# opendatalab/DocLayout-YOLO

DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception

Repository: https://github.com/opendatalab/DocLayout-YOLO
Canonical: https://ross.abutalabs.com/products/doclayout-yolo
Homepage: https://huggingface.co/spaces/opendatalab/DocLayout-YOLO
Language: Python
License: AGPL-3.0
License Family: copyleft
Last push: 2025-04-14T07:18:01+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 16, release rhythm 35, longevity 49
- inputs: {"age_days": 689, "days_push": 506, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2258, forks 171 (observed 2026-08-28T04:06:31.582332+00:00)

## What it is
DocLayout-YOLO is a real-time YOLO-v10-based model for detecting document layout elements (text blocks, tables, figures, etc.) in diverse documents, trained with a large synthetic dataset (DocSynth-300K). It ships as a pip-installable Python package with pretrained weights, inference scripts, and a dataset synthesis pipeline.

## Use cases
- detect layout of pdf documents
- extract tables and figures from scanned documents
- preprocess documents for ocr pipelines
- segment document pages into structural elements
- build a pdf-to-markdown extraction pipeline
- generate synthetic document layout training data

## When to choose
- you need fast, real-time document layout detection with pretrained models
- you want a pip-installable library integrated into document extraction workflows like PDF-Extract-Kit
- you need a pipeline to synthesize large-scale document layout datasets

## When to avoid
- you need full OCR text recognition rather than layout detection
- your project requires a permissive license since this is AGPL-3.0
- you need general-purpose object detection unrelated to documents

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, deep-learning, ocr, pdf, image-processing
- domain: computer-vision, pdf, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: document-layout-analysis, yolo, object-detection, pytorch, pdf-parsing, pretrained-models, gpu

## Member repositories
- opendatalab/DocLayout-YOLO (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:31.582332+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:43:39.269903+00:00, confidence not recorded.
  - readme: https://github.com/opendatalab/DocLayout-YOLO (fetched 2026-08-28T04:06:31.582332+00:00, sha 3ac1f50a008f)
  - homepage: https://huggingface.co/spaces/opendatalab/DocLayout-YOLO (fetched 2026-08-29T10:23:28.372265+00:00, sha 1ac89f197a31)
- Data as of 2026-08-30T08:39:29.467469+00:00.