Update README.md

README.md

@@ -1,6 +1,7 @@
 ---
 base_model:
 - Ultralytics/YOLOv8
+pipeline_tag: object-detection
 ---
 
 ## Text column and row line intersection detection from Finnish census records from the 1930s
@@ -72,22 +73,22 @@ model.train(data=yaml_path, name='model_name', epochs=100, imgsz=2560, max_det=5
 ## Evaluation results
 
 Evaluation results using the validation dataset are listed below:
-|Class|Images|Class instances|Box precision|Box recall|Box mAP50|Box mAP50-95|Mask precision|Mask recall|Mask mAP50|Mask mAP50-95
--|-|-|-|-|-|-|-|-|-|-
-Text line|574|43156|0.912|0.888|0.949|0.701|0.935|0.907|0.954|0.55
+|Class|Images|Class instances|Box precision|Box recall|Box mAP50|Box mAP50-95
+-|-|-|-|-|-|-
+Intersection|25|10411|0.996|0.997|0.994|0.653|0.935|0.907|0.954|0.55
 
 More information on the performance metrics can be found [here](https://docs.ultralytics.com/guides/yolo-performance-metrics/).
 
 ## Inference
 
-If the model file `tuomiokirja_lines_05122023.pt` is downloaded to a folder `\models\tuomiokirja_lines_05122023.pt`
+If the model file `huoneistokortit_13082024.pt` is downloaded to a folder `\models\ huoneistokortit_13082024.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\tuomiokirja_lines_05122023.pt`)
+model = YOLO(`\models\ huoneistokortit_13082024.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).