Cnn Explainer

Learning Convolutional Neural Networks with Interactive Visualization.
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Readme

CNN Explainer

An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs)

build arxiv badge DOI:10.1109/TVCG.2020.3030418

For more information, check out our manuscript:

CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. Wang, Zijie J., Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, and Duen Horng Chau. IEEE Transactions on Visualization and Computer Graphics (TVCG), 2020.

Live Demo

For a live demo, visit: http://poloclub.github.io/cnn-explainer/

Running Locally

Clone or download this repository:

git clone [email protected]:poloclub/cnn-explainer.git

# use degit if you don't want to download commit histories
degit poloclub/cnn-explainer

Install the dependencies:

npm install

Then run CNN Explainer:

npm run dev

Navigate to localhost:3000. You should see CNN Explainer running in your broswer :)

To see how we trained the CNN, visit the directory ./tiny-vgg/. If you want to use CNN Explainer with your own CNN model or image classes, see #8 and #14.

Credits

CNN Explainer was created by Jay Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, and Polo Chau, which was the result of a research collaboration between Georgia Tech and Oregon State.

We thank Anmol Chhabria, Kaan Sancak, Kantwon Rogers, and the Georgia Tech Visualization Lab for their support and constructive feedback.

Citation

@article{wangCNNExplainerLearning2020,
  title = {{{CNN Explainer}}: {{Learning Convolutional Neural Networks}} with {{Interactive Visualization}}},
  shorttitle = {{{CNN Explainer}}},
  author = {Wang, Zijie J. and Turko, Robert and Shaikh, Omar and Park, Haekyu and Das, Nilaksh and Hohman, Fred and Kahng, Minsuk and Chau, Duen Horng},
  journal={IEEE Transactions on Visualization and Computer Graphics (TVCG)},
  year={2020},
  publisher={IEEE}
}

License

The software is available under the MIT License.

Contact

If you have any questions, feel free to open an issue or contact Jay Wang.

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