Evaluation

Evaluation functions for models.

image_classification_tools.pytorch.evaluation.evaluate_model(model, test_loader)[source]

Evaluate model on test set.

Note: Assumes data is already on the correct device.

Return type:: tuple[float, ndarray, ndarray]

Functions

image_classification_tools.pytorch.evaluation.evaluate_model(model, test_loader)[source]

Evaluate model on test set.

Note: Assumes data is already on the correct device.

Return type:: tuple[float, ndarray, ndarray]

Overview

The evaluation module provides functions for:

Model evaluation on test sets
Prediction generation
Accuracy calculation
Support for custom devices

Example usage

Basic evaluation:

from image_classification_tools.pytorch.evaluation import evaluate_model

# Evaluate model
test_accuracy, predictions, true_labels = evaluate_model(model, test_loader)

print(f"Test accuracy: {test_accuracy:.2f}%")

Per-class accuracy:

# Define your class names
class_names = ['class1', 'class2', 'class3', ...]

# Calculate per-class accuracy
class_correct = {name: 0 for name in class_names}
class_total = {name: 0 for name in class_names}

for pred, true in zip(predictions, true_labels):
    class_name = class_names[true]
    class_total[class_name] += 1
    if pred == true:
        class_correct[class_name] += 1

for name in class_names:
    acc = 100 * class_correct[name] / class_total[name]
    print(f'{name}: {acc:.2f}%')

With visualization:

from image_classification_tools.pytorch.plotting import plot_confusion_matrix
import matplotlib.pyplot as plt

test_accuracy, predictions, true_labels = evaluate_model(model, test_loader)

fig, ax = plot_confusion_matrix(true_labels, predictions, class_names)
plt.title(f'Test Accuracy: {test_accuracy:.2f}%')
plt.show()