Pytorch implementation of LIME-CAM (LIME-based variant of CAM) and Grad-CAM

• Python 2.7
• Numpy
• pytorch 0.4.0
• torchvision 0.2.1
• opencv
• sklearn

python main.py --help

• --image_path: a path to an image (required)
• --result_path: a path to the explanation result (default: results/result_{method}_{class}.png)
• --model: a model name from torchvision.models, e.g., 'vgg16' (default: vgg16)
• --method: a method to generate the explanation e.g., 'limecam, 'gradcam' (required)
• --no_cuda: disables GPU usage

[1] R. R. Selvaraju, A. Das, R. Vedantam, M. Cogswell, D. Parikh, and D. Batra. "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization". arXiv, 2016
[2] M. T. Riberio, S. Singh, and C. Guestrin. ""Why should I Trust You?": Explaining the Predictions of Any Classifier". arXiv, 2016