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v0.6.0 Heatmap visualizations via Grad-CAM++ and backend base class refactor
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@mbeissinger mbeissinger released this 17 Nov 19:28
· 4 commits to master since this release
v0.6.0
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Release 0.6.0

Breaking Changes

  • Refactored the ML backends into sub-folders:
    • TFModel class: backends/backend_tf.py -> backends/tf/backend.py
    • TFLiteModel class: backends/backend_tflite.py -> backends/tflite/backend.py
    • ONNXModel class: backends/backend_onnx.py -> backends/onnx/backend.py

Bug Fixes and Other Improvements

  • Added Backend and ImageBackend abstract base classes in backends/backend.py
  • Added ImageBackend classes for each ML backend:
    • TFImageModel class: backends/tf/image_backend.py
    • TFLiteImageModel class: backends/tflite/image_backend.py
    • ONNXImageModel class: backends/onnx/image_backend.py
  • Added Grad-CAM++ implementation (ImageBackend.gradcam_plusplus(image, label) -> np.ndarray) for visualizing
    convolutional neural network heatmaps for explaining why the model predicted a certain label.
    Note: Grad-CAM++ only implemented currently in TFImageModel for TensorFlow Lobe model exports.
    The visualization can be called from the top-level API of ImageModel -> ImageModel.visualize(image)
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