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
File size: 3,631 Bytes
858b7b2 | 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 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | {
"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,
"train_box_loss": 1.35495,
"train_cls_loss": 1.13978,
"val_box_loss": 1.50381,
"val_cls_loss": 1.16088,
"precision": 0.86048,
"recall": 0.79786,
"map50": 0.86459,
"map50_95": 0.54281,
"test_precision": 0.9077380952380952,
"test_recall": 0.8579545454545455,
"test_map50": 0.8388392857142857,
"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,
"val_cls_loss": 1.35903,
"precision": 0.85334,
"recall": 0.81873,
"map50": 0.88242,
"map50_95": 0.41246,
"test_precision": 0.9032503333433675,
"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"
} |