Object Detection
ultralytics
English
German
yolo
yolov8
invoice
document-layout
document-understanding
ocr-prep
invoice-extraction
Eval Results (legacy)
Instructions to use AvoCahDoe/invoice-layout-yolov8s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use AvoCahDoe/invoice-layout-yolov8s with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("AvoCahDoe/invoice-layout-yolov8s") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: apache-2.0
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tags:
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- object-detection
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- yolo
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- invoice
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- document-layout
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library_name: ultralytics
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---
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# Invoice Layout — yolov8s
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Fine-tuned yolov8s for invoice macro-region detection (8 classes).
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## Classes
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`invoice_metadata`, `vendor_block`, `customer_block`, `table_block`, `line_item`, `summary_block`, `payment_block`, `Column`
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## Metrics (test split)
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| Metric | Value |
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|--------|-------|
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| mAP50 | 0.0000 |
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| mAP50-95 | 0.0000 |
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## Usage
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```python
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from ultralytics import YOLO
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model = YOLO("best.pt")
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results = model.predict("invoice.png")
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```
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