# 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.
 
## Documentation
See below for a quickstart installation and usage example, and see the YOLOv8 Docs for full documentation on training, validation, prediction and deployment.
 
## CLI
YOLOv8 may be used directly in the Command Line Interface (CLI) with a yolo command:
```bash  
yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
```  
## Python 
To use this model for object detection, follow these steps: 
```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/models/yolov8/#usage-examples).


## Example usage 
code for performing object detection 
```bash 
# Install the import necessary dependencies:
pip install ultralytics
pip install opencv-python

import cv2
from ultralytics import YOLO


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
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)
``` 
 

```bash  
@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}
}
```