Layout Heron GGUF
Document layout analysis model for CrispEmbed. Detects 17 region types in document images.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 43,1–161 MB
- Versão
- vRevision 5feaea8
- Atualizado
- 1 de nov. de 2026
- Downloads
- 245
- Fonte
- Hugging Face
Descrição da fonte
RT-DETRv2 Document Layout Detection (GGUF)
Document layout analysis model for CrispEmbed. Detects 17 region types in document images.
Architecture
- Backbone: ResNet-50-D (BN-folded Conv2d)
- Encoder: HybridEncoder (AIFI self-attention + FPN/PAN with CSP-RepVGG)
- Decoder: 6-layer transformer with deformable multi-scale cross-attention (300 queries)
- Classes: 17 (text, title, table, figure, formula, caption, section_header, list_item, footnote, page_header, page_footer, code, document_index, checkbox_selected, checkbox_unselected, form, key_value_region)
- Parameters: 42M
- Source: docling-project/docling-layout-heron (Apache-2.0)
Variants
| File | Size | Format | Notes |
|---|---|---|---|
| layout-heron-f32.gguf | 161 MB | F32 | Full precision, development |
| layout-heron-q8_0.gguf | 43 MB | Q8_0 | Recommended for inference |
Usage
# CLI
./build/crispembed -m layout-heron --layout document.png --json
# Server
./build/crispembed-server --layout layout-heron-q8_0.gguf
curl -X POST http://localhost:8080/layout/detect -d '{"image": "page.png"}'
from crispembed import CrispLayout
layout = CrispLayout("layout-heron-q8_0.gguf")
regions = layout.detect("document.png")
Parity
- Encoder: all stages cos=1.0 vs HF reference (with exact input)
- Detection score: 0.934 (HF reference: 0.955)
- 14 parity bugs found and fixed via systematic layer-by-layer diff
License
Apache-2.0 (same as upstream docling-layout-heron).
Provenance and EU AI Act Art. 53 note
- Upstream model: docling-project/docling-layout-heron — published by
docling-project. - Upstream licence:
apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not. - What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
- Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
- Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.
Fontes
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Fontes
1 fonteVerificado 1 de nov.
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Artefatos de modelo
4 artefatos
Artefatos de modelo
4 artefatoslayout-heron-q8_0.gguf
gguf · Q8_0 (unverified) · 43,1 MB · SHA-256 90c6905067ae…4bcd · Hugging Face
Download fileTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- license: apache-2.0 tags: - layout-detection - document-ai - rt-detr - gguf - crispembed pipeline_tag: object-detection --- # RT-DETRv2 Document Layout Detection (GGUF) Document layout analysis model for CrispEmbed. Detects 17 region types in document images. ## Architecture - **Backbone**: ResNet-50-D (BN-folded Conv2d) - **Encoder**: HybridEncoder (AIFI self-attention + FPN/PAN with CSP-RepVGG) - **Decoder**: 6-layer transformer with deformable multi-scale cross-attention (300 queries) - **Classes**: 17 (text, title, table, figure, formula, caption, section_header, list_item, footnote, page_header, page_footer, code, document_index, checkbox_selected, checkbox_unselected, form, key_value_region) - **Parameters**: 42M - **Source**: [docling-project/docling-layout-heron](https://huggingface.co/docling-project/docling-layout-heron) (Apache-2.0) ## Variants | File | Size | Format | Notes | |------|------|--------|-------| | layout-heron-f32.gguf | 161 MB | F32 | Full precision, development | | layout-heron-q8_0.gguf | 43 MB | Q8_0 | Recommended for inference | ## Usage ```bash # CLI ./build/crispembed -m layout-heron --layout document.png --json # Server ./build/crispembed-server --layout layout-heron-q8_0.gguf curl -X POST http://localhost:8080/layout/detect -d '{"image": "page.png"}' ``` ```python from crispembed import CrispLayout layout = CrispLayout("layout-heron-q8_0.gguf") regions = layout.detect("document.png") ``` ## Parity - Encoder: all stages cos=1.0 vs HF reference (with exact input) - Detection score: 0.934 (HF reference: 0.955) - 14 parity bugs found and fixed via systematic layer-by-layer diff ## License Apache-2.0 (same as upstream docling-layout-heron). ## Provenance and EU AI Act Art. 53 note - **Upstream model:** [docling-project/docling-layout-heron](https://huggingface.co/docling-project/docling-layout-heron) — published by `docling-project`. - **Upstream licence:** `apache-2.0`. This repository redistributes under the same terms; it grants no rights the upstream licence does not. - **What was done here:** format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs. - **Training data:** documented — where it is documented at all — by the upstream provid...
Source context: 245 downloads · 0 likes · Pipeline object-detection · Repo cstr/layout-heron-GGUF
Source context: 643 downloads · 0 likes · Pipeline object-detection · Repo cstr/layout-heron-GGUF
