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Table Transformer (fine-tuned for Table Structure Recognition)

Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Smock et al. and first released in this repository.

Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

The Table Transformer is equivalent to DETR, a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.


You can use the raw model for detecting the structure (like rows, columns) in tables. See the documentation for more info.

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28.8M params
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Inference API
This model can be loaded on Inference API (serverless).

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