Examples

Runnable, CPU-only examples ship in the examples/ directory. They do not download any model or dataset.

1. Synthetic data

examples/basic_segmentation_calibration.py

Creates over-confident synthetic 2D logits, splits them into calibration and test sets, fits TemperatureScaling, prints NLL and ECE before and after, then saves, reloads and verifies the calibrator produces identical output.

python examples/basic_segmentation_calibration.py

2. A generic PyTorch model

examples/pytorch_model_calibration.py

Uses a tiny nn.Module segmenter to show the real-world workflow: run the model over a DataLoader, stack the logits and labels, fit a calibrator and apply it at inference time.

python examples/pytorch_model_calibration.py

The key step is collecting logits in the layout Fiducio expects:

@torch.no_grad()
def collect_logits(model, loader):
    model.eval()
    logits, labels = [], []
    for images, targets in loader:
        logits.append(model(images))   # (B, C, H, W)
        labels.append(targets)         # (B, H, W)
    return torch.cat(logits), torch.cat(labels)

The exact same procedure applies to a U-Net, nnU-Net or SegFormer — only the model and the source of images/targets change. See Concepts.

3. Reliability diagram

examples/reliability_diagram.py

Fits TemperatureScaling and saves a before/after reliability diagram as a PNG. Requires the plotting extra (pip install "fiducio[plots]"); see Plotting.

python examples/reliability_diagram.py

4. Ensemble pooling

python examples/ensemble_pooling.py demonstrates two separate CMSap fits: one on the mean probabilities (input_type="probs") and one on the mean logits (input_type="logits"). Members are pooled before fitting. Calibration and test cases are disjoint. These are synthetic examples, not paper benchmark results.