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CC_net (1).pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b61ad39bb8f2872cff371265b3ad4ecbf9c5a201d64225f92d6bcc937d9e112b
+size 95648689

ResNet_for_CC.py

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+import torch
+import torch.nn as nn
+import torchvision.models as models
+
+class ResClassifier(nn.Module):
+    """
+    A classifier with two fully connected layers followed by a final linear layer.
+    Uses BatchNorm, ReLU activations, and Dropout for better generalization.
+    """
+    def __init__(self, num_classes=14):
+        super(ResClassifier, self).__init__()
+        
+        # First fully connected layer: reduces 128D features to 64D
+        self.fc1 = nn.Sequential(
+            nn.Linear(128, 64),
+            nn.BatchNorm1d(64, affine=True),
+            nn.ReLU(inplace=True),
+            nn.Dropout()
+        )
+        
+        # Second fully connected layer: retains 64D features
+        self.fc2 = nn.Sequential(
+            nn.Linear(64, 64),
+            nn.BatchNorm1d(64, affine=True),
+            nn.ReLU(inplace=True),
+            nn.Dropout()
+        )
+        
+        # Final classification layer mapping 64D features to class logits
+        self.fc3 = nn.Linear(64, num_classes)
+
+    def forward(self, x):
+        """
+        Forward pass through the classifier.
+        Returns class logits after two hidden layers.
+        """
+        x = self.fc1(x)  # First FC layer
+        x = self.fc2(x)  # Second FC layer
+        output = self.fc3(x)  # Final classification layer
+        return output
+
+
+class CC_model(nn.Module):
+    """
+    Clothing Classification Model based on ResNet50.
+    Extracts deep features and uses two independent classifiers for predictions.
+    """
+    def __init__(self, num_classes1=14, num_classes2=None):
+        super(CC_model, self).__init__()
+        
+        # If num_classes2 is not specified, default to num_classes1
+        num_classes2 = num_classes2 if num_classes2 else num_classes1
+        assert num_classes1 == num_classes2  # Ensure both classifiers predict the same categories
+        
+        self.num_classes = num_classes1
+        
+        # Load a pretrained ResNet-50 model as the feature extractor
+        self.model_resnet = models.resnet50(weights='ResNet50_Weights.DEFAULT')
+        
+        # Remove ResNet's original classification layer to use as a feature extractor
+        num_ftrs = self.model_resnet.fc.in_features
+        self.model_resnet.fc = nn.Identity()  # Identity layer keeps feature dimensions
+
+        # Additional transformation layer reducing feature size to 128D
+        self.dr = nn.Linear(num_ftrs, 128)
+
+        # Two independent classifiers
+        self.fc1 = ResClassifier(num_classes1)
+        self.fc2 = ResClassifier(num_classes1)
+
+    def forward(self, x, detach_feature=False):
+        """
+        Forward pass through the model.
+        Extracts deep features from ResNet and processes them through classifiers.
+        """
+        with torch.no_grad():
+            # Extract deep features using ResNet-50 (without its original classification head)
+            feature = self.model_resnet(x)
+
+        # Generate transformed features (128D) using the custom linear layer
+        dr_feature = self.dr(feature)
+
+        if detach_feature:
+            dr_feature = dr_feature.detach()  # Detach feature for non-trainable forward pass
+
+        # Pass features through two independent classifiers
+        out1 = self.fc1(dr_feature)
+        out2 = self.fc2(dr_feature)
+
+        # Compute the mean prediction from both classifiers
+        output_mean = (out1 + out2) / 2
+
+        return dr_feature, output_mean  # Returning feature embeddings and final prediction

requirements.txt

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+clip==0.2.0
+numpy==1.23.4
+openai_clip==1.0.1
+Pillow==9.4.0
+torch==2.6.0
+torchvision==0.21.0
+tqdm==4.64.1