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IFeaLiD

Interactive Feature Localization in Deep neural networks (IFeaLiD) is a web application that allows you to visualize and explore deep neural network layers or any hyperspectral image interactively in the browser. Read the paper.

IFeaLiD is available at ifealid.cebitec.uni-bielefeld.de.

Examples

Datasets

These are the examples that were presented in the paper:

Cityscapes Dataset (bielefeld_000000_007186_leftImg8bit.png)

COCO Dataset (000000015746.jpg)

DIV2K Dataset (0804.png)

DOTA Dataset (P0034.png)

Feature Extraction

The code that was used to extract the feature maps from ResNet101 for the examples is provided in scripts/feature-extraction. Usage:

  1. Load the submodule of the Mask R-CNN repository: git submodule update --init

  2. Install the requirements: pip3 install -r scripts/feature-extraction/requirements.txt

  3. Execute the script: python3 scripts/feature-extraction/resnet_feature_extraction.py <target directory> <image1> <image2> ...

By default, the script extracts feature maps at five stages of ResNet101. The feature maps conv2_x, conv3_x and conv4_x correspond to the files with suffix C1.npz, C2.npz and C3.npz, respectively.

Generating a Dataset

The script to generate an IFeaLiD dataset ZIP file from a NumPy array can be found in scripts/zip-creator. Usage:

  1. Install the requirements: pip3 install -r scripts/zip-creator/requirements.txt

  2. Generate the feature map as NumPy array and store it as a file (see above)

  3. Execute the script: python3 scripts/zip-creator/dataset-zip-creator.py <file>

The script supports the following options:

  • -n, --name: Optional dataset name. Default is the filename of the NumPy file.
  • -p, --precision: Dataset numeric precision in bits (8, 16 or 32). Default is 8.
  • -o, --overlay: Optional path to the original input image. If supplied, the input image is displayed in IFeaLiD and can be blended with the heat map visualization.

Installation

Clone this repository.

Developing

  1. Run npm install
  2. Run npm run dev
  3. Open the URL shown in the terminal

Production

  1. Run npm install
  2. Update base in vite.config.js. If you are deploying to https://example.com/<DIR>/, then set base to '/<DIR>/'. If you are not deploying to a subdirectory, then set base to '/'.
  3. Run npm run build
  4. Expose the contents of the dist directory to a web server.

Citation

Please cite our paper if it is helpful to your work:

@article{zurowietz2020interactive,
  title={An Interactive Visualization for Feature Localization in Deep Neural Networks},
  author={Zurowietz, Martin and Nattkemper, Tim Wilhelm},
  journal={Frontiers in Artificial Intelligence-Machine Learning and Artificial Intelligence},
  year={2020},
  doi={10.3389/frai.2020.00049}
}

References

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Interactive Feature Localization in Deep neural networks

ifealid.cebitec.uni-bielefeld.de/

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