modified README.md

README.md

@@ -16,17 +16,36 @@ yolo can be used for a variety of tasks and modes and accepts additional argumen
 
 To use this model for object detection, follow these steps:
 
+
 ## Python 
-Install the necessary dependencies:
+```bash 
+from ultralytics import YOLO
+
+# Load a model
+model = YOLO("yolov8n.yaml")  # build a new model from scratch
+model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+
+# Use the model
+model.train(data="coco128.yaml", epochs=3)  # train the model
+metrics = model.val()  # evaluate model performance on the validation set
+results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
+path = model.export(format="onnx")  # export the model to ONNX format
+```
+For more examples and detailed usage instructions, visit the [YOLOv8 Python Docs](https://docs.ultralytics.com/usage/python/).
+
+
+## Python 
+Install the import necessary dependencies:
 ```bash 
 pip install ultralytics
 pip install opencv-python
 
 import cv2
 from ultralytics import YOLO
-Make sure these dependencies are installed in your environment before proceeding to load and use the model.
+```
 
-# code for performing object detection
+code for performing object detection 
+```bash  
 def detect_objects(model_path, image_path1, image_path2):  
 
     # Read images  
@@ -47,22 +66,15 @@ def detect_objects(model_path, image_path1, image_path2):
         result.show()  # display to screen
         result.save(filename="result.jpg")  # save to disk
 
-# Example usage 
-# file paths
+# Example usage
 model_path = 'YOLOv8\yolov8n.pt'
 image_path1 = "path_to_your_image.jpg"
 image_path2 = "path_to_your_image.jpg"
 detect_objects(model_path, image_path1, image_path2)
-
+``` 
  
 
 
-Model Details
-Model Name: YOLOv8n
-Framework: PyTorch
-Architecture: YOLOv8
-
-
 @article{YOLOv8,
   title={YOLOv8: Improved Object Detection with Enhanced Performance},
   author={Muhammad Shahin},