Update README.md

README.md

@@ -59,7 +59,7 @@ class CNNV0(nn.Module):
         x = self.conv_block_2(x)
         x = self.classifier(x)
         return x
-
+```
 ## Requirements
 
 - **Python** 3.7 or higher
@@ -73,28 +73,29 @@ class CNNV0(nn.Module):
    git clone <repository-url>
    cd <repository-folder>
    pip install torch torchvision
-
+   ```
+   
 2. Load and use the model in your Python script:
    ```python
    import torch
-  from torchvision import transforms
-  from PIL import Image
-  
-  # Load the model
-  model = torch.load('model_0.pth')
-  model.eval()  # Set to evaluation mode
-  
-  # Load and preprocess the image
-  transform = transforms.Compose([
-      transforms.Resize((224, 224)),
-      transforms.ToTensor(),
-  ])
-  img = Image.open('path_to_image.jpg')
-  img = transform(img).view(1, 3, 224, 224)  # Reshape to (1, 3, 224, 224) for batch processing
-  
-  # Predict
-  with torch.no_grad():
-      output = model(img)
-      _, predicted = torch.max(output, 1)
-      print("Predicted Aircraft Type:", predicted.item())
+   from torchvision import transforms
+   from PIL import Image
+
+   # Load the model
+   model = torch.load('model_0.pth')
+   model.eval()  # Set to evaluation mode
+
+   # Load and preprocess the image
+   transform = transforms.Compose([
+       transforms.Resize((224, 224)),
+       transforms.ToTensor(),
+   ])
+   img = Image.open('path_to_image.jpg')
+   img = transform(img).view(1, 3, 224, 224)  # Reshape to (1, 3, 224, 224) for batch processing
 
+   # Predict
+   with torch.no_grad():
+       output = model(img)
+       _, predicted = torch.max(output, 1)
+       print("Predicted Aircraft Type:", predicted.item())
+   ```