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)
# Now 'features' is a list of feature vectors, you can convert it to a numpy array features = np.array(features) slike_SLOVENKE_socialMEDIArip_vol.1.rar
# Load pre-trained ResNet50 and remove the last layer model = resnet50(pretrained=True) model.fc = torch.nn.Identity() features = [] for filename in os
# Images directory images_dir = 'path/to/extracted/images' slike_SLOVENKE_socialMEDIArip_vol.1.rar
# 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.