DocLayoutYOLO

Fast document layout detection.

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Overview

Tasks	Layout Analysis
Backends	PyTorch
Speed	0.1-0.2s/page
Quality	Good
VRAM	2-4GB

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Why DocLayoutYOLO

• Extremely fast - 5-10x faster than VLM-based detection
• Low memory - Runs on modest GPUs or CPU
• Reliable - YOLO architecture, battle-tested
• Fixed labels - 11 pre-trained categories

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Basic Usage

from omnidocs.tasks.layout_analysis import DocLayoutYOLO, DocLayoutYOLOConfig
from PIL import Image

image = Image.open("document.png")

detector = DocLayoutYOLO(
    config=DocLayoutYOLOConfig(device="cuda")
)

result = detector.extract(image)

for elem in result.elements:
    print(f"{elem.label}: {elem.bbox} ({elem.confidence:.2f})")

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Configuration

config = DocLayoutYOLOConfig(
    device="cuda",        # "cuda", "cpu"
    confidence=0.25,      # Detection threshold (0.0-1.0)
    img_size=1024,        # Input image size
)

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Detected Labels

Label	Description
title	Document/section headings
text	Body paragraphs
list	Bullet or numbered lists
table	Data tables
figure	Images, diagrams, charts
caption	Figure/table captions
formula	Math equations
footnote	Footnotes
page_header	Running headers
page_footer	Running footers
unknown	Unclassified elements

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Filtering Results

# By label
tables = [e for e in result.elements if e.label == "table"]
figures = [e for e in result.elements if e.label == "figure"]

# By confidence
confident = [e for e in result.elements if e.confidence >= 0.8]

# Exclude headers/footers
content = [e for e in result.elements
           if e.label not in ["page_header", "page_footer"]]

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When to Use DocLayoutYOLO vs Qwen Layout

Use Case	Model
Speed-critical	DocLayoutYOLO
Custom labels needed	Qwen Layout
Limited GPU memory	DocLayoutYOLO
Higher accuracy	Qwen Layout
Batch processing	DocLayoutYOLO

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Troubleshooting

Missing elements

# Lower confidence threshold
config = DocLayoutYOLOConfig(confidence=0.15)

Too many false detections

# Increase confidence threshold
config = DocLayoutYOLOConfig(confidence=0.5)

Slow on CPU

# Expected: ~1-2s/page on CPU vs 0.1-0.2s on GPU
# Consider using GPU if available