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-yolov8n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use AvoCahDoe/invoice-layout-yolov8n with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("AvoCahDoe/invoice-layout-yolov8n") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 913 Bytes
ca4882b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | {
"model": "yolov8n",
"repo_id": "AvoCahDoe/invoice-layout-yolov8n",
"dataset": "AvoCahDoe/invoice-annotated-bbox",
"classes": {
"0": "invoice_metadata",
"1": "vendor_block",
"2": "customer_block",
"3": "table_block",
"4": "line_item",
"5": "summary_block",
"6": "payment_block",
"7": "Column"
},
"val": {
"epochs_trained": 87,
"best_epoch": 62,
"best_map50": 0.87868,
"best_map50_95": 0.62526,
"best_precision": 0.88531,
"best_recall": 0.78492,
"final_map50": 0.82816,
"final_map50_95": 0.60501,
"final_precision": 0.84392,
"final_recall": 0.77346,
"train_time_s": 2457.74
},
"test": {
"test_precision": 0.9502026366186389,
"test_recall": 0.9592350761984346,
"test_map50": 0.96,
"test_map50_95": 0.7167417072510822
},
"training": {
"weights": "yolov8n.pt",
"batch": 8,
"imgsz": 1024
}
} |