Example of how to use MATLAB to produce post-hoc explanations (using Grad-CAM) for image classification tasks.
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Explainable AI for Image Classification
This repository shows an example of how to use MATLAB to produce post-hoc explanations -- using Grad-CAM -- for two image classification tasks.
Requirements
- MATLAB 2022b or later
- Deep Learning Toolbox
- Deep Learning Toolbox™ Model for GoogLeNet Network support package
- Parallel Computing Toolbox (only required for training using a GPU)
Suggested steps
- Download or clone the repository.
- Open MATLAB.
- Run the
example.mlxscript and inspect results.
Additional remarks
- You are encouraged to expand and adapt the example to your needs.
- The choice of pretrained network and hyperparameters (learning rate, mini-batch size, number of epochs, etc.) is merely illustrative.
- You are encouraged to (use Experiment Manager app to) tweak those choices and find a better solution.
Cite As
Oge Marques (2026). Explainable AI for Image Classification (https://github.com/ogemarques/xai-image-classification/releases/tag/1.0.2), GitHub. Retrieved .
General Information
- Version 1.0.2 (46.7 MB)
-
View License on GitHub
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.2 |
To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.
