Create README.md

README.md

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