arm-on/interpretable-image-classification
Interpretability methods applied on image classifiers trained on MNIST and CIFAR10
This project helps machine learning researchers and practitioners understand why an image classification model makes a particular decision. It takes pre-trained image classifiers and visualizes which parts of an input image (like a handwritten digit or an animal photo) are most important for the model's prediction. The output helps users interpret the 'reasoning' behind the classification.
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Use this if you need to evaluate and compare different interpretability techniques for deep learning image classifiers, especially for digit or object recognition tasks.
Not ideal if you are working with non-image data, require interpretability for models other than deep neural networks, or need a production-ready interpretability library.
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Oct 17, 2022
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