Update README.md

README.md

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-license: apache-2.0
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+# YOLO Model Training and Inference
+
+This README provides instructions for using the YOLO (You Only Look Once) model for object detection tasks using the Ultralytics library. The guide covers both training and inference processes.
+
+## Installation
+
+First, install the required library:
+
+```
+pip install ultralytics
+```
+
+## Usage
+
+### Importing the YOLO model
+
+```python
+from ultralytics import YOLO
+```
+
+### Loading a pre-trained model
+
+```python
+# Load a pre-trained model
+model = YOLO('yolov8n.pt')
+```
+
+Replace `'yolov8n.pt'` with the path to your desired model weights.
+
+### Training the model
+
+To train the model on your custom dataset:
+
+```python
+# Train the model
+results = model.train(
+    data='path/to/your/data.yaml',
+    epochs=10,
+    imgsz=640,
+    device='mps'
+)
+```
+
+Parameters:
+- `data`: Path to your dataset's YAML file
+- `epochs`: Number of training epochs
+- `imgsz`: Input image size
+- `device`: Specify the device for training ('cuda' for NVIDIA GPU, 'mps' for Apple Silicon, 'cpu' for CPU)
+
+### Performing Inference
+
+To perform inference on an image or video:
+
+```python
+# Perform inference
+results = model('path/to/your/image.jpg')
+
+# Display results
+results.show()
+```
+
+## Additional Notes
+
+- Ensure your `data.yaml` file is correctly formatted and points to your training and validation datasets.
+- Adjust the `epochs` and `imgsz` parameters based on your specific requirements and dataset characteristics.
+- For multi-GPU training, you can use the `device` parameter to specify multiple GPUs, e.g., `device=[0,1]` for using GPUs 0 and 1.
+
+For more detailed information and advanced usage, please refer to the [Ultralytics documentation](https://docs.ultralytics.com/).