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Surya Layout2

A lightweight document layout detection model used by Surya. It detects layout regions (text, tables, figures, headers, captions, equations, etc.) on a page image and runs on CPU or GPU.

TypeOtherVersionRevision 0aee81dBClasse

Source du modèle

Type
Other
Taille du fichier
6,74–129 MB
Version
vRevision 0aee81d
Mis à jour
21 oct. 2026
Téléchargements
15 k
Source
Hugging Face

Description de la source

Surya Layout (fast)

A lightweight document layout detection model used by Surya. It detects layout regions (text, tables, figures, headers, captions, equations, etc.) on a page image and runs on CPU or GPU.

This is the "fast" layout detector — a compact object detector that serves as a drop-in alternative to Surya's VLM-based layout model.

➡️ Documentation, installation, and everything else lives in the Surya repository.

Usage

Install Surya:

pip install surya-ocr

Point the fast layout predictor at this checkpoint:

from PIL import Image
from surya.fast_layout import FastLayoutPredictor

predictor = FastLayoutPredictor(checkpoint="hf://datalab-to/surya_layout2")
layout = predictor([Image.open("page.png")])

for box in layout[0].bboxes:
    print(box.label, box.bbox, box.position)  # region label, [x0,y0,x1,y1], reading-order index

Or make it the default so the CLI and library use it without an explicit path:

export FAST_LAYOUT_MODEL_CHECKPOINT="hf://datalab-to/surya_layout2"

License

Released under the AI Pubs OpenRAIL-M license (see LICENSE) — the same license as the surya-ocr-2 model weights.

Sources

1 source
Hugging FacePrincipalVersion 0aee81d5fd9275c0582e545bf3a56944b1e75679
huggingface.co/datalab-to/surya_layout2/tree/0aee81d5fd9275c0582e545bf3a56944b1e75679

Vérifié 21 oct.

Artefacts du modèle

2 artefacts

order/order_ar.pt

pt · 6,74 MB · SHA-256 f381c3854801…3ad8 · Hugging Face

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rfdetr_layout.pth

pth · 129 MB · SHA-256 e01b79f85877…9fe7 · Hugging Face

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Extraits de sources

3 extraits

--- license: openrail license_link: LICENSE tags: - object-detection - document-layout-analysis - ocr library_name: surya --- # Surya Layout (fast) A lightweight document **layout detection** model used by [Surya](https://github.com/datalab-to/surya). It detects layout regions (text, tables, figures, headers, captions, equations, etc.) on a page image and runs on CPU or GPU. This is the "fast" layout detector — a compact object detector that serves as a drop-in alternative to Surya's VLM-based layout model. ➡️ **Documentation, installation, and everything else lives in the [Surya repository](https://github.com/datalab-to/surya).** ## Usage Install Surya: ```shell pip install surya-ocr ``` Point the fast layout predictor at this checkpoint: ```python from PIL import Image from surya.fast_layout import FastLayoutPredictor predictor = FastLayoutPredictor(checkpoint="hf://datalab-to/surya_layout2") layout = predictor([Image.open("page.png")]) for box in layout[0].bboxes: print(box.label, box.bbox, box.position) # region label, [x0,y0,x1,y1], reading-order index ``` Or make it the default so the CLI and library use it without an explicit path: ```shell export FAST_LAYOUT_MODEL_CHECKPOINT="hf://datalab-to/surya_layout2" ``` ## License Released under the AI Pubs OpenRAIL-M license (see `LICENSE`) — the same license as the [surya-ocr-2](https://huggingface.co/datalab-to/surya-ocr-2) model weights.

Source context: 14963 downloads · 8 likes · Pipeline object-detection · Library surya · Repo datalab-to/surya_layout2

Source context: 9529 downloads · 4 likes · Pipeline object-detection · Library surya · Repo datalab-to/surya_layout2