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Using a framework like PyTorch or TensorFlow, you can extract the feature vector as follows: torchvision torchvision transforms # 1. Load a pre-trained model = models.resnet50(pretrained= # 2. Remove the last classification layer (FC layer) feature_extractor = torch.nn.Sequential(*(list(model.children())[:- ])) feature_extractor.eval() # 3. Pass a pre-processed frame through the model # input_tensor shape: [Batch, 3, 224, 224] torch.no_grad(): feature_vector = feature_extractor(input_tensor) # Result is a 2048-dimensional deep feature Use code with caution. Copied to clipboard 4. Pooling and Aggregation Often accompanied by generic, upbeat stock music or

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