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--- |
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base_model: |
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- Ultralytics/YOLOv8 |
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pipeline_tag: object-detection |
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license: agpl-3.0 |
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--- |
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## Text column and row line intersection detection from Finnish death records from the 1930s |
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The model is trained to find the intersection points of table column and cell lines from digitized death record documents |
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from the 1930s. The model has been trained using yolov8x by Ultralytics as the base model. |
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## Intended uses & limitations |
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<img src='death_record_intersection_example.jpg' width='500'> |
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The model has been trained to detect intersection points from specific kinds of tables, and probably generalizes badly to other, |
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very different table types. |
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## Training data |
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Training dataset consisted of 244 digitized and annotated documents containing tables, while validation |
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dataset contained 31 annotated document images. |
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## Training procedure |
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This model was trained using 2 NVIDIA RTX A6000 GPUs with the following hyperparameters: |
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- image size: 2560 |
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- train batch size: 4 |
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- epochs: 100 |
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- patience: 10 epochs |
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- optimizer: SGD |
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- workers: 4 |
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- learning rate (lr0): 0.01 |
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Default settings were used for other training hyperparameters (find more information [here](https://docs.ultralytics.com/modes/train/#train-settings)). |
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Model training was performed using the following code: |
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```python |
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from ultralytics import YOLO |
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# Use pretrained Yolo segmentation model |
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model = YOLO('yolov8x.pt') |
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# Path to .yaml file where data location and object classes are defined |
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yaml_path = 'intersections.yaml' |
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# Start model training with the defined parameters |
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model.train(data=yaml_path, name='model_name', epochs=100, imgsz=640, workers=4, optimizer='SGD', |
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lr0=0.01, seed=42, val=True, cos_lr=False, patience=10, batch=32, device='0,1') |
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``` |
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## Evaluation results |
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Evaluation results using the validation dataset are listed below: |
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|Class|Images|Class instances|Box precision|Box recall|Box mAP50|Box mAP50-95 |
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|:----|:----|:----|:----|:----|:----|:----| |
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Intersection|31|6769|0.927|0.946|0.939|0.426 |
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More information on the performance metrics can be found [here](https://docs.ultralytics.com/guides/yolo-performance-metrics/). |
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## Inference |
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If the model file `yolo_kuolleet_09022024.pt` is downloaded to a folder `\models\yolo_kuolleet_09022024.pt` |
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and the input image path is `\data\image.jpg`, inference can be perfomed using the following code: |
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```python |
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from ultralytics import YOLO |
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# Initialize model |
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model = YOLO(`\models\yolo_kuolleet_09022024.pt`) |
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prediction_results = model.predict(source=`\data\image.jpg', save=True) |
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``` |
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More information for available inference arguments can be found [here](https://docs.ultralytics.com/modes/predict/#inference-arguments). |
