# Load a pre-trained model model = torchvision.models.resnet50(pretrained=True) model.eval() # Set to evaluation mode
# Process features as needed print(features.shape)
# Transform to apply to frames transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])
# Display or save frame if needed # ...
# Load a pre-trained model model = torchvision.models.resnet50(pretrained=True) model.eval() # Set to evaluation mode
# Process features as needed print(features.shape)
# Transform to apply to frames transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])
# Display or save frame if needed # ...
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