/integrated-gradient-pytorch

This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.

Primary LanguagePythonMIT LicenseMIT

Integrated Gradients

MIT License
This is the pytorch implementation of "Axiomatic Attribution for Deep Networks". The original tensorflow version could be found here.

Acknowledgement

Requirements

  • python-3.5.2
  • pytorch-0.4.1
  • opencv-python

TODO List

  • add more functions as the original code.
  • finetune the results, make them close to the original paper.

Instructions

Highly recommend to use GPU to accelerate the computation. If you use CPU, I will recommend to select some small networks, such as resnet18. You also need to put your images under examples/.

Lists of networks that support (of course, you can add any networks by yourself)

  • inception
  • resnet18
  • resnet152
  • vgg19

Run the code

python main.py --cuda --model-type='inception' --img='01.jpg'

Results

Results are slightly different from the original paper, it may have some bugs or need to do some adjustments. I will keep updating it, any contributions are welcome!

Inception-v3

inception

ResNet-18

resnet18

ResNet-152

resnet152

VGG-19

vgg19