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-yolov8m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use AvoCahDoe/invoice-layout-yolov8m with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("AvoCahDoe/invoice-layout-yolov8m") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| { | |
| "models": [ | |
| { | |
| "model": "yolov8n", | |
| "run_dir": "E:\\invoice-extractor\\training_model\\runs\\comparison\\yolov8n", | |
| "best_epoch": 53, | |
| "train_box_loss": 1.19897, | |
| "train_cls_loss": 1.03001, | |
| "val_box_loss": 1.29424, | |
| "val_cls_loss": 1.00109, | |
| "precision": 0.93255, | |
| "recall": 0.76417, | |
| "map50": 0.88236, | |
| "map50_95": 0.5973, | |
| "test_precision": 0.9502026366186389, | |
| "test_recall": 0.9592350761984346, | |
| "test_map50": 0.96, | |
| "test_map50_95": 0.7167417072510822, | |
| "test_speed_ms": 0.0 | |
| }, | |
| { | |
| "model": "yolov8s", | |
| "run_dir": "E:\\invoice-extractor\\training_model\\runs\\comparison\\yolov8s", | |
| "best_epoch": 84, | |
| "train_box_loss": 1.12892, | |
| "train_cls_loss": 0.98365, | |
| "val_box_loss": 1.19847, | |
| "val_cls_loss": 0.92536, | |
| "precision": 0.89289, | |
| "recall": 0.79291, | |
| "map50": 0.85802, | |
| "map50_95": 0.58125, | |
| "test_precision": 0.9418900776026258, | |
| "test_recall": 0.8677204132580655, | |
| "test_map50": 0.900625, | |
| "test_map50_95": 0.673915781700938, | |
| "test_speed_ms": 0.0 | |
| }, | |
| { | |
| "model": "yolov8m", | |
| "run_dir": "E:\\invoice-extractor\\training_model\\runs\\comparison\\yolov8m", | |
| "best_epoch": 96, | |
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| "test_map50_95": 0.5289323468472317, | |
| "test_speed_ms": 0.0 | |
| }, | |
| { | |
| "model": "yolo11m", | |
| "run_dir": "E:\\invoice-extractor\\training_model\\runs\\comparison\\yolo11m", | |
| "best_epoch": 87, | |
| "train_box_loss": 1.5606, | |
| "train_cls_loss": 1.49963, | |
| "val_box_loss": 1.9019, | |
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| "precision": 0.85334, | |
| "recall": 0.81873, | |
| "map50": 0.88242, | |
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| "test_recall": 0.8465909090909092, | |
| "test_map50": 0.8418105158730158, | |
| "test_map50_95": 0.4925741071428572, | |
| "test_speed_ms": 0.0 | |
| }, | |
| { | |
| "model": "yolov8x", | |
| "run_dir": "E:\\invoice-extractor\\training_model\\runs\\comparison\\yolov8x", | |
| "best_epoch": 98, | |
| "train_box_loss": 1.19512, | |
| "train_cls_loss": 1.00977, | |
| "val_box_loss": 1.33924, | |
| "val_cls_loss": 1.07422, | |
| "precision": 0.90676, | |
| "recall": 0.7602, | |
| "map50": 0.87977, | |
| "map50_95": 0.59026, | |
| "test_precision": 0.9144471501244504, | |
| "test_recall": 0.8734808033471103, | |
| "test_map50": 0.8756583298524088, | |
| "test_map50_95": 0.6224458809221967, | |
| "test_speed_ms": 0.0 | |
| }, | |
| { | |
| "model": "yolo11x", | |
| "run_dir": "E:\\invoice-extractor\\training_model\\runs\\comparison\\yolo11x", | |
| "best_epoch": 85, | |
| "train_box_loss": 1.43351, | |
| "train_cls_loss": 1.25472, | |
| "val_box_loss": 1.29899, | |
| "val_cls_loss": 0.9381, | |
| "precision": 0.88984, | |
| "recall": 0.79349, | |
| "map50": 0.89438, | |
| "map50_95": 0.61416, | |
| "test_precision": 0.9484481709774795, | |
| "test_recall": 0.8530167875685298, | |
| "test_map50": 0.87375, | |
| "test_map50_95": 0.6127151988636363, | |
| "test_speed_ms": 0.0 | |
| } | |
| ], | |
| "dataset": "E:\\invoice-extractor\\training_model\\data\\yolo_annotated\\dataset.yaml" | |
| } |