 | ~17 dB |
+| mixed_v5 | Various | Mixed v5 | ~17 dB |
+
+## V23 Architecture (Best Model)
+
+```
+① ConvNeXt-Base Encoder → L3 + L4 multi-scale features
+② Feature Fusion (fine details + semantics)
+③ U-Net Decoder with skip connections
+④ Height Attention (learns vertical regions)
+⑤ BiLSTM (temporal coherence)
+⑥ Conv1D + Linear → Sigmoid (V22 head)
+⑦ DSNT Sub-Pixel Head (Rank 3 winner technique)
+```
+
+## Directory Structure
+
+```
+├── README.md
+├── v7/                    # EfficientNet-B4 baseline
+├── v9/                    # Improved training
+├── v10/                   # Multi-scale features
+├── v10_1/                 # Refinements
+├── v11/                   # 1.5x scale
+├── v14/                   # ConvNeXt + SimDR
+├── v15/                   # Per-lead CNN
+├── v16/                   # Per-lead + BiLSTM
+├── v18/                   # Refiner network
+├── v19/                   # Augraphy augmentation
+├── v20/                   # Integral regression
+├── v21/                   # GRU refiner
+├── v22/                   # ConvNeXt-Base + U-Net
+├── v23/                   # V22 + DSNT (BEST)
+├── mixed/                 # Mixed training v1
+├── mixed_v4/              # Mixed training v4
+├── mixed_v5/              # Mixed training v5
+└── code/
+    ├── scripts/           # All training scripts
+    └── notebooks/         # Kaggle inference notebooks
+```
+
+## Checkpoint Format
+
+Each `.pth` file contains:
+- `model`: Model state dict
+- `opt`: Optimizer state dict
+- `epoch`: Training epoch
+- `snr`: Validation SNR (dB)
+- `mae`: Mean Absolute Error (pixels)
+
+## Usage
+
+```python
+import torch
+
+# Load checkpoint
+checkpoint = torch.load("v23/v23_epoch038.pth", map_location="cpu")
+
+# Load model state
+model.load_state_dict(checkpoint['model'])
+
+# Check metrics
+print(f"Epoch: {checkpoint['epoch']}")
+print(f"SNR: {checkpoint['snr']:.2f} dB")
+```
+
+## Key Files
+
+- `v23/v23_epoch038.pth` - Best V23 checkpoint (22.35 dB)
+- `v22/v22_best_snr.pth` - Best V22 checkpoint
+- `code/scripts/train_v23.py` - V23 training script
+- `code/scripts/inference_v23.py` - V23 inference script
+- `code/notebooks/kaggle_v23_inference.ipynb` - Kaggle submission notebook
+
+## License
+
+Research use only. Please cite if you use these weights.
+
+## Framework
+
+- PyTorch 2.x
+- timm (ConvNeXt-Base encoder)
+- albumentations (augmentation)