Table Transformer (fine-tuned for Table Detection)

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.

Usage

You can use the raw model for detecting tables in documents. See the documentation for more info.

microsoft/table-transformer-detection powered by Hugging Face Inference Toolkit

Send Request

You can use cURL or any REST Client to send a request to the AzureML endpoint with your AzureML token.

curl <AZUREML_ENDPOINT_URL> \
    -X POST \
    -H "Authorization: Bearer <AZUREML_TOKEN>" \
    -H "Content-Type: image/jpeg" \
    --data-binary @"image.jpg"

Supported Parameters

inputs (string): The input image data as a base64-encoded string. If no parameters are provided, you can also provide the image data as a raw bytes payload.
parameters (object):
- threshold (float): The probability necessary to make a prediction.

Check the full API Specification at the Hugging Face Inference documentation .

Model Specifications

LicenseMit

Last UpdatedJuly 2025

ProviderHuggingFace

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