Armaggheddon/yolo26-document-layout

object-detection model by Armaggheddon

Older snapshot: check the original source for current information.

From the publisher

Model card & documentation

Source preview

Read the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.

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. Get up and running with just a few lines of code. First, install the necessary libraries. This Python snippet shows how to download a model from the Hub and run inference on a local document image. We fine-tuned three YOLOv26 variants, allowing you to choose the best model for your use case. 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. Here's how the three models stack up across key metrics. The plots compare their…

Armaggheddon/yolo26-document-layout on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-10-28T01:09:49.078Z

Inside the original model card — Document outline

Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.

Links from the model card

References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.

Documentation belongs to its respective authors. Reported project/model license: mit. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.

Model overview

Armaggheddon/yolo26-document-layout is a object-detection model repository published by Armaggheddon on Hugging Face. The source reports the ultralytics library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task: object-detection
Library: ultralytics
Recent downloads (30 days): 1,373
Cumulative likes: 10
Hugging Face trending score: 0
Reported safetensors parameters: Not reported
Architecture: Not reported
License: mit
Access: Not gated by Hugging Face
Created: 2026-03-15T10:19:23.000Z
Last modified: 2026-03-15T16:01:26.000Z

Compatibility and lineage

Reported languages: en

Reported base models: Ultralytics/YOLO26

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

Use and evaluate

Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.

No model weights are downloaded or executed by AltAPIs. Never enable remote model code without reviewing it.

Model card and usage instructions

Model files and configuration

Community discussion

Source and freshness

Source: Hugging Face Hub. Metadata observed 2026-09-23T06:38:30.488Z. Daily imports are snapshots, not real-time monitoring.

Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.

Open original source

Related models

mudler/locate-anything.cpp-gguf

microsoft/table-transformer-structure-recognition

PekingU/rtdetr_r101vd_coco_o365

PaddlePaddle/PP-DocLayoutV3_safetensors

hustvl/yolos-small

microsoft/table-transformer-detection