# 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.
import torch import torchvision import torchvision.transforms as transforms from torchvision.models import resnet50 from PIL import Image import os import numpy as np slike_SLOVENKE_socialMEDIArip_vol.1.rar
# Images directory images_dir = 'path/to/extracted/images' # Save or use the features np
features = [] for filename in os.listdir(images_dir): img_path = os.path.join(images_dir, filename) image = Image.open(img_path) image = transform(image) image = image.unsqueeze(0).to(device) feature = model(image) feature = feature.detach().cpu().numpy().squeeze() features.append(feature) slike_SLOVENKE_socialMEDIArip_vol.1.rar
# Load pre-trained ResNet50 and remove the last layer model = resnet50(pretrained=True) model.fc = torch.nn.Identity()
# Move model to GPU if available device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model.to(device) model.eval()
# Define transformations for images transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])