Add README.md with usage instructions and model details

README.md

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+# YOLOv8
+
+This repository contains the YOLOv8 model weights (`yolov8n.pt`) for object detection. YOLOv8 is an advanced version of the YOLO (You Only Look Once) series of real-time object detection models.
+
+## Usage
+
+To use this model for object detection, follow these steps:
+
+### Installation
+
+Install the necessary dependencies:
+```bash 
+pip install ultralytics
+pip install opencv-python
+
+import cv2
+from ultralytics import YOLO
+
+
+# code for performing object detection
+def detect_objects(model_path, image_path1, image_path2):  
+
+    # Read images  
+    input_image1 = cv2.imread(image_path1)
+    input_image2 = cv2.imread(image_path2)
+
+    # Load a model
+    model = YOLO(model_path)
+
+    # Run batched inference on a list of images
+    results = model([input_image1, input_image2])  # return a list of Results objects
+
+    # Process results list
+    for result in results:
+        boxes = result.boxes  # Boxes object for bounding box outputs
+        labels = result.cls  # labels object for detceted classes outputs
+        probs = result.probs  # Probs object for classification outputs
+        result.show()  # display to screen
+        result.save(filename="result.jpg")  # save to disk
+
+# Example usage 
+# file paths
+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},
+  journal={Hugging Face Models},
+  year={2024},
+  url={link_to_your_huggingface_model}
+}