xailib.xailib_image

Image data explainability classes for XAI-Lib.

This module provides base classes for explaining predictions on image data. It extends the base Explainer and Explanation classes with image-specific functionality.

Image explanations typically highlight which regions of an image contributed most to a model’s prediction, using techniques such as:

  • Saliency maps and heatmaps

  • Superpixel importance

  • Activation visualizations

Classes:

ImageExplainer: Base class for image data explainers. ImageExplanation: Base class for image data explanations.

Example

Using GradCAM for image explanation:

from xailib.explainers.gradcam_explainer import GradCAMImageExplainer
from xailib.models.pytorch_classifier_wrapper import pytorch_classifier_wrapper

# Wrap your model
bb = pytorch_classifier_wrapper(your_pytorch_model)

# Create and fit explainer
explainer = GradCAMImageExplainer(bb)
explainer.fit(target_layers=[model.layer4])

# Generate explanation
heatmap = explainer.explain(image, class_index)

See also

xailib.explainers.gradcam_explainer: GradCAM implementation for image data. xailib.explainers.lime_explainer: LIME implementation for image data. xailib.explainers.rise_explainer: RISE implementation for image data. xailib.explainers.intgrad_explainer: Integrated Gradients for image data.

Classes

ImageExplainer()

Abstract base class for image data explainers.

ImageExplanation()

Abstract base class for image data explanations.