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
| license: apache-2.0 | |
| tags: | |
| - object-detection | |
| - yolo | |
| - invoice | |
| - document-layout | |
| library_name: ultralytics | |
| # Invoice Layout — yolov8s | |
| Fine-tuned yolov8s 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 | 0.0000 | | |
| | mAP50-95 | 0.0000 | | |
| ## Usage | |
| ```python | |
| from ultralytics import YOLO | |
| model = YOLO("best.pt") | |
| results = model.predict("invoice.png") | |
| ``` | |