Shypd.aiThe largest open index of AI toolsTransformer-MM-Explainability is an open-source research tool that provides a PyTorch implementation for visualizing and interpreting Transformer-based networks. It offers a novel method to explain attention models in bi-modal and encoder-decoder transformers, including examples for DETR and VQA.
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Transformer-MM-Explainability is an official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers. This open-source project offers a novel method to visualize and understand the decision-making processes of any Transformer-based network. It includes practical examples for popular models such as DETR, VQA, CLIP, and LXMERT, making it a valuable resource for researchers and developers working with multi-modal and encoder-decoder architectures. The tool provides notebooks for easy experimentation and reproduction of results, with clear instructions for setting up environments and running examples on GPUs, including Colab support.
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