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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

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 

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)