AvoCahDoe/invoice-layout-yolov8n

Invoice Layout Detection — YOLOv8n

Fine-tuned YOLOv8n for 8-class invoice document layout region detection (metadata, vendor/customer blocks, table, line items, summary, payment, column headers).

Part of the Hub collection [Invoice Layout Extraction](https://huggingface.co/collections/AvoCahDoe/invoice-layout-extraction-6a39cd2a061a508c8049d825).

Classes

Metrics

Test split (held-out, 7 images)

Validation (best epoch 62)

Training time: ~41.0 min on RTX 4070 Laptop GPU.

Training configuration

Training results

Learning curves

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Precision–Recall

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Confusion matrices

Validation predictions

Training batches (augmented)

Label distribution

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Usage

Ultralytics (recommended)

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

weights = hf_hub_download("AvoCahDoe/invoice-layout-yolov8n", "weights/best.pt")
model = YOLO(weights)
results = model.predict("invoice_page.png", imgsz=1024, conf=0.25)
results[0].show()

Load from Hub by repo id

from ultralytics import YOLO

model = YOLO("hf://AvoCahDoe/invoice-layout-yolov8n/weights/best.pt")
results = model.predict("invoice_page.png")

Training reproduction

python scripts/train_comparison.py --models yolov8n

Repository layout

weights/best.pt          # Best checkpoint (use this)
weights/last.pt          # Last epoch checkpoint
config/                  # Training configuration
metrics/                 # Per-epoch and summary metrics
assets/                  # Plots and visualizations

Model comparison (test split)

All six architectures fine-tuned on the same invoice layout dataset.

License

Apache 2.0