Create model.py

model.py

@@ -0,0 +1,20 @@
+import torch
+import torch.nn as nn
+from torchvision import models
+
+class FoodIngredientClassifier(nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+        self.backbone = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
+
+        num_features = self.backbone.fc.in_features
+        self.backbone.fc = nn.Sequential(
+            nn.Dropout(0.5),
+            nn.Linear(num_features, 512),
+            nn.ReLU(),
+            nn.Dropout(0.3),
+            nn.Linear(512, num_classes)
+        )
+
+    def forward(self, x):
+        return self.backbone(x)