Add model.py with FineGrainedClassifier definition

model.py

@@ -0,0 +1,35 @@
+import torch
+import torch.nn as nn
+from torchvision import models
+from transformers import AutoModel
+
+class FineGrainedClassifier(nn.Module):
+    def __init__(self, num_classes=434, text_dim=768):
+        super(FineGrainedClassifier, self).__init__()
+
+        # Image encoder (ResNet50)
+        self.resnet = models.resnet50(pretrained=False)
+        self.resnet.fc = nn.Identity()  # Remove final classification layer
+
+        # Text encoder (Jina embeddings)
+        self.text_encoder = AutoModel.from_pretrained("jinaai/jina-embeddings-v2-base-en", trust_remote_code=True)
+
+        # Fully connected layers for text embeddings
+        self.text_fc = nn.Linear(text_dim, 1024)
+
+        # Fusion and classification
+        self.fusion_fc = nn.Linear(2048 + 1024, num_classes)
+
+    def forward(self, images, text_embeddings):
+        # Extract image features
+        image_features = self.resnet(images)
+
+        # Process text embeddings
+        text_features = self.text_fc(text_embeddings)
+
+        # Concatenate image and text features
+        combined = torch.cat((image_features, text_features), dim=1)
+
+        # Classify
+        output = self.fusion_fc(combined)
+        return output