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IBD: Interpretable Basis Decomposition for Visual Explanation
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README.md

IBD: Interpretable Basis Decomposition for Visual Explanation

Introduction

This repository contains the demo code for the ECCV'18 paper "Interpretable Basis Decomposition for Visual Explanation".

Download

  • Clone the code of Network Dissection Lite from github
    git clone https://github.com/CSAILVision/IBD
    cd IBD
  • Download the Broden dataset (~1GB space) and the example pretrained model. If you already download this, you can create a symbolic link to your original dataset.
    ./script/dlbroden.sh
    ./script/dlzoo.sh

Note that AlexNet models work with 227x227 image input, while VGG, ResNet, GoogLeNet works with 224x224 image input.

Requirements

  • Python Environments
    pip3 install numpy sklearn scipy scikit-image matplotlib easydict torch torchvision

Note: The repo was written by pytorch-0.3.1. (PyTorch, Torchvision)

Run IBD in PyTorch

  • You can configure settings.py to load your own model, or change the default parameters.

  • Run IBD

    python3 test.py

IBD Result

  • At the end of the dissection script, a HTML-formatted report will be generated inside result folder that summarizes the interpretable units of the tested network.

Train Concept Basis

  • If you want to train the concept basis, delete the pretrained files first.
    rm result/pytorch_resnet18_places365/snapshot/14.pth 
    rm result/pytorch_resnet18_places365/decompose.npy 

  • Run the train script.
    python3 train.py
  • Then run IBD.
    python3 test.py

Reference

If you find the codes useful, please cite this paper

@inproceedings{IBD2018,
  title={Interpretable Basis Decomposition for Visual Explanation},
  author={Zhou, Bolei* and Sun, Yiyou* and Bau, David* and Torralba, Antonio},
  booktitle={European Conference on Computer Vision},
  year={2018}
}
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