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.
Run as ONNX (CPU / NPU / GPU)
Detect tables ~14× faster on a Windows NPU at half the model size, with mAP within 1% of the original PyTorch checkpoint — by exporting this model to ONNX. You can also export to ONNX to run on CPU or GPU.
Benchmarked on an Intel Core Ultra 7 258V (PubTables-1M validation, 1000 samples):
| Model | Device | Precision | mAP | mean latency (ms) | p50 latency (ms) | Size (MB) |
|---|---|---|---|---|---|---|
| PyTorch | CPU | fp32 | 0.9887 | 620.9 | 600.3 | 115 |
| ONNX | OpenVINO NPU | w8a16 (QDQ) | 0.9822 | 44.1 | 41.6 | 58 |
- How to convert — Export and quantize with Microsoft's WinML CLI. The NPU build is QDQ-quantized to w8a16; fp32 builds for CPU and GPU are also supported. End-to-end build, evaluation, and a Python inference example: examples/microsoft-table-transformer-detection.
- How to run on Windows — Use Windows ML, which manages execution providers for NPU / GPU / CPU and routes ONNX inference to the right backend automatically.
- How to run on other platforms — Use ONNX Runtime with the execution provider of your choice (OpenVINO, QNN, DirectML, CUDA, CPU, etc.).