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 spantable row — row spantable column header — header row cellstable projected row header — projected row headertable spanning cell — cells spanning multiple rows/columnsno 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
