Update README.md

README.md

@@ -15,4 +15,75 @@ tags:
 - self-driving
 new_version: yasirfaizahmed/license-plate-object-detection
 library_name: ultralytics
----
+---
+
+
+---
+
+
+# YOLOv8 License Plate Detection 
+This project uses the **YOLOv8**  object detection model to detect license plates. The dataset used is **Keremberke's License Plate Object Detection** , and the model is trained using the **Ultralytics YOLOv8 framework** .
+## Installation 
+
+Ensure you have the required dependencies installed:
+
+
+```bash
+pip install datasets ultralytics opencv-python numpy pandas matplotlib
+```
+
+## Dataset 
+The dataset is loaded from Hugging Face's `datasets` library:
+
+```python
+from datasets import load_dataset
+ds = load_dataset("keremberke/license-plate-object-detection", "full")
+```
+
+The dataset is split into:
+ 
+- **Training Set**
+ 
+- **Validation Set**
+ 
+- **Test Set**
+
+## Data Preprocessing 
+
+- Images are extracted from the dataset and saved locally.
+ 
+- Bounding box annotations are converted into **YOLO format**  (normalized coordinates).
+ 
+- The dataset is structured into:
+
+```kotlin
+dataset/
+├── images/
+│   ├── train/
+│   ├── val/
+├── labels/
+│   ├── train/
+│   ├── val/
+```
+
+## Model Training 
+A pre-trained **YOLOv8**  model (`yolov8n.pt`) is fine-tuned on the dataset:
+
+```python
+from ultralytics import YOLO
+
+model = YOLO('yolov8n.pt')  # Load a small YOLOv8 model
+results = model.train(data="dataset.yaml", epochs=75, imgsz=640, batch=16)
+```
+
+## Training Configuration 
+ 
+- **Epochs** : 75
+ 
+- **Image Size** : 640x640
+ 
+- **Batch Size** : 16
+
+
+
+---