Transformer-MM-Explainability
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
Visualize and interpret how attention works in Transformer-based models using provided Jupyter notebooks.
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Free · no card · unsubscribe anytime[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
Transformer-MM-Explainability has 911 stars on GitHub. It has been forked 116 times. Transformer-MM-Explainability is written mainly in Jupyter Notebook. It has been in active development since 2021. Transformer-MM-Explainability is available under the MIT license. Its main topics are clip, detr, explainability, explainable-ai.
What is Transformer-MM-Explainability?
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
Is Transformer-MM-Explainability open source?
Transformer-MM-Explainability is an open-source project. It is released under the MIT license.
Is Transformer-MM-Explainability free?
Yes. Transformer-MM-Explainability is free and open source — you can use, modify and self-host it.
What license does Transformer-MM-Explainability use?
Transformer-MM-Explainability is available under the MIT license.
What language is Transformer-MM-Explainability written in?
Transformer-MM-Explainability is written mainly in Jupyter Notebook.
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