There are many other models that can be implemented for image classification besides VGG. Here are a few examples:

ResNet (Residual Network): This model uses residual connections to help with training deep neural networks.

Inception: This model uses multiple convolutional filters with different sizes in parallel to improve accuracy.

MobileNet: This model is designed to be lightweight and efficient, making it well-suited for mobile devices.

DenseNet: This model connects each layer to every other layer in a feed-forward fashion, leading to more efficient use of parameters and improved accuracy.

AlexNet: This model was one of the first deep learning models to achieve breakthrough results on the ImageNet dataset and uses a series of convolutional and pooling layers to extract features from images.

These are just a few examples, and there are many other models that can be used for image classification depending on the specific needs and constraints of the task at hand.
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