Update README.md

README.md

@@ -29,28 +29,56 @@ Inference
 To use this model for inference, you can load it using Hugging Face's from_pretrained functionality and pass in an image for orientation prediction.
 
 ```python
-from PIL import Image
 import torch
-from transformers import SimpleCNN
+import torch.nn as nn
+import torch.nn.functional as F
+from safetensors.torch import load_file
+from PIL import Image
+import numpy as np
+
+# Define the corrected SimpleCNN architecture
+class SimpleCNN(nn.Module):
+    def __init__(self):
+        super(SimpleCNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)  # Adjusted to 16 output channels
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)  # Adjusted to 32 output channels
+        self.conv3 = nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1)  # Adjusted to 32 output channels
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.fc1 = nn.Linear(32 * 16 * 16, 32)  # Adjusted input and output dimensions
+        self.fc2 = nn.Linear(32, 2)  # Adjusted input dimension
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = self.pool(F.relu(self.conv3(x)))
+        x = x.view(x.size(0), -1)  # Flatten
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
 
 # Load the model
-model = SimpleCNN.from_pretrained("path_to_model")
+model = SimpleCNN()
+state_dict = load_file("model.safetensors")
+model.load_state_dict(state_dict)
+model.eval()
 
 # Function to predict orientation
 def predict_orientation(image_path, model):
     img = Image.open(image_path).convert('L')  # Load image in grayscale
     img = img.resize((128, 128))               # Resize to 128x128
-    img_tensor = torch.tensor(np.array(img) / 255.0).unsqueeze(0).unsqueeze(0)
+    img_tensor = torch.tensor(np.array(img) / 255.0, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
     with torch.no_grad():
         output = model(img_tensor)
     is_rotated = torch.argmax(output, dim=1).item() == 1
     return "Rotated" if is_rotated else "Normal"
 
 # Example usage
-result = predict_orientation("example_image.jpg", model)
+result = predict_orientation("example.jpg", model)
 print(f"Image Orientation: {result}")
 ```
 
+(HT: https://huggingface.co/khasinski)
+
 ## Training
 The model was trained using standard binary cross-entropy loss and an Adam optimizer. It was trained on grayscale images resized to 128x128 pixels.