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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 census 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 census 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='census_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 218 digitized and annotated documents containing tables, while validation |
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dataset contained 25 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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- initial learning rate (lr0): 0.00098 |
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- final learning rate (lrf): 0.01285 |
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- maximum number of detections per image (max_det): 500 |
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- train batch size: 2 |
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- epochs: 100 |
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- patience: 30 epochs |
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- warmup_epochs: 3.91327 |
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- optimizer: AdamW |
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- workers: 4 |
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- momentum: 0.90725 |
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- warmup_momentum: 0.72051 |
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- weight_decay: 0.00061 |
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- box loss weight (box): 9.34214 |
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- classification loss weight (cls): 0.34133 |
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- distribution focal loss weight (dfl): 1.83008 |
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- hue augment (hsv_h): 0.01126 |
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- saturation augment (hsv_s): 0.84221 |
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- brightness augment (hsv_v): 0.435 |
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- translation augment (translate): 0.11692 |
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- scale augment (scale): 0.45713 |
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- flip augment (fliplr): 0.38368 |
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- mosaic augment (mosaic): 0.77082 |
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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=2560, max_det=500, workers=4, optimizer='AdamW', |
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lr0=0.00098, lrf=0.01285, momentum=0.90725, weight_decay=0.00061, warmup_epochs=3.91327, warmup_momentum=0.72051, |
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box=9.34214, cls=0.34133, dfl=1.83008, hsv_h=0.01126, hsv_s=0.84221, hsv_v=0.435, translate=0.11692, |
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scale=0.45713, fliplr=0.38368, mosaic=0.77082, seed=42, val=True, patience=30, batch=2, 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|25|10411|0.996|0.997|0.994|0.653 |
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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 `huoneistokortit_13082024.pt` is downloaded to a folder `\models\ huoneistokortit_13082024.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\ huoneistokortit_13082024.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). |
