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model files - updated weights

best_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:60b37dbdd4cdd3898f62e8c966c8e4cdaaab7977a19568c07a6b0ee21fd1cebe
+size 9146552

classification_report.txt

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+Final Model Accuracy: 96.25%
+
+Classification Report:
+              precision    recall  f1-score   support
+
+      indoor       0.96      0.96      0.96       200
+     outdoor       0.96      0.96      0.96       200
+
+    accuracy                           0.96       400
+   macro avg       0.96      0.96      0.96       400
+weighted avg       0.96      0.96      0.96       400

sample_inference_code.py

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+
+import torch
+from torchvision import transforms, models
+import torch.nn as nn
+from PIL import Image
+
+def classify_image(image_path, model_path="indoor_outdoor_classifier_from_scratch/best_model.pth"):
+    # Check if MPS (Apple Silicon GPU) is available
+    if torch.backends.mps.is_available():
+        device = torch.device("mps")
+    else:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    # Define the model architecture
+    def get_model():
+        model = models.mobilenet_v2(weights=None)
+        num_ftrs = model.classifier[1].in_features
+        model.classifier[1] = nn.Linear(num_ftrs, 2)
+        return model
+    
+    # Load the model
+    model = get_model()
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model = model.to(device)
+    model.eval()
+    
+    # Prepare image transformation
+    transform = transforms.Compose([
+        transforms.Resize((160, 160)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    
+    # Load and preprocess the image
+    img = Image.open(image_path).convert('RGB')
+    img_tensor = transform(img).unsqueeze(0).to(device)
+    
+    # Make prediction
+    with torch.no_grad():
+        output = model(img_tensor)
+        _, predicted = torch.max(output, 1)
+        probabilities = torch.nn.functional.softmax(output, dim=1)
+    
+    # Get class label and probability
+    class_names = ['indoor', 'outdoor']
+    predicted_class = class_names[predicted.item()]
+    probability = probabilities[0][predicted.item()].item()
+    
+    return {
+        'class': predicted_class,
+        'probability': probability * 100,
+        'all_probabilities': {
+            class_names[i]: probabilities[0][i].item() * 100 for i in range(len(class_names))
+        }
+    }
+
+# Example usage
+if __name__ == "__main__":
+    import sys
+    
+    if len(sys.argv) > 1:
+        image_path = sys.argv[1]
+    else:
+        image_path = input("Enter the path to the image: ")
+    
+    result = classify_image(image_path)
+    print(f"Class: {result['class']} ({result['probability']:.2f}%)")