# Disable gradient computation since we're only doing inference with torch.no_grad(): features = model(input_data)
# Remove the last layer to use as a feature extractor num_ftrs = model.fc.in_features model.fc = torch.nn.Linear(num_ftrs, 128) # Adjust the output dimension as needed
# Load a pre-trained model model = torchvision.models.resnet50(pretrained=True)
# Disable gradient computation since we're only doing inference with torch.no_grad(): features = model(input_data)
# Remove the last layer to use as a feature extractor num_ftrs = model.fc.in_features model.fc = torch.nn.Linear(num_ftrs, 128) # Adjust the output dimension as needed
# Load a pre-trained model model = torchvision.models.resnet50(pretrained=True)