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