MahoEmpire/yolo-table-detection
AI model by MahoEmpire
From the publisher
Model card & documentation
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中文 A single-class table detection model based on Ultralytics YOLO26s, designed to detect table regions in documents, reports, scanned pages, screenshots, and other images. Class definition: The base model was trained on 3,100 samples selected and merged from multiple publicly available table-related datasets. An additional 1,100 high-quality, accurately annotated samples were subsequently prepared for business-domain refinement and model fine-tuning. The best recorded result was obtained at Epoch 40. The following images are already generated prediction results produced by the trained model. The detected table bounding boxes are included in the images and are provided to demonstrate actual inference results. Install Ultralytics: Load the model and run inference: For a single image: For a single image: Because the model contains only one class, all detected objects are: The detection output includes: This model is designed for table region detection and does not directly perform: A table structure recognition or OCR model…
Read the full model card ↗ · Preview checked 2026-09-23T07:44:25.998Z
Inside the original model card — Document outline
- YOLO26s Table Detection Model
- Repository Structure
- Model Information
- Training Data
- Best Validation Result
- Prediction Results
- Test Result 01
- Test Result 02
- Usage
- Python
- Windows CMD
- Model Output
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Model overview
MahoEmpire/yolo-table-detection is a task-unspecified model repository published by MahoEmpire on Hugging Face. A library was not reported.
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Model facts
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- Recent downloads (30 days)
- 0
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- Hugging Face trending score
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- Architecture
- Not reported
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-09-23T06:28:46.000Z
- Last modified
- 2026-09-23T06:28:46.000Z
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