cv-iou-threshold-sweep

Overview

Segmentation models output continuous probability maps but metrics require binary masks. The default 0.5 threshold is often suboptimal — shifting it can improve IoU by several points with zero cost. This technique sweeps a dense grid of thresholds over the validation set, measures mean IoU at each, and picks the best one as the final binarization cutoff.

Quick Start

import numpy as np

def iou_metric_batch(y_true, y_pred_binary):
    batch = y_true.shape[0]
    return np.mean([iou_metric(y_true[i], y_pred_binary[i]) for i in range(batch)])

# Sweep thresholds
thresholds = np.linspace(0.2, 0.9, 31)
ious = np.array([
    iou_metric_batch(y_valid, (preds_valid > t).astype(np.int32))
    for t in thresholds
])
best_idx = np.argmax(ious)
best_threshold = thresholds[best_idx]
best_iou = ious[best_idx]

print(f"Best threshold: {best_threshold:.3f} → IoU: {best_iou:.4f}")

Workflow

1. Generate continuous predictions on the validation set
2. Define a threshold grid — linspace(0.2, 0.9, 31) works for most tasks
3. For each threshold, binarize and compute mean IoU
4. Pick the argmax threshold and apply it at test time
5. Optional: also compute the best threshold per fold and average them

Key Decisions

• Grid resolution: 31 points is usually enough. Finer grids rarely change the answer.
• Grid range: (0.2, 0.9) covers most cases. Extend to (0.05, 0.95) if predictions are very peaked.
• Per-fold vs global: Per-fold thresholds capture calibration differences but risk overfitting — average them for the final submission.
• vs. fixed 0.5: Almost always better. A properly tuned threshold routinely adds 1-3 IoU points.

References

• Nested Unet with EfficientNet Encoder