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For the classification of extremely small images, a CNN structure with fewer convolutional layers and smaller filter sizes may be appropriate. This is because small images contain fewer details, so fewer convolutional layers may be sufficient to capture the important features. Additionally, smaller filter sizes can help the model better capture localized features in the small images. However, the exact structure will depend on the specific characteristics of the images and the complexity of the classification task. It may be important to experiment with different architectures and hyperparameters to find the best performing model.