Object Detection
ultralytics
English
German
yolo
yolo11
invoice
document-layout
document-understanding
ocr-prep
invoice-extraction
Eval Results (legacy)
Instructions to use AvoCahDoe/invoice-layout-yolo11m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use AvoCahDoe/invoice-layout-yolo11m with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("AvoCahDoe/invoice-layout-yolo11m") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 580 Bytes
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license: apache-2.0
tags:
- object-detection
- yolo
- invoice
- document-layout
library_name: ultralytics
---
# Invoice Layout — yolo11m
Fine-tuned yolo11m for invoice macro-region detection (8 classes).
## Classes
`invoice_metadata`, `vendor_block`, `customer_block`, `table_block`, `line_item`, `summary_block`, `payment_block`, `Column`
## Metrics (test split)
| Metric | Value |
|--------|-------|
| mAP50 | 0.8418 |
| mAP50-95 | 0.4926 |
## Usage
```python
from ultralytics import YOLO
model = YOLO("best.pt")
results = model.predict("invoice.png")
```
|