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 metrics/summary.json with huggingface_hub
Browse files- metrics/summary.json +39 -0
metrics/summary.json
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{
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"model": "yolov8s",
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"repo_id": "AvoCahDoe/invoice-layout-yolov8s",
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"dataset": "AvoCahDoe/invoice-annotated-bbox",
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"classes": {
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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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},
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"val": {
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"epochs_trained": 100,
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"best_epoch": 94,
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"best_map50": 0.85371,
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"best_map50_95": 0.61396,
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"best_precision": 0.85874,
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"best_recall": 0.77948,
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"final_map50": 0.8478,
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"final_map50_95": 0.60857,
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"final_precision": 0.90963,
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"final_recall": 0.79076,
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"train_time_s": 1919.85
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},
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"test": {
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"test_precision": 0.9418900776026258,
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"test_recall": 0.8677204132580655,
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"test_map50": 0.900625,
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"test_map50_95": 0.673915781700938
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},
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"training": {
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"weights": "yolov8s.pt",
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"batch": 8,
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"imgsz": 1024
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}
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}
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