import torch import torchvision import torchvision.transforms as transforms from torchvision.models import resnet50 from PIL import Image import os import numpy as np
# Images directory images_dir = 'path/to/extracted/images' slike_SLOVENKE_socialMEDIArip_vol.1.rar
# Load pre-trained ResNet50 and remove the last layer model = resnet50(pretrained=True) model.fc = torch.nn.Identity() import torch import torchvision import torchvision
# Save or use the features np.save('image_features.npy', features) Please adjust paths and details according to your specific situation. This example assumes you have PyTorch installed and have extracted the images from the .rar file. slike_SLOVENKE_socialMEDIArip_vol.1.rar
# Now 'features' is a list of feature vectors, you can convert it to a numpy array features = np.array(features)
# Move model to GPU if available device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model.to(device) model.eval()