features = extract_features(model, frames_tensor) print(features.shape) You might want to save these features for later use:
def prepare_model(): model = models.video.slowfast_r50_2x16x32_featurizer(pretrained=True) model.eval() # Set the model to evaluation mode return model 22241mp4
pip install torch torchvision We'll use the SlowFast model pre-trained on Kinetics-400. This example assumes you're familiar with PyTorch basics. One common approach to achieve this is by
import torch import torchvision import torchvision.transforms as transforms from torchvision import models features = extract_features(model
To prepare a deep feature for a video file like "22241.mp4", we need to extract meaningful and high-level representations from the video that can be used for tasks such as video classification, retrieval, or clustering. One common approach to achieve this is by using a pre-trained deep learning model, particularly those designed for video analysis like 3D convolutional neural networks (CNNs) or models that can handle sequential data like recurrent neural networks (RNNs) or Transformers.