# Prepare a transform transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])
# Do something with features...
import torch import torchvision import torchvision.transforms as transforms import cv2 HMN-032-MR.mp4
If you're working in a field like computer vision or video analysis, "deep features" might refer to features extracted from deep learning models, such as convolutional neural networks (CNNs), that are used for various tasks including object detection, classification, or video understanding. # Prepare a transform transform = transforms
# Extract features features = [] with torch.no_grad(): for frame in frames: frame = transform(frame) frame = frame.unsqueeze(0) # Add batch dimension output = model(frame) features.append(output.detach().cpu().numpy()) such as convolutional neural networks (CNNs)
# Define a pre-trained model model = torchvision.models.resnet50(pretrained=True) model.eval()
For example, if you're using PyTorch and want to extract features from a video using a pre-trained model, a basic approach might look something like this: