Vision Explanation Methods is an open-source package that implements D-RISE (Detector Randomized Input Sampling for Explanation) towards visual interpretations of object detection models. D-RISE is a black-boxed, or model-agnostic, explainability method which can produce saliency maps for any object detection or instance segmentation models provided these models are appropriately wrapped. In essence, D-RISE works by randomly masking the input images and isolating the parts that are most pertinent for the detection or segmentation of the object in question.
(Diagram from Petsiuk et al. 2020)
To install the vision explanation package, run:
pip install vision-explanation-methods
The process of fine-tuning an object detection model and visualizing it through D-RISE is illustrated in this colab notebook.
To generate saliency maps, import the package and run:
res = DRISE_runner.get_drise_saliency_map(
imagelocation: str,
model: Optional[object],
numclasses: int,
savename: str,
nummasks: int=25,
maskres: Tuple[int, int]=(4,4),
maskpadding: Optional[int]=None,
devicechoice: Optional[str]=None,
wrapperchoice: Optional[object] = PytorchFasterRCNNWrapper
)
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