Update README.md

README.md

@@ -38,7 +38,7 @@ Default settings were used for other training hyperparameters (find more informa
 
 Model training was performed using the following code:
 
-```
+```python
 from ultralytics import YOLO
 
 # Use pretrained Yolo segmentation model
@@ -55,7 +55,7 @@ model.train(data=yaml_path, name='model_name', epochs=100, imgsz=640, workers=4,
 
 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
 
 More information on the performance metrics can be found [here](https://docs.ultralytics.com/guides/yolo-performance-metrics/).
@@ -65,11 +65,11 @@ More information on the performance metrics can be found [here](https://docs.ult
 If the model file `tuomiokirja_lines_05122023.pt` is downloaded to a folder `\models\tuomiokirja_lines_05122023.pt`
 and the input image path is `\data\image.jpg', inference can be perfomed using the following code:
 
-```
+```python
 from ultralytics import YOLO
 
 # Initialize model
-model = YOLO(`\models\tuomiokirja_lines_05122023.pt`)
-prediction_results = model.predict(source=`\data\image.jpg', save=True)
+model = YOLO('\models\tuomiokirja_lines_05122023.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).