Coverage-Stratified Split

Overview

In segmentation tasks, naive random splits can produce folds with unbalanced foreground/background ratios — some folds get mostly empty masks, others get mostly full masks. Compute per-image mask coverage (foreground pixel ratio), bin into discrete classes, and use stratified splitting on these bins. This ensures each fold sees the full range of mask densities.

Quick Start

import numpy as np
from sklearn.model_selection import train_test_split

coverage = masks.sum(axis=(1, 2)) / (masks.shape[1] * masks.shape[2])

def coverage_to_class(val):
    for i in range(0, 11):
        if val * 10 <= i:
            return i
    return 10

coverage_classes = np.array([coverage_to_class(c) for c in coverage])

X_train, X_val, y_train, y_val = train_test_split(
    images, masks, test_size=0.2,
    stratify=coverage_classes, random_state=42
)

Workflow

1. Compute mask coverage ratio for each training image (sum of foreground pixels / total pixels)
2. Bin coverage into discrete classes (e.g., 0-10% → class 0, 10-20% → class 1, ...)
3. Use binned classes as stratify parameter in train_test_split or StratifiedKFold
4. Validate that each fold has similar coverage distribution

Key Decisions

• 10 bins: covers 0-100% in 10% increments — fine enough for most tasks
• Empty mask handling: images with 0% coverage form their own bin, preventing empty-mask imbalance
• vs random split: critical when dataset has skewed coverage distribution (many empty masks)
• With KFold: use StratifiedKFold(n_splits=5).split(X, coverage_classes) for cross-validation

References

• U-net, dropout, augmentation, stratification
• U-net with simple ResNet Blocks v2 (New loss)