Update README.md

README.md

@@ -1,4 +1,65 @@
 ---
 license: apache-2.0
 pipeline_tag: object-detection
----
+---
+# Nepal Vehicle License Plates Detection
+
+```python
+# Example Code: You can test this model on colab
+
+# Install required libraries
+!pip install ultralytics
+!pip install PIL
+
+# Import necessary libraries
+from ultralytics import YOLO
+import matplotlib.pyplot as plt
+from PIL import Image, ImageDraw
+from google.colab import files
+import requests
+
+# Step 1: Download the model from Hugging Face
+model_url = "https://huggingface.co/krishnamishra8848/Nepal_Vehicle_License_Plates_Detection_Version2/resolve/main/best.pt"
+model_path = "best.pt"
+
+# Download the model
+print("Downloading the model...")
+response = requests.get(model_url)
+with open(model_path, 'wb') as f:
+    f.write(response.content)
+print("Model downloaded!")
+
+# Step 2: Load the model
+model = YOLO(model_path)
+
+# Step 3: Upload an image
+print("Please upload an image to test:")
+uploaded = files.upload()
+image_path = list(uploaded.keys())[0]
+
+# Step 4: Run inference
+results = model(image_path)
+
+# Step 5: Open the image and draw bounding boxes
+img = Image.open(image_path)
+draw = ImageDraw.Draw(img)
+
+for box in results[0].boxes:
+    # Extract bounding box coordinates and class information
+    x_min, y_min, x_max, y_max = box.xyxy[0].tolist()
+    label = int(box.cls)  # Class ID
+    confidence = float(box.conf)  # Confidence score
+
+    # Draw bounding box
+    draw.rectangle([x_min, y_min, x_max, y_max], outline="red", width=3)
+
+    # Add label and confidence
+    text = f"Class {label}, {confidence:.2f}"
+    draw.text((x_min, y_min - 10), text, fill="red")
+
+# Step 6: Display the image with bounding boxes
+plt.figure(figsize=(10, 10))
+plt.imshow(img)
+plt.axis('off')
+plt.show()
+