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# 2. Remove the Classification Layer # We want the feature representation, not the class probabilities. # The final fully connected layer (fc) is removed. model = nn.Sequential(*list(model.children())[:-1]) The Appeal of High-Definition Imagery Website & Portfolio

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# 3. Define Image Transformations # The image must match the format the model was trained on (ImageNet standards) preprocess = transforms.Compose([ transforms.Resize(256), # Resize the short side to 256 transforms.CenterCrop(224), # Crop the center 224x224 pixels transforms.ToTensor(), # Convert image to PyTorch Tensor transforms.Normalize( # Normalize with ImageNet mean/std mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ), ])

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