Kreuzberg/layout-models

By Kreuzberg · ↓ 0 · ♥ 0 · Updated 2026-03-16

What's in this repo

This page lists the files in Kreuzberg/layout-models, including model.onnx and more.

2 ONNX models (.onnx files)
4 files, including 2 .onnx, 1 .md
Last updated 2026-03-16

ONNX files

File	Size	Download
rtdetr/model.onnx	—	Download
tatr/model.onnx	—	Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

Kreuzberg Layout Models

ONNX models used by Kreuzberg for document layout detection and table structure recognition.

Models

RT-DETR (Document Layout Detection)

Property	Value
Path	`rtdetr/model.onnx`
Size	169 MB
Precision	FP32
Architecture	RT-DETR v2 (Real-Time Detection Transformer)
Input	`images`: `[batch, 3, 640, 640]` f32 (ImageNet-normalized, letterboxed)
Input	`orig_target_sizes`: `[batch, 2]` i64 (original `[height, width]`)
Outputs	`labels` i64, `boxes` f32 `[batch, N, 4]`, `scores` f32
Classes	17 document layout classes
SHA256	`3bf2fb0ee6df87435b7ae47f0f3930ec3dc97ec56fd824acc6d57bc7a6b89ef2`

Layout Classes: Caption, Footnote, Formula, ListItem, PageFooter, PageHeader, Picture, SectionHeader, Table, Text, Title, DocumentIndex, Code, CheckboxSelected, CheckboxUnselected, Form, KeyValueRegion

TATR (Table Structure Recognition)

Property	Value
Path	`tatr/model.onnx`
Size	29 MB
Precision	INT8 quantized
Architecture	DETR (DEtection TRansformer) — non-autoregressive object detection
Input	`pixel_values`: `[batch, 3, H, W]` f32 (variable size, typically 800×800)
Outputs	`logits` f32 `[batch, 125, 7]` (class probabilities), `pred_boxes` f32 `[batch, 125, 4]` (normalized cx/cy/w/h)
Classes	7 classes (see below)
SHA256	see release commit

Table Structure Classes: 0. table — entire table region

table column — column span
table row — row span
table column header — header row cells
table projected row header — projected row header
table spanning cell — cells spanning multiple rows/columns
no object — background

Attribution & Provenance

RT-DETR

This model is mirrored from docling-project/docling-layout-heron-onnx, created by the Docling team at IBM Research.

Original repository: docling-project/docling-layout-heron-onnx
License: Apache-2.0
Architecture paper: Zhao et al., "DETRs Beat YOLOs on Real-time Object Detection" (arXiv:2304.08069)
Training data: DocLayNet and internal IBM document datasets

TATR (Table Transformer)

This model is based on microsoft/table-transformer-structure-recognition by Microsoft Research. The ONNX conversion was produced by Xenova/table-transformer-structure-recognition using HuggingFace Optimum. Quantized to INT8 for inference efficiency.

Original repository: microsoft/table-transformer-structure-recognition
ONNX source: Xenova/table-transformer-structure-recognition
License: MIT
Architecture paper: Smock et al., "PubTables-1M: Towards comprehensive table extraction from unstructured documents" (arXiv:2110.00061)
Training data: PubTables-1M dataset
Quantization: INT8 (dynamic quantization via ONNX Runtime)

Usage

These models are automatically downloaded and cached by the Kreuzberg document extraction library. See the layout extraction documentation for details.

License

RT-DETR: Apache-2.0 License
TATR: MIT License

Model info

onnx document-layout-analysis table-structure-recognition kreuzberg arxiv:2304.08069 arxiv:2110.00061 license:apache-2.0 region:us

↓ 0 ♥ 0

Created: 2026-03-09

Updated: 2026-03-16

View on Hugging Face

Files (4)

.gitattributes

README.md

rtdetr/model.onnx ONNX

tatr/model.onnx ONNX

Related models

More models

No task type declared.