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-yolov8x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use AvoCahDoe/invoice-layout-yolov8x with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("AvoCahDoe/invoice-layout-yolov8x") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Upload config/dataset.yaml with huggingface_hub
Browse files- config/dataset.yaml +14 -0
config/dataset.yaml
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path: E:/invoice-extractor/training_model/data/yolo_annotated
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train: images/train
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val: images/val
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test: images/test
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names:
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0: invoice_metadata
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1: vendor_block
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2: customer_block
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3: table_block
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4: line_item
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5: summary_block
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6: payment_block
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7: Column
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nc: 8
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