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Framework for the evaluation of XAI algorithms (XAIEV)

Installation (work in progress)

  • clone the repo
  • pip install -e .
  • ask the authors for the dataset

Usage

General Notes on Paths

Many scripts and notebooks in this repo depend on paths. To ensure that the code runs on different machines (local development machines, HPC, etc) we use a .env file. This file is machine-specific and is expected to define the necessary paths in environment variables.

Example (see also .env-example):

# Note: This directory might contain several GB of (auto-generated) data
XAIEV_BASE_DIR="/home/username/xaiev/data"

This file is evaluated by utils.read_paths_from_dotenv(). Note: The package opencv-python has to be installed (see requirements.txt)

The expected path structure is as follows:

<BASE_DIR>                      specified in .env file
├── atsds_large/
│   ├── test/
│   │   ├── 0001/               class directory
│   │   │   ├── 000000.png      individual image of this class
│   │   │   └── ...             more images
│   │   └── ...                 more classes
│   └── train/
│       └── <class dirs with image files>
│
├── atsds_large_background/...  background images with same structure
│                               as in atsds_large (test/..., train/...)
│
├── atsds_large_mask/...        corresponding mask images with same structure
│                               as in atsds_large (test/..., train/...)
├── model_checkpoints/
│   ├── convnext_tiny_1_1.tar
│   ├── resnet50_1_1.tar
│   ├── simple_cnn_1_1.tar
│   └── vgg16_1_1.tar
│
├── XAI_evaluation
│   ├── simple_cnn/gradcam/test/    same structure as `XAI_results`
│   │   ├── revelation
│   │   └── occlusion
│   └── ...                     other XAI methods and models
│
└── XAI_results
    ├── simple_cnn/             cnn model directory
    │   ├── gradcam/            xai method
    │   │   ├── test/           split fraction (train/test)
    │   │   │   ├── mask/
    │   │   │   │   ├── 000000.png.npy
    │   │   │   │   └── ...
    │   │   │   ├── mask_on_image/
    │   │   │   │   ├── 000000.png
    │   │   │   │   └── ...
    │   …   …   …
    ├── vgg16/...
    ├── resnet50/..
    ├── convnext_tiny/..

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