Tranisa Video

We'll use a pre-trained CNN (like ResNet) for spatial features and an LSTM for temporal features.

import torchvision.models as models
import torch.nn as nn
class VideoFeatureExtractor(nn.Module):
    def __init__(self, num_classes=1000):
        super(VideoFeatureExtractor, self).__init__()
        self.cnn = models.resnet50(pretrained=True)
        self.lstm = nn.LSTM(input_size=2048, hidden_size=512, num_layers=1, batch_first=True)
def forward(self, x):
        # Assuming x is a tensor of shape (T, C, H, W)
        batch_size = x.size(0)
        T = x.size(1)
        cnn_out = []
        for t in range(T):
            out = self.cnn(x[:, t, :, :, :])
            cnn_out.append(out)
        cnn_out = torch.stack(cnn_out, dim=1).view(batch_size, T, -1)
        lstm_out, _ = self.lstm(cnn_out)
        return lstm_out[:, -1, :]

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What does the future hold? Based on current trends, we can expect: We'll use a pre-trained CNN (like ResNet) for

As the demand for authentic, non-algorithmic content grows, Tranisa Video is well-positioned to become a blueprint for the next generation of digital auteurs.

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