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Our approach uses a CNN-based architecture, which consists of several convolutional and pooling layers, followed by fully connected layers. The model is trained on a large dataset of labeled images, which includes both NSFW and SFW images. We use a transfer learning approach, where the model is pre-trained on a large image classification dataset and fine-tuned on our dataset.

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Several approaches have been proposed for NSFW image classification, including traditional computer vision techniques and machine learning-based methods. However, these approaches have limitations, such as relying on hand-engineered features or requiring a large amount of labeled data. Deep learning-based approaches, particularly CNNs, have shown promising results in image classification tasks, including NSFW image classification. Our approach uses a CNN-based architecture, which consists

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