xaitoolkit 0.1.1

A lightweight toolkit to explain PyTorch vision models with gradient and perturbation-based XAI.

XAIToolkit — Explain any PyTorch CNN in minutes

A lightweight, batteries-included toolkit to explain image-classification models (your own CNN, torchvision models, or timm models) using:

• Gradient-based XAI: Saliency, SmoothGrad, Integrated Gradients
• Model-agnostic XAI: RISE, Occlusion
• Local surrogate XAI: LIME-Stratified (superpixels) (stable neighborhood sampling)
• Region / tree-style XAI: Axis-aligned SHAP-like attributions (rectangle partitioning)

This repo starts with a teaching-first notebook and also ships as a small Python package + CLI.

Quick start

1) Install (editable for development)

pip install -e .

2) Run the notebook

Open:

• notebooks/01_grad_based_xai.ipynb

It loads a CNN, predicts Top-5 classes, and visualizes three gradient-based explainers in a 2×3 figure.

3) Explain an image from the CLI

Torchvision model:

xai-explain --image assets/cat_dog.jpg --model tv:resnet50 --methods saliency smoothgrad ig --outdir outputs

timm model (pretrained):

xai-explain --image assets/flamingo.jpg --model timm:swin_tiny_patch4_window7_224 --methods rise lime_strat shap_axis --outdir outputs

Your checkpoint + architecture:

xai-explain --image assets/cat_dog.jpg --model ckpt:checkpoints/best.pt --arch tv:resnet50 --methods rise occlusion --outdir outputs

What gets saved

For each method:

• outputs/<method>_heatmap.png
• outputs/<method>_overlay.png

Plus outputs/original.png.

Methods

Gradient-based

• saliency: ∂logit/∂input (absolute, channel-mean)
• smoothgrad: noise-averaged saliency
• ig: Integrated Gradients (manual implementation, no Captum)

Model-agnostic

• rise: Randomized Input Sampling for Explanation (RISE)
• occlusion: Sliding-window occlusion sensitivity

Surrogate / region-based

• lime_strat: LIME Image using stratified sampling of the neighborhood (bins on model output)
• shap_axis: Axis-aligned SHAP-like attributions using hierarchical rectangle splits

Each method file includes references and canonical links at the top.

Bring your own model

You can load models in four ways:

• tv:<name> — torchvision, e.g. tv:resnet50
• timm:<name> — timm pretrained models
• ckpt:<path> + --arch tv:<name>|timm:<name> — load checkpoint into a known architecture
• py:<file.py>:<factory_fn> — load a custom model factory that returns torch.nn.Module

Project layout

• src/xaitoolkit/ — package code
• scripts/ — small wrappers (CLI lives here)
• notebooks/ — teaching notebook(s)
• assets/ — demo images
• outputs/ — generated artifacts (gitignored)

Citation & credits

• ResNet: https://arxiv.org/abs/1512.03385
• Integrated Gradients: https://arxiv.org/abs/1703.01365
• SmoothGrad: https://arxiv.org/abs/1706.03825
• Grad-CAM: https://arxiv.org/abs/1610.02391
• RISE: https://arxiv.org/abs/1806.07421