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 config/train_comparison.yaml with huggingface_hub
Browse files- config/train_comparison.yaml +66 -0
config/train_comparison.yaml
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# Multi-model YOLO comparison training (optimized for RTX 4070 Laptop)
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epochs: 100
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patience: 25
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device: 0
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workers: 0
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cache: disk
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project: runs/comparison
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# Optimizer
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optimizer: AdamW
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cos_lr: true
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lr0: 0.01
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lrf: 0.01
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momentum: 0.937
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weight_decay: 0.0005
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warmup_epochs: 3.0
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# Loss weights
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box: 7.5
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cls: 0.5
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dfl: 1.5
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# Geometric augmentation
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mosaic: 1.0
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mixup: 0.15
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copy_paste: 0.1
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scale: 0.5
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perspective: 0.0005
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degrees: 3.0
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translate: 0.1
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fliplr: 0.0
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flipud: 0.0
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close_mosaic: 10
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# Visual augmentation
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hsv_h: 0.015
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hsv_s: 0.7
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hsv_v: 0.4
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erasing: 0.4
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# Per-model overrides — train smallest first (n → s → m → 11m → v8x → 11x)
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models:
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yolov8n:
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weights: yolov8n.pt
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batch: 8
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imgsz: 1024
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yolov8s:
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weights: yolov8s.pt
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batch: 8
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imgsz: 1024
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yolov8m:
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weights: yolov8m.pt
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batch: 6
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imgsz: 1024
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yolo11m:
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weights: yolo11m.pt
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batch: 6
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imgsz: 1024
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yolov8x:
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weights: yolov8x.pt
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batch: 2
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imgsz: 1024
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yolo11x:
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weights: yolo11x.pt
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batch: 2
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imgsz: 1024
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