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  • Topical overviews
  • Tabular examples
  • Text examples
  • Image examples
    • Image classification
      • Explain PyTorch MobileNetV2 using the Partition explainer
      • Explain ResNet50 using the Partition explainer
      • Explain an Intermediate Layer of VGG16 on ImageNet
      • Explain an Intermediate Layer of VGG16 on ImageNet (PyTorch)
      • Front Page DeepExplainer MNIST Example
      • Explain ResNet50 ImageNet classification using Partition explainer
      • Multi-class ResNet50 on ImageNet (TensorFlow)
      • Multi-class ResNet50 on ImageNet (TensorFlow)
      • Multi-input Gradient Explainer MNIST Example
      • PyTorch Deep Explainer MNIST example
    • Image captioning
      • Explaining Image Captioning (Image to Text) using Azure Cognitive Services and Partition Explainer
      • Explaining Image Captioning (Image to Text) using Open Source Image Captioning Model and Partition Explainer
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Image examples

These examples explain machine learning models applied to image data. They are all generated from Jupyter notebooks available on GitHub.

Image classification

Examples using shap.explainers.Partition to explain image classifiers.

  • Explain PyTorch MobileNetV2 using the Partition explainer
  • Explain ResNet50 using the Partition explainer
  • Explain an Intermediate Layer of VGG16 on ImageNet
  • Explain an Intermediate Layer of VGG16 on ImageNet (PyTorch)
  • Front Page DeepExplainer MNIST Example
  • Explain ResNet50 ImageNet classification using Partition explainer
  • Multi-class ResNet50 on ImageNet (TensorFlow)
  • Multi-class ResNet50 on ImageNet (TensorFlow)
  • Multi-input Gradient Explainer MNIST Example
  • PyTorch Deep Explainer MNIST example

Image captioning

Examples using shap.explainers.Permutation to produce explanations in a model agnostic manner.

  • Explaining Image Captioning (Image to Text) using Azure Cognitive Services and Partition Explainer
  • Explaining Image Captioning (Image to Text) using Open Source Image Captioning Model and Partition Explainer
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