This repository hosts three YOLOv26 models (nano, small, and medium) fine-tuned for high-performance Document Layout Analysis on the challenging DocLayNet v1.2 dataset.

The goal is to accurately detect and classify key layout elements in a document, such as text, tables, figures, and titles. This is a fundamental task for document understanding and information extraction pipelines.

✨ Model Highlights

🚀 Three Powerful Variants: Choose between nano, small, and medium models to fit your performance needs.
🎯 High Accuracy: Trained on the comprehensive DocLayNet v1.2 dataset to recognize 11 distinct layout types.
⚡ Optimized for Efficiency: The recommended yolo26n (nano) model offers an exceptional balance of speed and accuracy, making it ideal for production environments.

🚀 Get Started

Get up and running with just a few lines of code.

1. Installation

First, install the necessary libraries.

pip install ultralytics huggingface_hub

2. Inference Example

This Python snippet shows how to download a model from the Hub and run inference on a local document image.

python

from pathlib import Path
from huggingface_hub import hf_hub_download
from ultralytics import YOLO

# Define the local directory to save models
DOWNLOAD_PATH = Path("./models")
DOWNLOAD_PATH.mkdir(exist_ok=True)

# Choose which model to use
# 0: nano, 1: small, 2: medium
model_files = [
    "yolo26n_doc_layout.pt",
    "yolo26s_doc_layout.pt",
    "yolo26m_doc_layout.pt",
]
selected_model_file = model_files[0] # Using the recommended nano model

# Download the model from the Hugging Face Hub
model_path = hf_hub_download(
    repo_id="Armaggheddon/yolo26-document-layout",
    filename=selected_model_file,
    repo_type="model",
    local_dir=DOWNLOAD_PATH,
)

# Initialize the YOLO model
model = YOLO(model_path)

# Run inference on an image
# Replace 'path/to/your/document.jpg' with your file
results = model('path/to/your/document.jpg')

# Process and display results
results[0].print()  # Print detection details
results[0].show()   # Display the image with bounding boxes

📊 Model Performance & Evaluation

We fine-tuned three YOLOv26 variants, allowing you to choose the best model for your use case.

yolo26n_doc_layout.pt: Recommended. The nano model offers the best trade-off between speed and accuracy.
yolo26s_doc_layout.pt: A larger, slightly more accurate model.
yolo26m_doc_layout.pt: The largest model, providing the highest accuracy with a corresponding increase in computational cost.

As shown in the analysis below, performance gains are marginal when moving from the small to the medium model, making the nano and small variants the most practical choices.

Nano vs. Small vs. Medium Comparison

Here's how the three models stack up across key metrics. The plots compare their performance for each document layout label.

mAP@50-95 (Strict IoU)	mAP@50 (Standard IoU)
mAP@50-95	mAP@50

Precision (Box Quality)	Recall (Detection Coverage)
Precision	Recall

Click to see detailed Training Metrics & Confusion Matrices

Model	Training Metrics	Normalized Confusion Matrix
`yolo26n`	yolo26n results	yolo26n confusion matrix
`yolo26s`	yolo26s results	yolo26s confusion matrix
`yolo26m`	yolo26m results	yolo26m confusion matrix

📚 About the Dataset: DocLayNet

The models were trained on the DocLayNet v1.2 dataset, which provides a rich and diverse collection of document images annotated with 11 layout categories:

Text, Title, Section-header
Table, Picture, Caption
List-item, Formula
Page-header, Page-footer, Footnote

Training Resolution: All models were trained at 1280x1280 resolution. Initial tests at the default 640x640 resulted in a significant performance drop, especially for smaller elements like footnote and caption.

Yolo26 🆚 Yolo11

Comparing the new YOLOv26 models to the previous YOLOv11 baseline, we see significant improvements across all metrics, particularly in mAP@50-95 and recall. The nano model alone outperforms the yolo11m model, demonstrating the effectiveness of the YOLOv26 architecture for document layout analysis.

💻 Code & Training Details

This model card focuses on results and usage. For the complete end-to-end pipeline, including training scripts, dataset conversion utilities, and detailed examples, please visit the main GitHub repository:

➡️ GitHub Repo: yolo_doc_layout