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# Example input input_data = torch.randn(1, 3, 224, 224) # 1 image, 3 channels, 224x224 pixels
# 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)
import torch import torchvision import torchvision.transforms as transforms
# Disable gradient computation since we're only doing inference with torch.no_grad(): features = model(input_data)
# Example input input_data = torch.randn(1, 3, 224, 224) # 1 image, 3 channels, 224x224 pixels
# 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 fc2ppv18559752part1rar upd
# Load a pre-trained model model = torchvision.models.resnet50(pretrained=True) # Example input input_data = torch
import torch import torchvision import torchvision.transforms as transforms # Example input input_data = torch.randn(1
# Disable gradient computation since we're only doing inference with torch.no_grad(): features = model(input_data)
