table transformer detection

Providermicrosoft
Categoryobject-detection
Licensemit
Downloads646
Stars0

Overview

Table Transformer (TATR) is a specialized object detection model designed to solve the complex problem of table structure recognition within unstructured documents. Unlike general-purpose detectors, it is optimized to identify table boundaries and internal cell layouts from images or PDFs, converting visual layouts into machine-readable formats. For developers building RAG pipelines or automated data extraction tools, this model serves as a critical preprocessing step to ensure tabular data is parsed accurately rather than treated as plain text. It integrates well into document AI workflows and offers a robust alternative to traditional rule-based OCR parsing by leveraging a Transformer-based architecture for higher spatial accuracy.

Highlights

  • Specialized in table boundary and structure detection
  • Optimized for document AI and automated data extraction
  • Permissive MIT license for commercial integration
  • Transformer-based architecture improves layout parsing accuracy

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("microsoft/table-transformer-detection")
tokenizer = AutoTokenizer.from_pretrained("microsoft/table-transformer-detection")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download microsoft/table-transformer-detection

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download microsoft/table-transformer-detection config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('microsoft/table-transformer-detection')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/microsoft/table-transformer-detection

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/microsoft/table-transformer-detection

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('microsoft/table-transformer-detection')
tokenizer = AutoTokenizer.from_pretrained('microsoft/table-transformer-detection')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model microsoft/table-transformer-detection

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model microsoft/table-transformer-detection README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('microsoft/table-transformer-detection')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/microsoft/table-transformer-detection.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/microsoft/table-transformer-detection.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'microsoft/table-transformer-detection')

Full Documentation

来源: HuggingFace

---
license: mit
widget:

  • src: https://www.invoicesimple.com/wp-content/uploads/2018/06/Sample-Invoice-printable.png

example_title: Invoice
---

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.

View official source
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