Armaggheddon/yolo11-document-layout

object-detection model by Armaggheddon

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This repository hosts three YOLOv11 models (nano, small, and medium) fine-tuned for high-performance Document Layout Analysis on the challenging DocLayNet 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 YOLOv11 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 performance…

Armaggheddon/yolo11-document-layout on Hugging Face A short preview, not the full document.

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Model overview

Armaggheddon/yolo11-document-layout is a object-detection model repository published by Armaggheddon on Hugging Face. The source reports the ultralytics library.

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Model facts

Task: object-detection
Library: ultralytics
Recent downloads (30 days): 1,516
Cumulative likes: 17
Hugging Face trending score: 0
Reported safetensors parameters: Not reported
Architecture: Not reported
License: mit
Access: Not gated by Hugging Face
Created: 2025-09-28T14:03:39.000Z
Last modified: 2026-03-17T20:29:46.000Z

Compatibility and lineage

Reported languages: en

Reported base models: Ultralytics/YOLO11

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.

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Source: Hugging Face Hub. Metadata observed 2026-11-08T06:59:29.904Z. Daily imports are snapshots, not real-time monitoring.

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