print(features.shape) The extracted features can be used for various downstream tasks such as video clustering, similarity search, classification, etc.
# Modify the model to output features num_ftrs = model.fc.in_features model.fc = nn.Identity() # Replace the classification layer with an identity function anal friend request.mp4
import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms import cv2 print(features
# Prepare a transform for preprocessing frames transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) anal friend request.mp4
# Assuming 'video_path' is your video file video_path = 'anal friend request.mp4' video_tensor = video_to_tensor(video_path)
# Reshape for model video_tensor = video_tensor.unsqueeze(0) # Add batch dimension
# Load a pre-trained model model = torchvision.models.video.i3d_resnet50(pretrained=True)