Update README.md

README.md

@@ -54,7 +54,7 @@ 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
@@ -74,7 +74,7 @@ model.train(data=yaml_path, name='model_name', epochs=100, imgsz=2560, max_det=5
 
 Evaluation results using the validation dataset are listed below:
 |Class|Images|Class instances|Box precision|Box recall|Box mAP50|Box mAP50-95
--|-|-|-|-|-|-
+|:----|:----|:----|:----|:----|:----|:----|
 Intersection|25|10411|0.996|0.997|0.994|0.653
 
 More information on the performance metrics can be found [here](https://docs.ultralytics.com/guides/yolo-performance-metrics/).
@@ -82,13 +82,13 @@ More information on the performance metrics can be found [here](https://docs.ult
 ## Inference
 
 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:
+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\ huoneistokortit_13082024.pt`)
-prediction_results = model.predict(source=`\data\image.jpg', save=True)
+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).